From a480b8f16ca5b12c5e23cf5f2e63dcd8aaf0696e Mon Sep 17 00:00:00 2001 From: Geliebte <1297754537@qq.com> Date: Sun, 23 Aug 2026 13:07:58 +0800 Subject: [PATCH] update --- config.json | 9 +- package.json | 1 + src/voice/cosyvoice-opt-test.py | 66 + .../asset/cross_lingual_prompt.wav | Bin 0 -> 606404 bytes src/voice/cosyvoice-src/asset/dingding.png | Bin 0 -> 122824 bytes .../cosyvoice-src/asset/zero_shot_prompt.wav | Bin 0 -> 334138 bytes src/voice/cosyvoice-src/cosyvoice/__init__.py | 0 .../cosyvoice/bin/average_model.py | 93 + .../cosyvoice-src/cosyvoice/bin/export_jit.py | 99 + .../cosyvoice/bin/export_onnx.py | 114 + .../cosyvoice-src/cosyvoice/bin/train.py | 195 + .../cosyvoice-src/cosyvoice/cli/__init__.py | 0 .../cosyvoice-src/cosyvoice/cli/cosyvoice.py | 238 + .../cosyvoice-src/cosyvoice/cli/frontend.py | 224 + .../cosyvoice-src/cosyvoice/cli/model.py | 450 + .../cosyvoice/dataset/__init__.py | 0 .../cosyvoice/dataset/dataset.py | 155 + .../cosyvoice/dataset/processor.py | 431 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.../hifi_dataset_piper_phonemizer.yaml | 14 + .../configs/experiment/ljspeech.yaml | 14 + .../experiment/ljspeech_from_durations.yaml | 19 + .../experiment/ljspeech_min_memory.yaml | 18 + .../configs/experiment/multispeaker.yaml | 14 + .../Matcha-TTS/configs/extras/default.yaml | 8 + .../configs/hparams_search/mnist_optuna.yaml | 52 + .../Matcha-TTS/configs/hydra/default.yaml | 19 + .../Matcha-TTS/configs/local/.gitkeep | 0 .../Matcha-TTS/configs/logger/aim.yaml | 28 + .../Matcha-TTS/configs/logger/comet.yaml | 12 + .../Matcha-TTS/configs/logger/csv.yaml | 7 + .../configs/logger/many_loggers.yaml | 9 + .../Matcha-TTS/configs/logger/mlflow.yaml | 12 + .../Matcha-TTS/configs/logger/neptune.yaml | 9 + .../configs/logger/tensorboard.yaml | 10 + .../Matcha-TTS/configs/logger/wandb.yaml | 16 + .../Matcha-TTS/configs/model/cfm/default.yaml | 3 + .../configs/model/decoder/default.yaml | 7 + .../configs/model/encoder/default.yaml | 18 + .../Matcha-TTS/configs/model/matcha.yaml | 16 + .../configs/model/optimizer/adam.yaml | 4 + .../Matcha-TTS/configs/paths/default.yaml | 18 + .../third_party/Matcha-TTS/configs/train.yaml | 51 + .../Matcha-TTS/configs/trainer/cpu.yaml | 5 + .../Matcha-TTS/configs/trainer/ddp.yaml | 9 + .../Matcha-TTS/configs/trainer/ddp_sim.yaml | 7 + .../Matcha-TTS/configs/trainer/default.yaml | 20 + .../Matcha-TTS/configs/trainer/gpu.yaml | 5 + .../Matcha-TTS/configs/trainer/mps.yaml | 5 + .../third_party/Matcha-TTS/matcha/VERSION | 1 + .../third_party/Matcha-TTS/matcha/__init__.py | 0 .../third_party/Matcha-TTS/matcha/app.py | 357 + .../third_party/Matcha-TTS/matcha/cli.py | 419 + .../Matcha-TTS/matcha/hifigan/LICENSE | 21 + .../Matcha-TTS/matcha/hifigan/README.md | 101 + .../Matcha-TTS/matcha/hifigan/__init__.py | 0 .../Matcha-TTS/matcha/hifigan/config.py | 28 + .../Matcha-TTS/matcha/hifigan/denoiser.py | 68 + .../Matcha-TTS/matcha/hifigan/env.py | 17 + .../Matcha-TTS/matcha/hifigan/meldataset.py | 217 + .../Matcha-TTS/matcha/hifigan/models.py | 368 + .../Matcha-TTS/matcha/hifigan/xutils.py | 60 + .../Matcha-TTS/matcha/models/__init__.py | 0 .../matcha/models/baselightningmodule.py | 210 + .../matcha/models/components/__init__.py | 0 .../matcha/models/components/decoder.py | 443 + .../matcha/models/components/flow_matching.py | 132 + .../matcha/models/components/text_encoder.py | 410 + .../matcha/models/components/transformer.py | 316 + .../Matcha-TTS/matcha/models/matcha_tts.py | 245 + .../Matcha-TTS/matcha/onnx/__init__.py | 0 .../Matcha-TTS/matcha/onnx/export.py | 181 + .../Matcha-TTS/matcha/onnx/infer.py | 168 + .../Matcha-TTS/matcha/text/__init__.py | 57 + .../Matcha-TTS/matcha/text/cleaners.py | 144 + .../Matcha-TTS/matcha/text/numbers.py | 71 + .../Matcha-TTS/matcha/text/symbols.py | 17 + .../third_party/Matcha-TTS/matcha/train.py | 122 + .../Matcha-TTS/matcha/utils/__init__.py | 5 + .../Matcha-TTS/matcha/utils/audio.py | 82 + .../matcha/utils/generate_data_statistics.py | 110 + .../utils/get_durations_from_trained_model.py | 195 + .../Matcha-TTS/matcha/utils/instantiators.py | 56 + .../Matcha-TTS/matcha/utils/logging_utils.py | 53 + .../Matcha-TTS/matcha/utils/model.py | 90 + .../matcha/utils/monotonic_align/__init__.py | 22 + .../matcha/utils/monotonic_align/core.pyx | 47 + .../matcha/utils/monotonic_align/setup.py | 7 + .../Matcha-TTS/matcha/utils/pylogger.py | 21 + .../Matcha-TTS/matcha/utils/rich_utils.py | 101 + .../Matcha-TTS/matcha/utils/utils.py | 259 + .../third_party/Matcha-TTS/notebooks/.gitkeep | 0 .../third_party/Matcha-TTS/pyproject.toml | 51 + .../third_party/Matcha-TTS/requirements.txt | 44 + .../Matcha-TTS/scripts/schedule.sh | 7 + .../third_party/Matcha-TTS/setup.py | 53 + .../third_party/Matcha-TTS/synthesis.ipynb | 419 + src/voice/cosyvoice-test.py | 58 + src/voice/test-cosyvoice-provider.mjs | 19 + src/voice/tts-providers.js | 109 +- src/voice/tts_cosyvoice.py | 78 + src/voice/voxcpm-clone-test.py | 56 + src/voice/voxcpm-test.py | 63 + 待办.txt | 59 + 166 files changed, 78617 insertions(+), 5 deletions(-) create mode 100644 src/voice/cosyvoice-opt-test.py create mode 100644 src/voice/cosyvoice-src/asset/cross_lingual_prompt.wav create mode 100644 src/voice/cosyvoice-src/asset/dingding.png create mode 100644 src/voice/cosyvoice-src/asset/zero_shot_prompt.wav create mode 100644 src/voice/cosyvoice-src/cosyvoice/__init__.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/bin/average_model.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/bin/export_jit.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/bin/export_onnx.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/bin/train.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/cli/__init__.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/cli/cosyvoice.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/cli/frontend.py create mode 100644 src/voice/cosyvoice-src/cosyvoice/cli/model.py create mode 100644 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src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/symbols.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/train.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/__init__.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/audio.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/get_durations_from_trained_model.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/instantiators.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/logging_utils.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/model.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/pylogger.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/rich_utils.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/utils.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/notebooks/.gitkeep create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/pyproject.toml create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/requirements.txt create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/scripts/schedule.sh create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/setup.py create mode 100644 src/voice/cosyvoice-src/third_party/Matcha-TTS/synthesis.ipynb create mode 100644 src/voice/cosyvoice-test.py create mode 100644 src/voice/test-cosyvoice-provider.mjs create mode 100644 src/voice/tts_cosyvoice.py create mode 100644 src/voice/voxcpm-clone-test.py create mode 100644 src/voice/voxcpm-test.py create mode 100644 待办.txt diff --git a/config.json b/config.json index 9ca39cd..c370627 100644 --- a/config.json +++ b/config.json @@ -2,13 +2,14 @@ "clawbot": {}, "voice": { "whisperModel": "small", - "voiceProvider": "volcengine", + "voiceProvider": "local", "volcAsrApiKey": "0f9a6c2b-8d91-4f2b-92b0-531c357b24da", - "volcAsrResourceId": "volc.bigasr.sauc.duration" + "volcAsrResourceId": "volc.bigasr.sauc.duration", + "voiceEngine": "sensevoice" }, "tts": { - "ttsProvider": "doubao", - "ttsVoiceId": "zh_female_xiaohe_uranus_bigtts", + "ttsProvider": "cosyvoice", + "ttsVoiceId": "model", "doubaoKey": "0f9a6c2b-8d91-4f2b-92b0-531c357b24da" }, "embedding": {}, diff --git a/package.json b/package.json index bfbe366..73bb330 100644 --- a/package.json +++ b/package.json @@ -72,6 +72,7 @@ "!scripts/smoke-*.mjs", "!src/**/__pycache__/**", "!src/**/*.pyc", + "!src/voice/models/CosyVoice2-0.5B/**", "!**/node_modules/*/{CHANGELOG.md,README.md,readme.md,*.d.ts}", "!**/node_modules/.cache", "!**/.vscode", diff --git a/src/voice/cosyvoice-opt-test.py b/src/voice/cosyvoice-opt-test.py new file mode 100644 index 0000000..3666348 --- /dev/null +++ b/src/voice/cosyvoice-opt-test.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +"""CosyVoice2 性能对比：非流式 / 流式 / 流式+jit + +用法: py -3 src/voice/cosyvoice-opt-test.py [--jit] [--stream] +""" +import os +import sys +import time +import subprocess + +BASE = os.path.dirname(os.path.abspath(__file__)) +MODEL_DIR = os.path.join(BASE, "models", "CosyVoice2-0.5B") +REF_WAV = os.path.join(BASE, "models", "melo-tts", "ref_female.wav") +REF_TEXT = "你好，我是小雅，今天想和你聊聊天。" + +DO_JIT = "--jit" in sys.argv +DO_STREAM = "--stream" in sys.argv + +sys.path.insert(0, os.path.join(BASE, "cosyvoice-src")) +sys.path.insert(0, os.path.join(BASE, "cosyvoice-src", "third_party", "Matcha-TTS")) + +def gpu_mem(): + try: + out = subprocess.run( + ["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=5, + ).stdout.strip() + return out + except Exception: + return "N/A" + +def main(): + tag = "stream" if DO_STREAM else "batch" + if DO_JIT: + tag += "+jit" + print(f"[opt] 配置: {tag} | GPU: {gpu_mem()} MiB", flush=True) + t0 = time.time() + + from cosyvoice.cli.cosyvoice import AutoModel + cosyvoice = AutoModel(model_dir=MODEL_DIR, fp16=True, load_jit=DO_JIT) + print(f"[opt] 模型加载: {time.time()-t0:.1f}s | GPU: {gpu_mem()} MiB", flush=True) + + text = "你好，我是小雅，很高兴认识你。今天我们聊聊白龙马的奇妙旅程吧。" + + import torch + t1 = time.time() + first_chunk_t = None + chunks = [] + gen = cosyvoice.inference_zero_shot(text, REF_TEXT, REF_WAV, stream=DO_STREAM) + for i, j in enumerate(gen): + if i == 0: + first_chunk_t = time.time() - t1 + print(f"[opt] 首包: {first_chunk_t:.1f}s | GPU: {gpu_mem()} MiB", flush=True) + chunks.append(j['tts_speech']) + elapsed = time.time() - t1 + wav = torch.cat(chunks, dim=1) + duration = wav.shape[1] / cosyvoice.sample_rate + print(f"[opt] 合成: {elapsed:.1f}s | 音频 {duration:.1f}s | RTF {elapsed/duration:.2f}" + f" | 首包 {first_chunk_t:.1f}s | GPU: {gpu_mem()} MiB", flush=True) + + import soundfile as sf + sf.write(f"cosyvoice_{tag}.wav", wav.squeeze(0).cpu().numpy(), cosyvoice.sample_rate) + print(f"[opt] 已保存: cosyvoice_{tag}.wav", flush=True) + +if __name__ == "__main__": + main() diff --git a/src/voice/cosyvoice-src/asset/cross_lingual_prompt.wav b/src/voice/cosyvoice-src/asset/cross_lingual_prompt.wav new file mode 100644 index 0000000000000000000000000000000000000000..35d6d4eed08b24ca379fea873c4925bdb0b58d1e GIT binary patch literal 606404 zcmeFaWt1GZwkRxBRWnZydrUFKF~!V|nVBguGsf(gA?Cy!Gc(&UGcz-@$1~%h=ISEp z+vPs}jeYJp@11-9-1XLS)#@shw6!H|skWpI)y?bGu6?4a0kx^tx>nBt!*baXLKx2P zS|e0*CqfLeqwZaXby*59luA^j0$dfitEck+ha`xVs0^nH5ri{z4c&Qo3Y|Qp@L!yv 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zAHFb#r|FK^-U)4loFfnA_cV{GzePS`dNc_AY|G=1+%;Za;G^O)bZaSoOPO{$Lp^FpsfjY6p=6@&9YZ{{<9zuyzhuqE1Kry`|z{ch~pA{8Z~nR|~l1+wWN?(o41$ zb2c3Pdhjdto7890dyjS#z+>v+$Nv~3KaaRo*&G-A5P!2O=(WguXDAOO&sTXY)@i;M z9zgC~`ex%b;k`$mOvBCE{0Psu<9$V8zo;D#%xCQa_3hwm;fKJD#xYLYL%~!wJc4%^ z9e2?Y@F{!PU-Saokg4iBVo8j|JNzQ@hx{1teb{f8I-KX@nIu2g#Ta{m+4%QW@^@qTKKMtVtZqF2>N{Rpz=8&zUztpx`U0Y^?8f>vEU3ayjuTGZ=F0| zs>j*7Cz^dJtp$ZxYB2t5l-q&c)__ literal 0 HcmV?d00001 diff --git a/src/voice/cosyvoice-src/cosyvoice/__init__.py b/src/voice/cosyvoice-src/cosyvoice/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/cosyvoice/bin/average_model.py b/src/voice/cosyvoice-src/cosyvoice/bin/average_model.py new file mode 100644 index 0000000..b7140c1 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/bin/average_model.py @@ -0,0 +1,93 @@ +# Copyright (c) 2020 Mobvoi Inc (Di Wu) +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import glob + +import yaml +import torch + + +def get_args(): + parser = argparse.ArgumentParser(description='average model') + parser.add_argument('--dst_model', required=True, help='averaged model') + parser.add_argument('--src_path', + required=True, + help='src model path for average') + parser.add_argument('--val_best', + action="store_true", + help='averaged model') + parser.add_argument('--num', + default=5, + type=int, + help='nums for averaged model') + + args = parser.parse_args() + print(args) + return args + + +def main(): + args = get_args() + val_scores = [] + if args.val_best: + yamls = glob.glob('{}/*.yaml'.format(args.src_path)) + yamls = [ + f for f in yamls + if not (os.path.basename(f).startswith('train') + or os.path.basename(f).startswith('init')) + ] + for y in yamls: + with open(y, 'r') as f: + dic_yaml = yaml.load(f, Loader=yaml.BaseLoader) + loss = float(dic_yaml['loss_dict']['loss']) + epoch = int(dic_yaml['epoch']) + step = int(dic_yaml['step']) + tag = dic_yaml['tag'] + val_scores += [[epoch, step, loss, tag]] + sorted_val_scores = sorted(val_scores, + key=lambda x: x[2], + reverse=False) + print("best val (epoch, step, loss, tag) = " + + str(sorted_val_scores[:args.num])) + path_list = [ + args.src_path + '/epoch_{}_whole.pt'.format(score[0]) + for score in sorted_val_scores[:args.num] + ] + print(path_list) + avg = {} + num = args.num + assert num == len(path_list) + for path in path_list: + print('Processing {}'.format(path)) + states = torch.load(path, map_location=torch.device('cpu')) + for k in states.keys(): + if k not in ['step', 'epoch']: + if k not in avg.keys(): + avg[k] = states[k].clone() + else: + avg[k] += states[k] + # average + for k in avg.keys(): + if avg[k] is not None: + # pytorch 1.6 use true_divide instead of /= + avg[k] = torch.true_divide(avg[k], num) + print('Saving to {}'.format(args.dst_model)) + torch.save(avg, args.dst_model) + + +if __name__ == '__main__': + main() diff --git a/src/voice/cosyvoice-src/cosyvoice/bin/export_jit.py b/src/voice/cosyvoice-src/cosyvoice/bin/export_jit.py new file mode 100644 index 0000000..3ecfd05 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/bin/export_jit.py @@ -0,0 +1,99 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import os +import sys +import torch +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../..'.format(ROOT_DIR)) +sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import AutoModel +from cosyvoice.utils.file_utils import logging + + +def get_args(): + parser = argparse.ArgumentParser(description='export your model for deployment') + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice-300M', + help='local path') + args = parser.parse_args() + print(args) + return args + + +def get_optimized_script(model, preserved_attrs=[]): + script = torch.jit.script(model) + if preserved_attrs != []: + script = torch.jit.freeze(script, preserved_attrs=preserved_attrs) + else: + script = torch.jit.freeze(script) + script = torch.jit.optimize_for_inference(script) + return script + + +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + torch._C._jit_set_fusion_strategy([('STATIC', 1)]) + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + + model = AutoModel(model_dir=args.model_dir) + + if model.__class__.__name__ == 'CosyVoice': + # 1. export llm text_encoder + llm_text_encoder = model.model.llm.text_encoder + script = get_optimized_script(llm_text_encoder) + script.save('{}/llm.text_encoder.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(llm_text_encoder.half()) + script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir)) + logging.info('successfully export llm_text_encoder') + + # 2. export llm llm + llm_llm = model.model.llm.llm + script = get_optimized_script(llm_llm, ['forward_chunk']) + script.save('{}/llm.llm.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(llm_llm.half(), ['forward_chunk']) + script.save('{}/llm.llm.fp16.zip'.format(args.model_dir)) + logging.info('successfully export llm_llm') + + # 3. export flow encoder + flow_encoder = model.model.flow.encoder + script = get_optimized_script(flow_encoder) + script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(flow_encoder.half()) + script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir)) + logging.info('successfully export flow_encoder') + elif model.__class__.__name__ == 'CosyVoice2': + # 1. export flow encoder + flow_encoder = model.model.flow.encoder + script = get_optimized_script(flow_encoder) + script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(flow_encoder.half()) + script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir)) + logging.info('successfully export flow_encoder') + else: + raise ValueError('unsupported model type') + + +if __name__ == '__main__': + main() diff --git a/src/voice/cosyvoice-src/cosyvoice/bin/export_onnx.py b/src/voice/cosyvoice-src/cosyvoice/bin/export_onnx.py new file mode 100644 index 0000000..58e7708 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/bin/export_onnx.py @@ -0,0 +1,114 @@ +# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com) +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import os +import sys +import onnxruntime +import random +import torch +from tqdm import tqdm +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../..'.format(ROOT_DIR)) +sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import AutoModel +from cosyvoice.utils.file_utils import logging + + +def get_dummy_input(batch_size, seq_len, out_channels, device): + x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device) + mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + t = torch.rand((batch_size), dtype=torch.float32, device=device) + spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device) + cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + return x, mask, mu, t, spks, cond + + +def get_args(): + parser = argparse.ArgumentParser(description='export your model for deployment') + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice-300M', + help='local path') + args = parser.parse_args() + print(args) + return args + + +@torch.no_grad() +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + model = AutoModel(model_dir=args.model_dir) + + # 1. export flow decoder estimator + estimator = model.model.flow.decoder.estimator + estimator.eval() + + device = model.model.device + batch_size, seq_len = 2, 256 + out_channels = model.model.flow.decoder.estimator.out_channels + x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device) + torch.onnx.export( + estimator, + (x, mask, mu, t, spks, cond), + '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), + export_params=True, + opset_version=18, + do_constant_folding=True, + input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'], + output_names=['estimator_out'], + dynamic_axes={ + 'x': {2: 'seq_len'}, + 'mask': {2: 'seq_len'}, + 'mu': {2: 'seq_len'}, + 'cond': {2: 'seq_len'}, + 'estimator_out': {2: 'seq_len'}, + } + ) + + # 2. test computation consistency + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider'] + estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), + sess_options=option, providers=providers) + + for _ in tqdm(range(10)): + x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device) + output_pytorch = estimator(x, mask, mu, t, spks, cond) + ort_inputs = { + 'x': x.cpu().numpy(), + 'mask': mask.cpu().numpy(), + 'mu': mu.cpu().numpy(), + 't': t.cpu().numpy(), + 'spks': spks.cpu().numpy(), + 'cond': cond.cpu().numpy() + } + output_onnx = estimator_onnx.run(None, ort_inputs)[0] + torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4) + logging.info('successfully export estimator') + + +if __name__ == "__main__": + main() diff --git a/src/voice/cosyvoice-src/cosyvoice/bin/train.py b/src/voice/cosyvoice-src/cosyvoice/bin/train.py new file mode 100644 index 0000000..d650161 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/bin/train.py @@ -0,0 +1,195 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function +import argparse +import datetime +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +from copy import deepcopy +import os +import torch +import torch.distributed as dist +import deepspeed + +from hyperpyyaml import load_hyperpyyaml + +from torch.distributed.elastic.multiprocessing.errors import record + +from cosyvoice.utils.losses import DPOLoss +from cosyvoice.utils.executor import Executor +from cosyvoice.utils.train_utils import ( + init_distributed, + init_dataset_and_dataloader, + init_optimizer_and_scheduler, + init_summarywriter, save_model, + wrap_cuda_model, check_modify_and_save_config) + + +def get_args(): + parser = argparse.ArgumentParser(description='training your network') + parser.add_argument('--train_engine', + default='torch_ddp', + choices=['torch_ddp', 'deepspeed'], + help='Engine for paralleled training') + parser.add_argument('--model', required=True, help='model which will be trained') + parser.add_argument('--ref_model', required=False, help='ref model used in dpo') + parser.add_argument('--config', required=True, help='config file') + parser.add_argument('--train_data', required=True, help='train data file') + parser.add_argument('--cv_data', required=True, help='cv data file') + parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path') + parser.add_argument('--onnx_path', required=False, help='onnx path, which is required for online feature extraction') + parser.add_argument('--checkpoint', help='checkpoint model') + parser.add_argument('--model_dir', required=True, help='save model dir') + parser.add_argument('--tensorboard_dir', + default='tensorboard', + help='tensorboard log dir') + parser.add_argument('--ddp.dist_backend', + dest='dist_backend', + default='nccl', + choices=['nccl', 'gloo'], + help='distributed backend') + parser.add_argument('--num_workers', + default=0, + type=int, + help='num of subprocess workers for reading') + parser.add_argument('--prefetch', + default=100, + type=int, + help='prefetch number') + parser.add_argument('--pin_memory', + action='store_true', + default=False, + help='Use pinned memory buffers used for reading') + parser.add_argument('--use_amp', + action='store_true', + default=False, + help='Use automatic mixed precision training') + parser.add_argument('--dpo', + action='store_true', + default=False, + help='Use Direct Preference Optimization') + parser.add_argument('--deepspeed.save_states', + dest='save_states', + default='model_only', + choices=['model_only', 'model+optimizer'], + help='save model/optimizer states') + parser.add_argument('--timeout', + default=60, + type=int, + help='timeout (in seconds) of cosyvoice_join.') + parser = deepspeed.add_config_arguments(parser) + args = parser.parse_args() + return args + + +@record +def main(): + args = get_args() + os.environ['onnx_path'] = args.onnx_path + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + # gan train has some special initialization logic + gan = True if args.model == 'hifigan' else False + + override_dict = {k: None for k in ['llm', 'flow', 'hift', 'hifigan'] if k != args.model} + if gan is True: + override_dict.pop('hift') + if args.qwen_pretrain_path is not None: + override_dict['qwen_pretrain_path'] = args.qwen_pretrain_path + with open(args.config, 'r') as f: + configs = load_hyperpyyaml(f, overrides=override_dict) + if gan is True: + configs['train_conf'] = configs['train_conf_gan'] + configs['train_conf'].update(vars(args)) + + # Init env for ddp + init_distributed(args) + + # Get dataset & dataloader + train_dataset, cv_dataset, train_data_loader, cv_data_loader = \ + init_dataset_and_dataloader(args, configs, gan, args.dpo) + + # Do some sanity checks and save config to arsg.model_dir + configs = check_modify_and_save_config(args, configs) + + # Tensorboard summary + writer = init_summarywriter(args) + + # load checkpoint + if args.dpo is True: + configs[args.model].forward = configs[args.model].forward_dpo + model = configs[args.model] + start_step, start_epoch = 0, -1 + if args.checkpoint is not None: + if os.path.exists(args.checkpoint): + state_dict = torch.load(args.checkpoint, map_location='cpu') + model.load_state_dict(state_dict, strict=False) + if 'step' in state_dict: + start_step = state_dict['step'] + if 'epoch' in state_dict: + start_epoch = state_dict['epoch'] + else: + logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint)) + + # Dispatch model from cpu to gpu + model = wrap_cuda_model(args, model) + + # Get optimizer & scheduler + model, optimizer, scheduler, optimizer_d, scheduler_d = init_optimizer_and_scheduler(args, configs, model, gan) + scheduler.set_step(start_step) + if scheduler_d is not None: + scheduler_d.set_step(start_step) + + # Save init checkpoints + info_dict = deepcopy(configs['train_conf']) + info_dict['step'] = start_step + info_dict['epoch'] = start_epoch + save_model(model, 'init', info_dict) + + # DPO related + if args.dpo is True: + ref_model = deepcopy(configs[args.model]) + state_dict = torch.load(args.ref_model, map_location='cpu') + ref_model.load_state_dict(state_dict, strict=False) + dpo_loss = DPOLoss(beta=0.01, label_smoothing=0.0, ipo=False) + # NOTE maybe it is not needed to wrap ref_model as ddp because its parameter is not updated + ref_model = wrap_cuda_model(args, ref_model) + else: + ref_model, dpo_loss = None, None + + # Get executor + executor = Executor(gan=gan, ref_model=ref_model, dpo_loss=dpo_loss) + executor.step = start_step + + # Init scaler, used for pytorch amp mixed precision training + scaler = torch.cuda.amp.GradScaler() if args.use_amp else None + print('start step {} start epoch {}'.format(start_step, start_epoch)) + + # Start training loop + for epoch in range(start_epoch + 1, info_dict['max_epoch']): + executor.epoch = epoch + train_dataset.set_epoch(epoch) + dist.barrier() + group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout)) + if gan is True: + executor.train_one_epoc_gan(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, + writer, info_dict, scaler, group_join) + else: + executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=ref_model) + dist.destroy_process_group(group_join) + + +if __name__ == '__main__': + main() diff --git a/src/voice/cosyvoice-src/cosyvoice/cli/__init__.py b/src/voice/cosyvoice-src/cosyvoice/cli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/cosyvoice/cli/cosyvoice.py b/src/voice/cosyvoice-src/cosyvoice/cli/cosyvoice.py new file mode 100644 index 0000000..7ab04a7 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/cli/cosyvoice.py @@ -0,0 +1,238 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import time +from typing import Generator +from tqdm import tqdm +from hyperpyyaml import load_hyperpyyaml +from modelscope import snapshot_download +import torch +from cosyvoice.cli.frontend import CosyVoiceFrontEnd +from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model +from cosyvoice.utils.file_utils import logging +from cosyvoice.utils.class_utils import get_model_type + + +class CosyVoice: + + def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1): + self.model_dir = model_dir + self.fp16 = fp16 + if not os.path.exists(model_dir): + model_dir = snapshot_download(model_dir) + hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f) + assert get_model_type(configs) == CosyVoiceModel, 'do not use {} for CosyVoice initialization!'.format(model_dir) + self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], + configs['feat_extractor'], + '{}/campplus.onnx'.format(model_dir), + '{}/speech_tokenizer_v1.onnx'.format(model_dir), + '{}/spk2info.pt'.format(model_dir), + configs['allowed_special']) + self.sample_rate = configs['sample_rate'] + if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True): + load_jit, load_trt, fp16 = False, False, False + logging.warning('no cuda device, set load_jit/load_trt/fp16 to False') + self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16) + self.model.load('{}/llm.pt'.format(model_dir), + '{}/flow.pt'.format(model_dir), + '{}/hift.pt'.format(model_dir)) + if load_jit: + self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) + if load_trt: + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + del configs + + def list_available_spks(self): + spks = list(self.frontend.spk2info.keys()) + return spks + + def add_zero_shot_spk(self, prompt_text, prompt_wav, zero_shot_spk_id): + assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id' + model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_wav, self.sample_rate, '') + del model_input['text'] + del model_input['text_len'] + self.frontend.spk2info[zero_shot_spk_id] = model_input + return True + + def save_spkinfo(self): + torch.save(self.frontend.spk2info, '{}/spk2info.pt'.format(self.model_dir)) + + def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_sft(i, spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_zero_shot(self, tts_text, prompt_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True): + prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend) + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text): + logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text)) + model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_wav, self.sample_rate, zero_shot_spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_cross_lingual(self, tts_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_cross_lingual(i, prompt_wav, self.sample_rate, zero_shot_spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True): + assert self.__class__.__name__ == 'CosyVoice', 'inference_instruct is only implemented for CosyVoice!' + instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend) + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_vc(self, source_wav, prompt_wav, stream=False, speed=1.0): + model_input = self.frontend.frontend_vc(source_wav, prompt_wav, self.sample_rate) + start_time = time.time() + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + +class CosyVoice2(CosyVoice): + + def __init__(self, model_dir, load_jit=False, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1): + self.model_dir = model_dir + self.fp16 = fp16 + if not os.path.exists(model_dir): + model_dir = snapshot_download(model_dir) + hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) + assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir) + self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], + configs['feat_extractor'], + '{}/campplus.onnx'.format(model_dir), + '{}/speech_tokenizer_v2.onnx'.format(model_dir), + '{}/spk2info.pt'.format(model_dir), + configs['allowed_special']) + self.sample_rate = configs['sample_rate'] + if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or load_vllm is True or fp16 is True): + load_jit, load_trt, load_vllm, fp16 = False, False, False, False + logging.warning('no cuda device, set load_jit/load_trt/load_vllm/fp16 to False') + self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16) + self.model.load('{}/llm.pt'.format(model_dir), + '{}/flow.pt'.format(model_dir), + '{}/hift.pt'.format(model_dir)) + if load_vllm: + self.model.load_vllm('{}/vllm'.format(model_dir)) + if load_jit: + self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) + if load_trt: + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + del configs + + def inference_instruct2(self, tts_text, instruct_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_wav, self.sample_rate, zero_shot_spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + +class CosyVoice3(CosyVoice2): + + def __init__(self, model_dir, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1): + self.model_dir = model_dir + self.fp16 = fp16 + if not os.path.exists(model_dir): + model_dir = snapshot_download(model_dir) + hyper_yaml_path = '{}/cosyvoice3.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) + assert get_model_type(configs) == CosyVoice3Model, 'do not use {} for CosyVoice3 initialization!'.format(model_dir) + self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], + configs['feat_extractor'], + '{}/campplus.onnx'.format(model_dir), + '{}/speech_tokenizer_v3.onnx'.format(model_dir), + '{}/spk2info.pt'.format(model_dir), + configs['allowed_special']) + self.sample_rate = configs['sample_rate'] + if torch.cuda.is_available() is False and (load_trt is True or fp16 is True): + load_trt, fp16 = False, False + logging.warning('no cuda device, set load_trt/fp16 to False') + self.model = CosyVoice3Model(configs['llm'], configs['flow'], configs['hift'], fp16) + self.model.load('{}/llm.pt'.format(model_dir), + '{}/flow.pt'.format(model_dir), + '{}/hift.pt'.format(model_dir)) + if load_vllm: + self.model.load_vllm('{}/vllm'.format(model_dir)) + if load_trt: + if self.fp16 is True: + logging.warning('DiT tensorRT fp16 engine have some performance issue, use at caution!') + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + del configs + + +def AutoModel(**kwargs): + if not os.path.exists(kwargs['model_dir']): + kwargs['model_dir'] = snapshot_download(kwargs['model_dir']) + if os.path.exists('{}/cosyvoice.yaml'.format(kwargs['model_dir'])): + return CosyVoice(**kwargs) + elif os.path.exists('{}/cosyvoice2.yaml'.format(kwargs['model_dir'])): + return CosyVoice2(**kwargs) + elif os.path.exists('{}/cosyvoice3.yaml'.format(kwargs['model_dir'])): + return CosyVoice3(**kwargs) + else: + raise TypeError('No valid model type found!') diff --git a/src/voice/cosyvoice-src/cosyvoice/cli/frontend.py b/src/voice/cosyvoice-src/cosyvoice/cli/frontend.py new file mode 100644 index 0000000..6d397cc --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/cli/frontend.py @@ -0,0 +1,224 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial +from typing import Generator +import json +import onnxruntime +import torch +import numpy as np +import whisper +from typing import Callable +import torchaudio.compliance.kaldi as kaldi +import os +import re +import inflect +from cosyvoice.utils.file_utils import logging, load_wav +from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation + + +class CosyVoiceFrontEnd: + + def __init__(self, + get_tokenizer: Callable, + feat_extractor: Callable, + campplus_model: str, + speech_tokenizer_model: str, + spk2info: str = '', + allowed_special: str = 'all'): + self.tokenizer = get_tokenizer() + self.feat_extractor = feat_extractor + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"]) + self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, + providers=["CUDAExecutionProvider" if torch.cuda.is_available() else + "CPUExecutionProvider"]) + if os.path.exists(spk2info): + self.spk2info = torch.load(spk2info, map_location=self.device, weights_only=True) + else: + self.spk2info = {} + self.allowed_special = allowed_special + self.inflect_parser = inflect.engine() + # NOTE compatible when no text frontend tool is avaliable + try: + import ttsfrd + self.frd = ttsfrd.TtsFrontendEngine() + ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) + assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, \ + 'failed to initialize ttsfrd resource' + self.frd.set_lang_type('pinyinvg') + self.text_frontend = 'ttsfrd' + logging.info('use ttsfrd frontend') + except: + try: + from wetext import Normalizer as ZhNormalizer + from wetext import Normalizer as EnNormalizer + self.zh_tn_model = ZhNormalizer(remove_erhua=False) + self.en_tn_model = EnNormalizer() + self.text_frontend = 'wetext' + logging.info('use wetext frontend') + except: + self.text_frontend = '' + logging.info('no frontend is avaliable') + + + def _extract_text_token(self, text): + if isinstance(text, Generator): + logging.info('get tts_text generator, will return _extract_text_token_generator!') + # NOTE add a dummy text_token_len for compatibility + return self._extract_text_token_generator(text), torch.tensor([0], dtype=torch.int32).to(self.device) + else: + text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special) + text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device) + text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device) + return text_token, text_token_len + + def _extract_text_token_generator(self, text_generator): + for text in text_generator: + text_token, _ = self._extract_text_token(text) + for i in range(text_token.shape[1]): + yield text_token[:, i: i + 1] + + def _extract_speech_token(self, prompt_wav): + speech = load_wav(prompt_wav, 16000) + assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s' + feat = whisper.log_mel_spectrogram(speech, n_mels=128) + speech_token = self.speech_tokenizer_session.run(None, + {self.speech_tokenizer_session.get_inputs()[0].name: + feat.detach().cpu().numpy(), + self.speech_tokenizer_session.get_inputs()[1].name: + np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() + speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device) + speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device) + return speech_token, speech_token_len + + def _extract_spk_embedding(self, prompt_wav): + speech = load_wav(prompt_wav, 16000) + feat = kaldi.fbank(speech, + num_mel_bins=80, + dither=0, + sample_frequency=16000) + feat = feat - feat.mean(dim=0, keepdim=True) + embedding = self.campplus_session.run(None, + {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() + embedding = torch.tensor([embedding]).to(self.device) + return embedding + + def _extract_speech_feat(self, prompt_wav): + speech = load_wav(prompt_wav, 24000) + speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device) + speech_feat = speech_feat.unsqueeze(dim=0) + speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device) + return speech_feat, speech_feat_len + + def text_normalize(self, text, split=True, text_frontend=True): + if isinstance(text, Generator): + logging.info('get tts_text generator, will skip text_normalize!') + return [text] + # NOTE skip text_frontend when ssml symbol in text + if '<|' in text and '|>' in text: + text_frontend = False + if text_frontend is False or text == '': + return [text] if split is True else text + text = text.strip() + if self.text_frontend == 'ttsfrd': + texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]] + text = ''.join(texts) + else: + if contains_chinese(text): + if self.text_frontend == 'wetext': + text = self.zh_tn_model.normalize(text) + text = text.replace("\n", "") + text = replace_blank(text) + text = replace_corner_mark(text) + text = text.replace(".", "。") + text = text.replace(" - ", "，") + text = remove_bracket(text) + text = re.sub(r'[，,、]+$', '。', text) + texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80, + token_min_n=60, merge_len=20, comma_split=False)) + else: + if self.text_frontend == 'wetext': + text = self.en_tn_model.normalize(text) + text = spell_out_number(text, self.inflect_parser) + texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80, + token_min_n=60, merge_len=20, comma_split=False)) + texts = [i for i in texts if not is_only_punctuation(i)] + return texts if split is True else text + + def frontend_sft(self, tts_text, spk_id): + tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) + embedding = self.spk2info[spk_id]['embedding'] + model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding} + return model_input + + def frontend_zero_shot(self, tts_text, prompt_text, prompt_wav, resample_rate, zero_shot_spk_id): + tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) + if zero_shot_spk_id == '': + prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text) + speech_feat, speech_feat_len = self._extract_speech_feat(prompt_wav) + speech_token, speech_token_len = self._extract_speech_token(prompt_wav) + if resample_rate == 24000: + # cosyvoice2, force speech_feat % speech_token = 2 + token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1]) + speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len + speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len + embedding = self._extract_spk_embedding(prompt_wav) + model_input = {'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len, + 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len, + 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len, + 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len, + 'llm_embedding': embedding, 'flow_embedding': embedding} + else: + model_input = {**self.spk2info[zero_shot_spk_id]} + model_input['text'] = tts_text_token + model_input['text_len'] = tts_text_token_len + return model_input + + def frontend_cross_lingual(self, tts_text, prompt_wav, resample_rate, zero_shot_spk_id): + model_input = self.frontend_zero_shot(tts_text, '', prompt_wav, resample_rate, zero_shot_spk_id) + # in cross lingual mode, we remove prompt in llm + del model_input['prompt_text'] + del model_input['prompt_text_len'] + del model_input['llm_prompt_speech_token'] + del model_input['llm_prompt_speech_token_len'] + return model_input + + def frontend_instruct(self, tts_text, spk_id, instruct_text): + model_input = self.frontend_sft(tts_text, spk_id) + # in instruct mode, we remove spk_embedding in llm due to information leakage + del model_input['llm_embedding'] + instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text) + model_input['prompt_text'] = instruct_text_token + model_input['prompt_text_len'] = instruct_text_token_len + return model_input + + def frontend_instruct2(self, tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id): + model_input = self.frontend_zero_shot(tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id) + del model_input['llm_prompt_speech_token'] + del model_input['llm_prompt_speech_token_len'] + return model_input + + def frontend_vc(self, source_speech_16k, prompt_wav, resample_rate): + prompt_speech_token, prompt_speech_token_len = self._extract_speech_token(prompt_wav) + prompt_speech_feat, prompt_speech_feat_len = self._extract_speech_feat(prompt_wav) + embedding = self._extract_spk_embedding(prompt_wav) + source_speech_token, source_speech_token_len = self._extract_speech_token(source_speech_16k) + model_input = {'source_speech_token': source_speech_token, 'source_speech_token_len': source_speech_token_len, + 'flow_prompt_speech_token': prompt_speech_token, 'flow_prompt_speech_token_len': prompt_speech_token_len, + 'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len, + 'flow_embedding': embedding} + return model_input diff --git a/src/voice/cosyvoice-src/cosyvoice/cli/model.py b/src/voice/cosyvoice-src/cosyvoice/cli/model.py new file mode 100644 index 0000000..92a15d9 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/cli/model.py @@ -0,0 +1,450 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +from typing import Generator +import torch +import numpy as np +import threading +import time +from torch.nn import functional as F +from contextlib import nullcontext +import uuid +from cosyvoice.utils.common import fade_in_out +from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm +from cosyvoice.utils.common import TrtContextWrapper + + +class CosyVoiceModel: + + def __init__(self, + llm: torch.nn.Module, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.llm = llm + self.flow = flow + self.hift = hift + self.fp16 = fp16 + self.token_min_hop_len = 2 * self.flow.input_frame_rate + self.token_max_hop_len = 4 * self.flow.input_frame_rate + self.token_overlap_len = 20 + # mel fade in out + self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256) + self.mel_window = np.hamming(2 * self.mel_overlap_len) + # hift cache + self.mel_cache_len = 20 + self.source_cache_len = int(self.mel_cache_len * 256) + # speech fade in out + self.speech_window = np.hamming(2 * self.source_cache_len) + # rtf and decoding related + self.stream_scale_factor = 1 + assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf' + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.mel_overlap_dict = {} + self.flow_cache_dict = {} + self.hift_cache_dict = {} + self.silent_tokens = [] + + def load(self, llm_model, flow_model, hift_model): + self.llm.load_state_dict(torch.load(llm_model, map_location=self.device, weights_only=True), strict=True) + self.llm.to(self.device).eval() + self.flow.load_state_dict(torch.load(flow_model, map_location=self.device, weights_only=True), strict=True) + self.flow.to(self.device).eval() + # in case hift_model is a hifigan model + hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device, weights_only=True).items()} + self.hift.load_state_dict(hift_state_dict, strict=True) + self.hift.to(self.device).eval() + + def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model): + llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device) + self.llm.text_encoder = llm_text_encoder + llm_llm = torch.jit.load(llm_llm_model, map_location=self.device) + self.llm.llm = llm_llm + flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) + self.flow.encoder = flow_encoder + + def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16): + assert torch.cuda.is_available(), 'tensorrt only supports gpu!' + if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0: + convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16) + del self.flow.decoder.estimator + import tensorrt as trt + with open(flow_decoder_estimator_model, 'rb') as f: + estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model) + self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_trt_kwargs(self): + min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)] + opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)] + max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)] + input_names = ["x", "mask", "mu", "cond"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): + cur_silent_token_num, max_silent_token_num = 0, 5 + with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False): + if isinstance(text, Generator): + assert (self.__class__.__name__ != 'CosyVoiceModel') and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2/3 and do not support vllm!' + token_generator = self.llm.inference_bistream(text=text, + prompt_text=prompt_text.to(self.device), + prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), + prompt_speech_token=llm_prompt_speech_token.to(self.device), + prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), + embedding=llm_embedding.to(self.device)) + else: + token_generator = self.llm.inference(text=text.to(self.device), + text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device), + prompt_text=prompt_text.to(self.device), + prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), + prompt_speech_token=llm_prompt_speech_token.to(self.device), + prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), + embedding=llm_embedding.to(self.device), + uuid=uuid) + for i in token_generator: + if i in self.silent_tokens: + cur_silent_token_num += 1 + if cur_silent_token_num > max_silent_token_num: + continue + else: + cur_silent_token_num = 0 + self.tts_speech_token_dict[uuid].append(i) + self.llm_end_dict[uuid] = True + + def vc_job(self, source_speech_token, uuid): + self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist() + self.llm_end_dict[uuid] = True + + def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device, dtype=torch.int32), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + flow_cache=self.flow_cache_dict[uuid]) + + # mel overlap fade in out + if self.mel_overlap_dict[uuid].shape[2] != 0: + tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window) + # append hift cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + else: + hift_cache_source = torch.zeros(1, 1, 0) + # keep overlap mel and hift cache + if finalize is False: + self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:] + tts_mel = tts_mel[:, :, :-self.mel_overlap_len] + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], + 'source': tts_source[:, :, -self.source_cache_len:], + 'speech': tts_speech[:, -self.source_cache_len:]} + tts_speech = tts_speech[:, :-self.source_cache_len] + else: + if speed != 1.0: + assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + return tts_speech + + def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192), + prompt_text=torch.zeros(1, 0, dtype=torch.int32), + llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs): + # this_uuid is used to track variables related to this inference thread + this_uuid = str(uuid.uuid1()) + with self.lock: + self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False + self.hift_cache_dict[this_uuid] = None + self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0) + self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2) + if source_speech_token.shape[1] == 0: + p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) + else: + p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid)) + p.start() + if stream is True: + token_hop_len = self.token_min_hop_len + while True: + time.sleep(0.1) + if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len: + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \ + .unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=False) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:] + # increase token_hop_len for better speech quality + token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor)) + if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len: + break + p.join() + # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=True) + yield {'tts_speech': this_tts_speech.cpu()} + else: + # deal with all tokens + p.join() + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=True, + speed=speed) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict.pop(this_uuid) + self.llm_end_dict.pop(this_uuid) + self.mel_overlap_dict.pop(this_uuid) + self.hift_cache_dict.pop(this_uuid) + self.flow_cache_dict.pop(this_uuid) + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.current_stream().synchronize() + + +class CosyVoice2Model(CosyVoiceModel): + + def __init__(self, + llm: torch.nn.Module, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.llm = llm + self.flow = flow + self.hift = hift + self.fp16 = fp16 + # NOTE must matching training static_chunk_size + self.token_hop_len = 25 + # NOTE increase token_hop_len incrementally to avoid duplicate inference + self.token_max_hop_len = 4 * self.token_hop_len + self.stream_scale_factor = 2 + assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf' + # hift cache + self.mel_cache_len = 8 + self.source_cache_len = int(self.mel_cache_len * 480) + # speech fade in out + self.speech_window = np.hamming(2 * self.source_cache_len) + # rtf and decoding related + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.hift_cache_dict = {} + self.silent_tokens = [] + + def load_jit(self, flow_encoder_model): + flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) + self.flow.encoder = flow_encoder + + def load_vllm(self, model_dir): + export_cosyvoice2_vllm(self.llm, model_dir, self.device) + from vllm import EngineArgs, LLMEngine + engine_args = EngineArgs(model=model_dir, + skip_tokenizer_init=True, + enable_prompt_embeds=True, + gpu_memory_utilization=0.2) + self.llm.vllm = LLMEngine.from_engine_args(engine_args) + self.llm.lock = threading.Lock() + del self.llm.llm.model.model.layers + + def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + streaming=stream, + finalize=finalize) + tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] + # append hift cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + else: + hift_cache_source = torch.zeros(1, 1, 0) + # keep overlap mel and hift cache + if finalize is False: + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], + 'source': tts_source[:, :, -self.source_cache_len:], + 'speech': tts_speech[:, -self.source_cache_len:]} + tts_speech = tts_speech[:, :-self.source_cache_len] + else: + if speed != 1.0: + assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + return tts_speech + + def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192), + prompt_text=torch.zeros(1, 0, dtype=torch.int32), + llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs): + # this_uuid is used to track variables related to this inference thread + this_uuid = str(uuid.uuid1()) + with self.lock: + self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False + self.hift_cache_dict[this_uuid] = None + if source_speech_token.shape[1] == 0: + p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) + else: + p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid)) + p.start() + if stream is True: + token_offset = 0 + prompt_token_pad = int(np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len) * self.token_hop_len - flow_prompt_speech_token.shape[1]) + while True: + time.sleep(0.1) + this_token_hop_len = self.token_hop_len + prompt_token_pad if token_offset == 0 else self.token_hop_len + if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= this_token_hop_len + self.flow.pre_lookahead_len: + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + this_token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + token_offset=token_offset, + uuid=this_uuid, + stream=stream, + finalize=False) + token_offset += this_token_hop_len + self.token_hop_len = min(self.token_max_hop_len, self.token_hop_len * self.stream_scale_factor) + yield {'tts_speech': this_tts_speech.cpu()} + if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < this_token_hop_len + self.flow.pre_lookahead_len: + break + p.join() + # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + token_offset=token_offset, + uuid=this_uuid, + finalize=True) + yield {'tts_speech': this_tts_speech.cpu()} + else: + # deal with all tokens + p.join() + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + token_offset=0, + uuid=this_uuid, + finalize=True, + speed=speed) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict.pop(this_uuid) + self.llm_end_dict.pop(this_uuid) + self.hift_cache_dict.pop(this_uuid) + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.current_stream().synchronize() + + +class CosyVoice3Model(CosyVoice2Model): + + def __init__(self, + llm: torch.nn.Module, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.llm = llm + self.flow = flow + self.hift = hift + self.fp16 = fp16 + # NOTE must matching training static_chunk_size + self.token_hop_len = 25 + # NOTE increase token_hop_len incrementally to avoid duplicate inference + self.token_max_hop_len = 4 * self.token_hop_len + self.stream_scale_factor = 2 + assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf' + # rtf and decoding related + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.hift_cache_dict = {} + # FSQ silent and breath token + self.silent_tokens = [1, 2, 28, 29, 55, 248, 494, 2241, 2242, 2322, 2323] + + def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + streaming=stream, + finalize=finalize) + tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] + # append mel cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel = self.hift_cache_dict[uuid]['mel'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + self.hift_cache_dict[uuid]['mel'] = tts_mel + else: + self.hift_cache_dict[uuid] = {'mel': tts_mel, 'speech_offset': 0} + if speed != 1.0: + assert token_offset == 0 and finalize is True, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, _ = self.hift.inference(speech_feat=tts_mel, finalize=finalize) + tts_speech = tts_speech[:, self.hift_cache_dict[uuid]['speech_offset']:] + self.hift_cache_dict[uuid]['speech_offset'] += tts_speech.shape[1] + return tts_speech diff --git a/src/voice/cosyvoice-src/cosyvoice/dataset/__init__.py b/src/voice/cosyvoice-src/cosyvoice/dataset/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/cosyvoice/dataset/dataset.py b/src/voice/cosyvoice-src/cosyvoice/dataset/dataset.py new file mode 100644 index 0000000..ea19840 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/dataset/dataset.py @@ -0,0 +1,155 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +import math +from functools import partial + +import torch +import torch.distributed as dist +from torch.utils.data import IterableDataset +from cosyvoice.utils.file_utils import read_lists + + +class Processor(IterableDataset): + + def __init__(self, source, f, *args, **kw): + assert callable(f) + self.source = source + self.f = f + self.args = args + self.kw = kw + + def set_epoch(self, epoch): + self.source.set_epoch(epoch) + + def __iter__(self): + """ Return an iterator over the source dataset processed by the + given processor. + """ + assert self.source is not None + assert callable(self.f) + return self.f(iter(self.source), *self.args, **self.kw) + + def apply(self, f): + assert callable(f) + return Processor(self, f, *self.args, **self.kw) + + +class DistributedSampler: + + def __init__(self, shuffle=True, partition=True): + self.epoch = -1 + self.update() + self.shuffle = shuffle + self.partition = partition + + def update(self): + assert dist.is_available() + if dist.is_initialized(): + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + else: + self.rank = 0 + self.world_size = 1 + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + self.worker_id = 0 + self.num_workers = 1 + else: + self.worker_id = worker_info.id + self.num_workers = worker_info.num_workers + return dict(rank=self.rank, + world_size=self.world_size, + worker_id=self.worker_id, + num_workers=self.num_workers) + + def set_epoch(self, epoch): + self.epoch = epoch + + def sample(self, data): + """ Sample data according to rank/world_size/num_workers + + Args: + data(List): input data list + + Returns: + List: data list after sample + """ + data = list(range(len(data))) + # force datalist even + if self.partition: + if self.shuffle: + random.Random(self.epoch).shuffle(data) + if len(data) < self.world_size: + data = data * math.ceil(self.world_size / len(data)) + data = data[:self.world_size] + data = data[self.rank::self.world_size] + if len(data) < self.num_workers: + data = data * math.ceil(self.num_workers / len(data)) + data = data[:self.num_workers] + data = data[self.worker_id::self.num_workers] + return data + + +class DataList(IterableDataset): + + def __init__(self, lists, shuffle=True, partition=True): + self.lists = lists + self.sampler = DistributedSampler(shuffle, partition) + + def set_epoch(self, epoch): + self.sampler.set_epoch(epoch) + + def __iter__(self): + sampler_info = self.sampler.update() + indexes = self.sampler.sample(self.lists) + for index in indexes: + data = dict(src=self.lists[index]) + data.update(sampler_info) + yield data + + +def Dataset(data_list_file, + data_pipeline, + mode='train', + gan=False, + dpo=False, + shuffle=True, + partition=True): + """ Construct dataset from arguments + + We have two shuffle stage in the Dataset. The first is global + shuffle at shards tar/raw file level. The second is global shuffle + at training samples level. + + Args: + data_type(str): raw/shard + tokenizer (BaseTokenizer): tokenizer to tokenize + partition(bool): whether to do data partition in terms of rank + """ + lists = read_lists(data_list_file) + dataset = DataList(lists, + shuffle=shuffle, + partition=partition) + # map partial arg to padding func + for i in range(1, len(data_pipeline)): + if data_pipeline[i].func.__name__ == 'compute_fbank' and gan is True: + data_pipeline[i] = partial(data_pipeline[i], token_mel_ratio=0) + if data_pipeline[i].func.__name__ == 'padding': + data_pipeline[i] = partial(data_pipeline[i], gan=gan, dpo=dpo) + for func in data_pipeline: + dataset = Processor(dataset, func, mode=mode) + return dataset diff --git a/src/voice/cosyvoice-src/cosyvoice/dataset/processor.py b/src/voice/cosyvoice-src/cosyvoice/dataset/processor.py new file mode 100644 index 0000000..545c007 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/dataset/processor.py @@ -0,0 +1,431 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import random + +import pyarrow.parquet as pq +from io import BytesIO +import numpy as np +import whisper +import torch +import torchaudio +from torch.nn.utils.rnn import pad_sequence +import torch.nn.functional as F +import pyworld as pw +from cosyvoice.utils.onnx import embedding_extractor, online_feature + +AUDIO_FORMAT_SETS = {'flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'} + + +def parquet_opener(data, mode='train'): + """ Give url or local file, return file descriptor + Inplace operation. + + Args: + data(Iterable[str]): url or local file list + + Returns: + Iterable[{src, stream}] + """ + for sample in data: + assert 'src' in sample + url = sample['src'] + try: + for df in pq.ParquetFile(url).iter_batches(batch_size=64): + df = df.to_pandas() + for i in range(len(df)): + sample.update(dict(df.loc[i])) + # NOTE do not return sample directly, must initialize a new dict + yield {**sample} + except Exception as ex: + logging.warning('Failed to open {}, ex info {}'.format(url, ex)) + + +def filter(data, + max_length=10240, + min_length=10, + token_max_length=200, + token_min_length=1, + min_output_input_ratio=0.0005, + max_output_input_ratio=1, + mode='train'): + """ Filter sample according to feature and label length + Inplace operation. + + Args:: + data: Iterable[{key, wav, label, sample_rate}] + max_length: drop utterance which is greater than max_length(10ms) + min_length: drop utterance which is less than min_length(10ms) + token_max_length: drop utterance which is greater than + token_max_length, especially when use char unit for + english modeling + token_min_length: drop utterance which is + less than token_max_length + min_output_input_ratio: minimal ration of + token_length / feats_length(10ms) + max_output_input_ratio: maximum ration of + token_length / feats_length(10ms) + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + sample['speech'], sample['sample_rate'] = torchaudio.load(BytesIO(sample['audio_data'])) + sample['speech'] = sample['speech'].mean(dim=0, keepdim=True) + del sample['audio_data'] + # sample['wav'] is torch.Tensor, we have 100 frames every second + num_frames = sample['speech'].size(1) / sample['sample_rate'] * 100 + if num_frames < min_length: + continue + if num_frames > max_length: + continue + if len(sample['text_token']) < token_min_length: + continue + if len(sample['text_token']) > token_max_length: + continue + if online_feature is False and len(sample['speech_token']) == 0: + continue + if online_feature is False and 'reject_speech_token' in sample and len(sample['reject_speech_token']) == 0: + continue + if num_frames != 0: + if len(sample['text_token']) / num_frames < min_output_input_ratio: + continue + if len(sample['text_token']) / num_frames > max_output_input_ratio: + continue + yield sample + + +def resample(data, resample_rate=22050, min_sample_rate=16000, mode='train'): + """ Resample data. + Inplace operation. + + Args: + data: Iterable[{key, wav, label, sample_rate}] + resample_rate: target resample rate + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + assert 'sample_rate' in sample + assert 'speech' in sample + sample_rate = sample['sample_rate'] + waveform = sample['speech'] + if sample_rate != resample_rate: + if sample_rate < min_sample_rate: + continue + sample['sample_rate'] = resample_rate + sample['speech'] = torchaudio.transforms.Resample( + orig_freq=sample_rate, new_freq=resample_rate)(waveform) + max_val = sample['speech'].abs().max() + if max_val > 1: + sample['speech'] /= max_val + yield sample + + +def truncate(data, truncate_length=24576, mode='train'): + """ Truncate data. + + Args: + data: Iterable[{key, wav, label, sample_rate}] + truncate_length: truncate length + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + waveform = sample['speech'] + if waveform.shape[1] > truncate_length: + start = random.randint(0, waveform.shape[1] - truncate_length) + waveform = waveform[:, start: start + truncate_length] + else: + waveform = torch.concat([waveform, torch.zeros(1, truncate_length - waveform.shape[1])], dim=1) + sample['speech'] = waveform + yield sample + + +def compute_fbank(data, + feat_extractor, + num_frames=-1, + mode='train'): + """ Extract fbank + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + assert 'sample_rate' in sample + assert 'speech' in sample + assert 'utt' in sample + assert 'text_token' in sample + # NOTE in cosyvoice2/3, we support online token extraction, so we need to align speech to 25hz first + if num_frames != -1: + index = int(np.ceil(sample['speech'].shape[1] / num_frames)) + sample['speech'] = torch.concat([sample['speech'], torch.zeros(1, index * num_frames - sample['speech'].shape[1])], dim=1) + sample['speech_feat'] = feat_extractor(sample['speech']).squeeze(dim=0).transpose(0, 1) + yield sample + + +def compute_whisper_fbank(data, num_frames=-1, mode='train'): + """ Extract whisper fbank + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + if num_frames != -1: + assert sample['speech'].shape[1] % num_frames == 0, 'speech length is not aligned with speech_token' + sample['speech_16k'] = torchaudio.transforms.Resample(orig_freq=sample['sample_rate'], new_freq=16000)(sample['speech']) + sample['whisper_feat'] = whisper.log_mel_spectrogram(sample['speech_16k'], n_mels=128).squeeze(dim=0).transpose(0, 1) + yield sample + + +def compute_f0(data, sample_rate, hop_size, mode='train'): + """ Extract f0 + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + frame_period = hop_size * 1000 / sample_rate + for sample in data: + assert 'sample_rate' in sample + assert 'speech' in sample + assert 'utt' in sample + assert 'text_token' in sample + waveform = sample['speech'] + _f0, t = pw.harvest(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period) + if sum(_f0 != 0) < 5: # this happens when the algorithm fails + _f0, t = pw.dio(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period) # if harvest fails, try dio + f0 = pw.stonemask(waveform.squeeze(dim=0).numpy().astype('double'), _f0, t, sample_rate) + f0 = F.interpolate(torch.from_numpy(f0).view(1, 1, -1), size=sample['speech_feat'].shape[0], mode='linear').view(-1) + sample['pitch_feat'] = f0 + yield sample + + +def parse_embedding(data, normalize, mode='train'): + """ Parse utt_embedding/spk_embedding + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + if 'utt_embedding' not in sample and 'spk_embedding' not in sample: + sample['speech_16k'] = torchaudio.transforms.Resample(orig_freq=sample['sample_rate'], new_freq=16000)(sample['speech']) + embedding = embedding_extractor.inference(sample['speech_16k']) + sample['spk_embedding'] = sample['utt_embedding'] = embedding + else: + sample['utt_embedding'] = torch.tensor(sample['utt_embedding'], dtype=torch.float32) + sample['spk_embedding'] = torch.tensor(sample['spk_embedding'], dtype=torch.float32) + if normalize: + sample['utt_embedding'] = F.normalize(sample['utt_embedding'], dim=0) + sample['spk_embedding'] = F.normalize(sample['spk_embedding'], dim=0) + yield sample + + +def tokenize(data, get_tokenizer, allowed_special, mode='train'): + """ Decode text to chars or BPE + Inplace operation + + Args: + data: Iterable[{key, wav, txt, sample_rate}] + + Returns: + Iterable[{key, wav, txt, tokens, label, sample_rate}] + """ + tokenizer = get_tokenizer() + for sample in data: + assert 'text' in sample + sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special) + if 'instruct' in sample: + sample['instruct_token'] = tokenizer.encode(sample['instruct'], allowed_special=allowed_special) + yield sample + + +def shuffle(data, shuffle_size=10000, mode='train'): + """ Local shuffle the data + + Args: + data: Iterable[{key, feat, label}] + shuffle_size: buffer size for shuffle + + Returns: + Iterable[{key, feat, label}] + """ + buf = [] + yield_size = int(shuffle_size / 2) + for sample in data: + buf.append(sample) + if len(buf) >= shuffle_size: + random.shuffle(buf) + for x in buf[:yield_size]: + yield x + buf = buf[yield_size:] + # The sample left over + random.shuffle(buf) + for x in buf: + yield x + + +def sort(data, sort_size=500, mode='train'): + """ Sort the data by feature length. + Sort is used after shuffle and before batch, so we can group + utts with similar lengths into a batch, and `sort_size` should + be less than `shuffle_size` + + Args: + data: Iterable[{key, feat, label}] + sort_size: buffer size for sort + + Returns: + Iterable[{key, feat, label}] + """ + + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= sort_size: + buf.sort(key=lambda x: x['speech_feat'].size(0)) + for x in buf: + yield x + buf = [] + # The sample left over + buf.sort(key=lambda x: x['speech_feat'].size(0)) + for x in buf: + yield x + + +def static_batch(data, batch_size=16): + """ Static batch the data by `batch_size` + + Args: + data: Iterable[{key, feat, label}] + batch_size: batch size + + Returns: + Iterable[List[{key, feat, label}]] + """ + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= batch_size: + yield buf + buf = [] + if len(buf) > 0: + yield buf + + +def dynamic_batch(data, max_frames_in_batch=12000, mode='train'): + """ Dynamic batch the data until the total frames in batch + reach `max_frames_in_batch` + + Args: + data: Iterable[{key, feat, label}] + max_frames_in_batch: max_frames in one batch + + Returns: + Iterable[List[{key, feat, label}]] + """ + buf = [] + longest_frames = 0 + for sample in data: + assert 'speech_feat' in sample + assert isinstance(sample['speech_feat'], torch.Tensor) + new_sample_frames = sample['speech_feat'].size(0) + longest_frames = max(longest_frames, new_sample_frames) + frames_after_padding = longest_frames * (len(buf) + 1) + if frames_after_padding > max_frames_in_batch: + yield buf + buf = [sample] + longest_frames = new_sample_frames + else: + buf.append(sample) + if len(buf) > 0: + yield buf + + +def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, mode='train'): + """ Wrapper for static/dynamic batch + """ + if batch_type == 'static': + return static_batch(data, batch_size) + elif batch_type == 'dynamic': + return dynamic_batch(data, max_frames_in_batch) + else: + logging.fatal('Unsupported batch type {}'.format(batch_type)) + + +def padding(data, use_spk_embedding, mode='train', gan=False, dpo=False): + """ Padding the data into training data + + Args: + data: Iterable[List[{key, feat, label}]] + + Returns: + Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)] + """ + for sample in data: + assert isinstance(sample, list) + order = torch.argsort(torch.tensor([x['speech'].size(1) for x in sample], dtype=torch.int32), descending=True) + batch = {} + batch['utts'] = [sample[i]['utt'] for i in order] + batch['text'] = [sample[i]['text'] for i in order] + text_token = [torch.tensor(sample[i]['text_token']) for i in order] + batch['text_token_len'] = torch.tensor([i.size(0) for i in text_token], dtype=torch.int32) + batch['text_token'] = pad_sequence(text_token, batch_first=True, padding_value=0) + speech_feat = [sample[i]['speech_feat'] for i in order] + batch['speech_feat_len'] = torch.tensor([i.size(0) for i in speech_feat], dtype=torch.int32) + batch['speech_feat'] = pad_sequence(speech_feat, batch_first=True, padding_value=0) + batch['utt_embedding'] = torch.stack([sample[i]['utt_embedding'] for i in order], dim=0) + batch['spk_embedding'] = torch.stack([sample[i]['spk_embedding'] for i in order], dim=0) + if torch.tensor(['instruct_token' in sample[i] for i in order]).all(): + instruct_token = [torch.tensor(sample[i]['instruct_token']) for i in order] + batch['instruct_token_len'] = torch.tensor([i.size(0) for i in instruct_token], dtype=torch.int32) + batch['instruct_token'] = pad_sequence(instruct_token, batch_first=True, padding_value=0) + if torch.tensor(['whisper_feat' in sample[i] for i in order]).all(): + whisper_feat = [sample[i]['whisper_feat'] for i in order] + batch['whisper_feat_len'] = torch.tensor([i.size(0) for i in whisper_feat], dtype=torch.int32) + batch['whisper_feat'] = pad_sequence(whisper_feat, batch_first=True, padding_value=0) + if torch.tensor(['speech_token' in sample[i] for i in order]).all(): + speech_token = [torch.tensor(sample[i]['speech_token']) for i in order] + batch['speech_token_len'] = torch.tensor([i.size(0) for i in speech_token], dtype=torch.int32) + batch['speech_token'] = pad_sequence(speech_token, batch_first=True, padding_value=0) + if gan is True: + # in gan train, we need speech/pitch_feat + speech = [sample[i]['speech'].squeeze(dim=0) for i in order] + batch['speech_len'] = torch.tensor([i.size(0) for i in speech], dtype=torch.int32) + batch['speech'] = pad_sequence(speech, batch_first=True, padding_value=0) + pitch_feat = [sample[i]['pitch_feat'] for i in order] + batch['pitch_feat_len'] = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32) + batch['pitch_feat'] = pad_sequence(pitch_feat, batch_first=True, padding_value=0) + if dpo is True: + reject_speech_token = [torch.tensor(sample[i]['reject_speech_token']) for i in order] + batch['reject_speech_token_len'] = torch.tensor([i.size(0) for i in reject_speech_token], dtype=torch.int32) + batch['reject_speech_token'] = pad_sequence(reject_speech_token, batch_first=True, padding_value=0) + if use_spk_embedding is True: + batch["embedding"] = batch["spk_embedding"] + else: + batch["embedding"] = batch["utt_embedding"] + yield batch diff --git a/src/voice/cosyvoice-src/cosyvoice/flow/DiT/dit.py b/src/voice/cosyvoice-src/cosyvoice/flow/DiT/dit.py new file mode 100644 index 0000000..0d637e4 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/flow/DiT/dit.py @@ -0,0 +1,176 @@ + +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations + +import torch +from torch import nn +import torch.nn.functional as F +from einops import repeat +from x_transformers.x_transformers import RotaryEmbedding +from cosyvoice.utils.mask import add_optional_chunk_mask +from cosyvoice.flow.DiT.modules import ( + TimestepEmbedding, + ConvNeXtV2Block, + CausalConvPositionEmbedding, + DiTBlock, + AdaLayerNormZero_Final, + precompute_freqs_cis, + get_pos_embed_indices, +) + + +# Text embedding + + +class TextEmbedding(nn.Module): + def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2): + super().__init__() + self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token + + if conv_layers > 0: + self.extra_modeling = True + self.precompute_max_pos = 4096 # ~44s of 24khz audio + self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False) + self.text_blocks = nn.Sequential( + *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)] + ) + else: + self.extra_modeling = False + + def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722 + batch, text_len = text.shape[0], text.shape[1] + text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx() + text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens + text = F.pad(text, (0, seq_len - text_len), value=0) + + if drop_text: # cfg for text + text = torch.zeros_like(text) + + text = self.text_embed(text) # b n -> b n d + + # possible extra modeling + if self.extra_modeling: + # sinus pos emb + batch_start = torch.zeros((batch,), dtype=torch.long) + pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos) + text_pos_embed = self.freqs_cis[pos_idx] + text = text + text_pos_embed + + # convnextv2 blocks + text = self.text_blocks(text) + + return text + + +# noised input audio and context mixing embedding + + +class InputEmbedding(nn.Module): + def __init__(self, mel_dim, text_dim, out_dim, spk_dim=None): + super().__init__() + spk_dim = 0 if spk_dim is None else spk_dim + self.spk_dim = spk_dim + self.proj = nn.Linear(mel_dim * 2 + text_dim + spk_dim, out_dim) + self.conv_pos_embed = CausalConvPositionEmbedding(dim=out_dim) + + def forward( + self, + x: float["b n d"], + cond: float["b n d"], + text_embed: float["b n d"], + spks: float["b d"], + ): + to_cat = [x, cond, text_embed] + if self.spk_dim > 0: + spks = repeat(spks, "b c -> b t c", t=x.shape[1]) + to_cat.append(spks) + + x = self.proj(torch.cat(to_cat, dim=-1)) + x = self.conv_pos_embed(x) + x + return x + + +# Transformer backbone using DiT blocks + + +class DiT(nn.Module): + def __init__( + self, + *, + dim, + depth=8, + heads=8, + dim_head=64, + dropout=0.1, + ff_mult=4, + mel_dim=80, + mu_dim=None, + long_skip_connection=False, + spk_dim=None, + out_channels=None, + static_chunk_size=50, + num_decoding_left_chunks=2 + ): + super().__init__() + + self.time_embed = TimestepEmbedding(dim) + if mu_dim is None: + mu_dim = mel_dim + self.input_embed = InputEmbedding(mel_dim, mu_dim, dim, spk_dim) + + self.rotary_embed = RotaryEmbedding(dim_head) + + self.dim = dim + self.depth = depth + + self.transformer_blocks = nn.ModuleList( + [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)] + ) + self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None + + self.norm_out = AdaLayerNormZero_Final(dim) # final modulation + self.proj_out = nn.Linear(dim, mel_dim) + self.out_channels = out_channels + self.static_chunk_size = static_chunk_size + self.num_decoding_left_chunks = num_decoding_left_chunks + + def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False): + x = x.transpose(1, 2) + mu = mu.transpose(1, 2) + cond = cond.transpose(1, 2) + spks = spks.unsqueeze(dim=1) + batch, seq_len = x.shape[0], x.shape[1] + if t.ndim == 0: + t = t.repeat(batch) + + # t: conditioning time, c: context (text + masked cond audio), x: noised input audio + t = self.time_embed(t) + x = self.input_embed(x, cond, mu, spks.squeeze(1)) + + rope = self.rotary_embed.forward_from_seq_len(seq_len) + + if self.long_skip_connection is not None: + residual = x + + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, self.static_chunk_size, -1).unsqueeze(dim=1) + else: + attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1).unsqueeze(dim=1) + + for block in self.transformer_blocks: + x = block(x, t, mask=attn_mask.bool(), rope=rope) + + if self.long_skip_connection is not None: + x = self.long_skip_connection(torch.cat((x, residual), dim=-1)) + + x = self.norm_out(x, t) + output = self.proj_out(x).transpose(1, 2) + return output diff --git a/src/voice/cosyvoice-src/cosyvoice/flow/DiT/modules.py b/src/voice/cosyvoice-src/cosyvoice/flow/DiT/modules.py new file mode 100644 index 0000000..be8caec --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/flow/DiT/modules.py @@ -0,0 +1,616 @@ + +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations +from typing import Optional +import math + +import torch +from torch import nn +import torch.nn.functional as F +import torchaudio + +from x_transformers.x_transformers import apply_rotary_pos_emb + + +# raw wav to mel spec +class MelSpec(nn.Module): + def __init__( + self, + filter_length=1024, + hop_length=256, + win_length=1024, + n_mel_channels=100, + target_sample_rate=24_000, + normalize=False, + power=1, + norm=None, + center=True, + ): + super().__init__() + self.n_mel_channels = n_mel_channels + + self.mel_stft = torchaudio.transforms.MelSpectrogram( + sample_rate=target_sample_rate, + n_fft=filter_length, + win_length=win_length, + hop_length=hop_length, + n_mels=n_mel_channels, + power=power, + center=center, + normalized=normalize, + norm=norm, + ) + + self.register_buffer("dummy", torch.tensor(0), persistent=False) + + def forward(self, inp): + if len(inp.shape) == 3: + inp = inp.squeeze(1) # 'b 1 nw -> b nw' + + assert len(inp.shape) == 2 + + if self.dummy.device != inp.device: + self.to(inp.device) + + mel = self.mel_stft(inp) + mel = mel.clamp(min=1e-5).log() + return mel + + +# sinusoidal position embedding + + +class SinusPositionEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x, scale=1000): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +# convolutional position embedding + + +class ConvPositionEmbedding(nn.Module): + def __init__(self, dim, kernel_size=31, groups=16): + super().__init__() + assert kernel_size % 2 != 0 + self.conv1d = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Mish(), + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Mish(), + ) + + def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722 + if mask is not None: + mask = mask[..., None] + x = x.masked_fill(~mask, 0.0) + + x = x.permute(0, 2, 1) + x = self.conv1d(x) + out = x.permute(0, 2, 1) + + if mask is not None: + out = out.masked_fill(~mask, 0.0) + + return out + + +class CausalConvPositionEmbedding(nn.Module): + def __init__(self, dim, kernel_size=31, groups=16): + super().__init__() + assert kernel_size % 2 != 0 + self.kernel_size = kernel_size + self.conv1 = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0), + nn.Mish(), + ) + self.conv2 = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0), + nn.Mish(), + ) + + def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722 + if mask is not None: + mask = mask[..., None] + x = x.masked_fill(~mask, 0.0) + + x = x.permute(0, 2, 1) + x = F.pad(x, (self.kernel_size - 1, 0, 0, 0)) + x = self.conv1(x) + x = F.pad(x, (self.kernel_size - 1, 0, 0, 0)) + x = self.conv2(x) + out = x.permute(0, 2, 1) + + if mask is not None: + out = out.masked_fill(~mask, 0.0) + + return out + + +# rotary positional embedding related + + +def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0): + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py + theta *= theta_rescale_factor ** (dim / (dim - 2)) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cos = torch.cos(freqs) # real part + freqs_sin = torch.sin(freqs) # imaginary part + return torch.cat([freqs_cos, freqs_sin], dim=-1) + + +def get_pos_embed_indices(start, length, max_pos, scale=1.0): + # length = length if isinstance(length, int) else length.max() + scale = scale * torch.ones_like(start, dtype=torch.float32) # in case scale is a scalar + pos = ( + start.unsqueeze(1) + + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long() + ) + # avoid extra long error. + pos = torch.where(pos < max_pos, pos, max_pos - 1) + return pos + + +# Global Response Normalization layer (Instance Normalization ?) + + +class GRN(nn.Module): + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, 1, dim)) + self.beta = nn.Parameter(torch.zeros(1, 1, dim)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=1, keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x + + +# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py +# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108 + + +class ConvNeXtV2Block(nn.Module): + def __init__( + self, + dim: int, + intermediate_dim: int, + dilation: int = 1, + ): + super().__init__() + padding = (dilation * (7 - 1)) // 2 + self.dwconv = nn.Conv1d( + dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation + ) # depthwise conv + self.norm = nn.LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.grn = GRN(intermediate_dim) + self.pwconv2 = nn.Linear(intermediate_dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + residual = x + x = x.transpose(1, 2) # b n d -> b d n + x = self.dwconv(x) + x = x.transpose(1, 2) # b d n -> b n d + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.grn(x) + x = self.pwconv2(x) + return residual + x + + +# AdaLayerNormZero +# return with modulated x for attn input, and params for later mlp modulation + + +class AdaLayerNormZero(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 6) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb=None): + emb = self.linear(self.silu(emb)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) + + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +# AdaLayerNormZero for final layer +# return only with modulated x for attn input, cuz no more mlp modulation + + +class AdaLayerNormZero_Final(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 2) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb): + emb = self.linear(self.silu(emb)) + scale, shift = torch.chunk(emb, 2, dim=1) + + x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] + return x + + +# FeedForward + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + activation = nn.GELU(approximate=approximate) + project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation) + self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)) + + def forward(self, x): + return self.ff(x) + + +# Attention with possible joint part +# modified from diffusers/src/diffusers/models/attention_processor.py + + +class Attention(nn.Module): + def __init__( + self, + processor: JointAttnProcessor | AttnProcessor, + dim: int, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + context_dim: Optional[int] = None, # if not None -> joint attention + context_pre_only=None, + ): + super().__init__() + + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + + self.processor = processor + + self.dim = dim + self.heads = heads + self.inner_dim = dim_head * heads + self.dropout = dropout + + self.context_dim = context_dim + self.context_pre_only = context_pre_only + + self.to_q = nn.Linear(dim, self.inner_dim) + self.to_k = nn.Linear(dim, self.inner_dim) + self.to_v = nn.Linear(dim, self.inner_dim) + + if self.context_dim is not None: + self.to_k_c = nn.Linear(context_dim, self.inner_dim) + self.to_v_c = nn.Linear(context_dim, self.inner_dim) + if self.context_pre_only is not None: + self.to_q_c = nn.Linear(context_dim, self.inner_dim) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(self.inner_dim, dim)) + self.to_out.append(nn.Dropout(dropout)) + + if self.context_pre_only is not None and not self.context_pre_only: + self.to_out_c = nn.Linear(self.inner_dim, dim) + + def forward( + self, + x: float["b n d"], # noised input x # noqa: F722 + c: float["b n d"] = None, # context c # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding for x + c_rope=None, # rotary position embedding for c + ) -> torch.Tensor: + if c is not None: + return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope) + else: + return self.processor(self, x, mask=mask, rope=rope) + + +# Attention processor + + +class AttnProcessor: + def __init__(self): + pass + + def __call__( + self, + attn: Attention, + x: float["b n d"], # noised input x # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding + ) -> torch.FloatTensor: + batch_size = x.shape[0] + + # `sample` projections. + query = attn.to_q(x) + key = attn.to_k(x) + value = attn.to_v(x) + + # apply rotary position embedding + if rope is not None: + freqs, xpos_scale = rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + + query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) + key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) + + # attention + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + if mask is not None: + attn_mask = mask + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) + else: + attn_mask = None + + x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False) + x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + x = x.to(query.dtype) + + # linear proj + x = attn.to_out[0](x) + # dropout + x = attn.to_out[1](x) + + if mask is not None: + if mask.dim() == 2: + mask = mask.unsqueeze(-1) + else: + mask = mask[:, 0, -1].unsqueeze(-1) + x = x.masked_fill(~mask, 0.0) + + return x + + +# Joint Attention processor for MM-DiT +# modified from diffusers/src/diffusers/models/attention_processor.py + + +class JointAttnProcessor: + def __init__(self): + pass + + def __call__( + self, + attn: Attention, + x: float["b n d"], # noised input x # noqa: F722 + c: float["b nt d"] = None, # context c, here text # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding for x + c_rope=None, # rotary position embedding for c + ) -> torch.FloatTensor: + residual = x + + batch_size = c.shape[0] + + # `sample` projections. + query = attn.to_q(x) + key = attn.to_k(x) + value = attn.to_v(x) + + # `context` projections. + c_query = attn.to_q_c(c) + c_key = attn.to_k_c(c) + c_value = attn.to_v_c(c) + + # apply rope for context and noised input independently + if rope is not None: + freqs, xpos_scale = rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) + key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) + if c_rope is not None: + freqs, xpos_scale = c_rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale) + c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale) + + # attention + query = torch.cat([query, c_query], dim=1) + key = torch.cat([key, c_key], dim=1) + value = torch.cat([value, c_value], dim=1) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + if mask is not None: + attn_mask = F.pad(mask, (0, c.shape[1]), value=True) # no mask for c (text) + attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) + else: + attn_mask = None + + x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False) + x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + x = x.to(query.dtype) + + # Split the attention outputs. + x, c = ( + x[:, : residual.shape[1]], + x[:, residual.shape[1]:], + ) + + # linear proj + x = attn.to_out[0](x) + # dropout + x = attn.to_out[1](x) + if not attn.context_pre_only: + c = attn.to_out_c(c) + + if mask is not None: + mask = mask.unsqueeze(-1) + x = x.masked_fill(~mask, 0.0) + # c = c.masked_fill(~mask, 0.) # no mask for c (text) + + return x, c + + +# DiT Block + + +class DiTBlock(nn.Module): + def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1): + super().__init__() + + self.attn_norm = AdaLayerNormZero(dim) + self.attn = Attention( + processor=AttnProcessor(), + dim=dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + ) + + self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + + def forward(self, x, t, mask=None, rope=None): # x: noised input, t: time embedding + # pre-norm & modulation for attention input + norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t) + + # attention + attn_output = self.attn(x=norm, mask=mask, rope=rope) + + # process attention output for input x + x = x + gate_msa.unsqueeze(1) * attn_output + + ff_norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ff_output = self.ff(ff_norm) + x = x + gate_mlp.unsqueeze(1) * ff_output + + return x + + +# MMDiT Block https://arxiv.org/abs/2403.03206 + + +class MMDiTBlock(nn.Module): + r""" + modified from diffusers/src/diffusers/models/attention.py + + notes. + _c: context related. text, cond, etc. (left part in sd3 fig2.b) + _x: noised input related. (right part) + context_pre_only: last layer only do prenorm + modulation cuz no more ffn + """ + + def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False): + super().__init__() + + self.context_pre_only = context_pre_only + + self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim) + self.attn_norm_x = AdaLayerNormZero(dim) + self.attn = Attention( + processor=JointAttnProcessor(), + dim=dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + context_dim=dim, + context_pre_only=context_pre_only, + ) + + if not context_pre_only: + self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + else: + self.ff_norm_c = None + self.ff_c = None + self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + + def forward(self, x, c, t, mask=None, rope=None, c_rope=None): # x: noised input, c: context, t: time embedding + # pre-norm & modulation for attention input + if self.context_pre_only: + norm_c = self.attn_norm_c(c, t) + else: + norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t) + norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t) + + # attention + x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope) + + # process attention output for context c + if self.context_pre_only: + c = None + else: # if not last layer + c = c + c_gate_msa.unsqueeze(1) * c_attn_output + + norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + c_ff_output = self.ff_c(norm_c) + c = c + c_gate_mlp.unsqueeze(1) * c_ff_output + + # process attention output for input x + x = x + x_gate_msa.unsqueeze(1) * x_attn_output + + norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None] + x_ff_output = self.ff_x(norm_x) + x = x + x_gate_mlp.unsqueeze(1) * x_ff_output + + return c, x + + +# time step conditioning embedding + + +class TimestepEmbedding(nn.Module): + def __init__(self, dim, freq_embed_dim=256): + super().__init__() + self.time_embed = SinusPositionEmbedding(freq_embed_dim) + self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + + def forward(self, timestep: float["b"]): # noqa: F821 + time_hidden = self.time_embed(timestep) + time_hidden = time_hidden.to(timestep.dtype) + time = self.time_mlp(time_hidden) # b d + return time diff --git a/src/voice/cosyvoice-src/cosyvoice/flow/decoder.py b/src/voice/cosyvoice-src/cosyvoice/flow/decoder.py new file mode 100644 index 0000000..97768a4 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/flow/decoder.py @@ -0,0 +1,494 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import pack, rearrange, repeat +from cosyvoice.utils.common import mask_to_bias +from cosyvoice.utils.mask import add_optional_chunk_mask +from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D +from matcha.models.components.transformer import BasicTransformerBlock + + +class Transpose(torch.nn.Module): + def __init__(self, dim0: int, dim1: int): + super().__init__() + self.dim0 = dim0 + self.dim1 = dim1 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = torch.transpose(x, self.dim0, self.dim1) + return x + + +class CausalConv1d(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None + ) -> None: + super(CausalConv1d, self).__init__(in_channels, out_channels, + kernel_size, stride, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert stride == 1 + self.causal_padding = kernel_size - 1 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.pad(x, (self.causal_padding, 0), value=0.0) + x = super(CausalConv1d, self).forward(x) + return x + + +class CausalBlock1D(Block1D): + def __init__(self, dim: int, dim_out: int): + super(CausalBlock1D, self).__init__(dim, dim_out) + self.block = torch.nn.Sequential( + CausalConv1d(dim, dim_out, 3), + Transpose(1, 2), + nn.LayerNorm(dim_out), + Transpose(1, 2), + nn.Mish(), + ) + + def forward(self, x: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + output = self.block(x * mask) + return output * mask + + +class CausalResnetBlock1D(ResnetBlock1D): + def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8): + super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups) + self.block1 = CausalBlock1D(dim, dim_out) + self.block2 = CausalBlock1D(dim_out, dim_out) + + +class ConditionalDecoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + ): + """ + This decoder requires an input with the same shape of the target. So, if your text content + is shorter or longer than the outputs, please re-sampling it before feeding to the decoder. + """ + super().__init__() + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for _ in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] * 2 + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + resnet = ResnetBlock1D( + dim=input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + self.final_block = Block1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + self.initialize_weights() + + def initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.GroupNorm): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + + t = self.time_embeddings(t).to(t.dtype) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + if cond is not None: + x = pack([x, cond], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c").contiguous() + attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c").contiguous() + attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + skip = hiddens.pop() + x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0] + x = resnet(x, mask_up, t) + x = rearrange(x, "b c t -> b t c").contiguous() + attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + x = upsample(x * mask_up) + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + return output * mask + + +class CausalConditionalDecoder(ConditionalDecoder): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + static_chunk_size=50, + num_decoding_left_chunks=2, + ): + """ + This decoder requires an input with the same shape of the target. So, if your text content + is shorter or longer than the outputs, please re-sampling it before feeding to the decoder. + """ + torch.nn.Module.__init__(self) + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + self.static_chunk_size = static_chunk_size + self.num_decoding_left_chunks = num_decoding_left_chunks + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3) + ) + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for _ in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] * 2 + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + resnet = CausalResnetBlock1D( + dim=input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else CausalConv1d(output_channel, output_channel, 3) + ) + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + self.final_block = CausalBlock1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + self.initialize_weights() + + def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + t = self.time_embeddings(t).to(t.dtype) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + if cond is not None: + x = pack([x, cond], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c").contiguous() + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1) + else: + attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c").contiguous() + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1) + else: + attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + skip = hiddens.pop() + x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0] + x = resnet(x, mask_up, t) + x = rearrange(x, "b c t -> b t c").contiguous() + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1) + else: + attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + x = upsample(x * mask_up) + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + return output * mask diff --git a/src/voice/cosyvoice-src/cosyvoice/flow/flow.py b/src/voice/cosyvoice-src/cosyvoice/flow/flow.py new file mode 100644 index 0000000..c255186 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/flow/flow.py @@ -0,0 +1,443 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os, logging +import random +from typing import Dict, Optional +import torch +import torch.nn as nn +from torch.nn import functional as F +from omegaconf import DictConfig +from cosyvoice.utils.mask import make_pad_mask +from cosyvoice.utils.onnx import SpeechTokenExtractor, online_feature, onnx_path + + +class MaskedDiffWithXvec(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 4096, + input_frame_rate: int = 50, + only_mask_loss: bool = True, + encoder: torch.nn.Module = None, + length_regulator: torch.nn.Module = None, + decoder: torch.nn.Module = None, + decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1, + 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine', + 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}), + 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64, + 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.decoder_conf = decoder_conf + self.vocab_size = vocab_size + self.output_type = output_type + self.input_frame_rate = input_frame_rate + logging.info(f"input frame rate={self.input_frame_rate}") + self.input_embedding = nn.Embedding(vocab_size, input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.encoder = encoder + self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size) + self.decoder = decoder + self.length_regulator = length_regulator + self.only_mask_loss = only_mask_loss + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + token = batch['speech_token'].to(device) + token_len = batch['speech_token_len'].to(device) + feat = batch['speech_feat'].to(device) + feat_len = batch['speech_feat_len'].to(device) + embedding = batch['embedding'].to(device) + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len) + h = self.encoder_proj(h) + h, h_lengths = self.length_regulator(h, feat_len) + + # get conditions + conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(feat_len)).to(h) + # NOTE this is unnecessary, feat/h already same shape + loss, _ = self.decoder.compute_loss( + feat.transpose(1, 2).contiguous(), + mask.unsqueeze(1), + h.transpose(1, 2).contiguous(), + embedding, + cond=conds + ) + return {'loss': loss} + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + flow_cache): + assert token.shape[0] == 1 + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat speech token and prompt speech token + token_len1, token_len2 = prompt_token.shape[1], token.shape[1] + token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len) + h = self.encoder_proj(h) + mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256) + h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate) + + # get conditions + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + feat, flow_cache = self.decoder( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=10, + prompt_len=mel_len1, + cache=flow_cache + ) + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat.float(), flow_cache + + +class CausalMaskedDiffWithXvec(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 4096, + input_frame_rate: int = 50, + only_mask_loss: bool = True, + token_mel_ratio: int = 2, + pre_lookahead_len: int = 3, + encoder: torch.nn.Module = None, + decoder: torch.nn.Module = None, + decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1, + 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine', + 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}), + 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64, + 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.decoder_conf = decoder_conf + self.vocab_size = vocab_size + self.output_type = output_type + self.input_frame_rate = input_frame_rate + logging.info(f"input frame rate={self.input_frame_rate}") + self.input_embedding = nn.Embedding(vocab_size, input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.encoder = encoder + self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size) + self.decoder = decoder + self.only_mask_loss = only_mask_loss + self.token_mel_ratio = token_mel_ratio + self.pre_lookahead_len = pre_lookahead_len + if online_feature is True: + self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v2.batch.onnx')) + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + if 'speech_token' not in batch: + token, token_len = self.speech_token_extractor.inference(batch['whisper_feat'], batch['whisper_feat_len'], device) + else: + token = batch['speech_token'].to(device) + token_len = batch['speech_token_len'].to(device) + feat = batch['speech_feat'].to(device) + feat_len = batch['speech_feat_len'].to(device) + embedding = batch['embedding'].to(device) + + # NOTE unified training, static_chunk_size > 0 or = 0 + streaming = True if random.random() < 0.5 else False + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len, streaming=streaming) + h = self.encoder_proj(h) + + # get conditions + conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h) + loss, _ = self.decoder.compute_loss( + feat.transpose(1, 2).contiguous(), + mask.unsqueeze(1), + h.transpose(1, 2).contiguous(), + embedding, + cond=conds, + streaming=streaming, + ) + return {'loss': loss} + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + streaming, + finalize): + assert token.shape[0] == 1 + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + if finalize is True: + h, h_lengths = self.encoder(token, token_len, streaming=streaming) + else: + token, context = token[:, :-self.pre_lookahead_len], token[:, -self.pre_lookahead_len:] + h, h_lengths = self.encoder(token, token_len, context=context, streaming=streaming) + mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] + h = self.encoder_proj(h) + + # get conditions + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + feat, _ = self.decoder( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=10, + streaming=streaming + ) + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat.float(), None + + +class CausalMaskedDiffWithDiT(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 4096, + input_frame_rate: int = 50, + only_mask_loss: bool = True, + token_mel_ratio: int = 2, + pre_lookahead_len: int = 3, + pre_lookahead_layer: torch.nn.Module = None, + decoder: torch.nn.Module = None, + decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1, + 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine', + 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}), + 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64, + 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.decoder_conf = decoder_conf + self.vocab_size = vocab_size + self.output_type = output_type + self.input_frame_rate = input_frame_rate + logging.info(f"input frame rate={self.input_frame_rate}") + self.input_embedding = nn.Embedding(vocab_size, input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.pre_lookahead_len = pre_lookahead_len + self.pre_lookahead_layer = pre_lookahead_layer + self.decoder = decoder + self.only_mask_loss = only_mask_loss + self.token_mel_ratio = token_mel_ratio + if online_feature is True: + self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v3.batch.onnx')) + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + if 'speech_token' not in batch: + token, token_len = self.speech_token_extractor.inference(batch['whisper_feat'], batch['whisper_feat_len'], device) + else: + token = batch['speech_token'].to(device) + token_len = batch['speech_token_len'].to(device) + feat = batch['speech_feat'].to(device) + feat_len = batch['speech_feat_len'].to(device) + embedding = batch['embedding'].to(device) + + # NOTE unified training, static_chunk_size > 0 or = 0 + streaming = True if random.random() < 0.5 else False + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h = self.pre_lookahead_layer(token) + h = h.repeat_interleave(self.token_mel_ratio, dim=1) + mask = mask.repeat_interleave(self.token_mel_ratio, dim=1).squeeze(dim=-1) + + # get conditions + conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] + conds = conds.transpose(1, 2) + + loss, _ = self.decoder.compute_loss( + feat.transpose(1, 2).contiguous(), + mask.unsqueeze(1), + h.transpose(1, 2).contiguous(), + embedding, + cond=conds, + streaming=streaming, + ) + return {'loss': loss} + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + streaming, + finalize): + assert token.shape[0] == 1 + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + if finalize is True: + h = self.pre_lookahead_layer(token) + else: + h = self.pre_lookahead_layer(token[:, :-self.pre_lookahead_len], context=token[:, -self.pre_lookahead_len:]) + h = h.repeat_interleave(self.token_mel_ratio, dim=1) + mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] + + # get conditions + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + feat, _ = self.decoder( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=10, + streaming=streaming + ) + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat.float(), None + + +if __name__ == '__main__': + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + from hyperpyyaml import load_hyperpyyaml + with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f: + configs = load_hyperpyyaml(f, overrides={'llm': None, 'hift': None}) + model = configs['flow'] + device = 'cuda' if torch.cuda.is_available() else 'cpu' + model.to(device) + model.eval() + max_len = 10 * model.decoder.estimator.static_chunk_size + chunk_size = model.decoder.estimator.static_chunk_size + context_size = model.pre_lookahead_layer.pre_lookahead_len + token = torch.randint(0, 6561, size=(1, max_len)).to(device) + token_len = torch.tensor([max_len]).to(device) + prompt_token = torch.randint(0, 6561, size=(1, chunk_size)).to(device) + prompt_token_len = torch.tensor([chunk_size]).to(device) + prompt_feat = torch.rand(1, chunk_size * 2, 80).to(device) + prompt_feat_len = torch.tensor([chunk_size * 2]).to(device) + prompt_embedding = torch.rand(1, 192).to(device) + pred_gt, _ = model.inference(token, token_len, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=True) + for i in range(0, max_len, chunk_size): + finalize = True if i + chunk_size + context_size >= max_len else False + pred_chunk, _ = model.inference(token[:, :i + chunk_size + context_size], torch.tensor([token[:, :i + chunk_size + context_size].shape[1]]).to(device), + prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=finalize) + pred_chunk = pred_chunk[:, :, i * model.token_mel_ratio:] + print((pred_gt[:, :, i * model.token_mel_ratio: i * model.token_mel_ratio + pred_chunk.shape[2]] - pred_chunk).abs().max().item()) diff --git a/src/voice/cosyvoice-src/cosyvoice/flow/flow_matching.py b/src/voice/cosyvoice-src/cosyvoice/flow/flow_matching.py new file mode 100644 index 0000000..d3beb9e --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/flow/flow_matching.py @@ -0,0 +1,227 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn.functional as F +from matcha.models.components.flow_matching import BASECFM +from cosyvoice.utils.common import set_all_random_seed + + +class ConditionalCFM(BASECFM): + def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None): + super().__init__( + n_feats=in_channels, + cfm_params=cfm_params, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + self.t_scheduler = cfm_params.t_scheduler + self.training_cfg_rate = cfm_params.training_cfg_rate + self.inference_cfg_rate = cfm_params.inference_cfg_rate + in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0) + # Just change the architecture of the estimator here + self.estimator = estimator + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, cache=torch.zeros(1, 80, 0, 2)): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + + z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature + cache_size = cache.shape[2] + # fix prompt and overlap part mu and z + if cache_size != 0: + z[:, :, :cache_size] = cache[:, :, :, 0] + mu[:, :, :cache_size] = cache[:, :, :, 1] + z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2) + mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2) + cache = torch.stack([z_cache, mu_cache], dim=-1) + + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) + if self.t_scheduler == 'cosine': + t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), cache + + def solve_euler(self, x, t_span, mu, mask, spks, cond, streaming=False): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + """ + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + t = t.unsqueeze(dim=0) + + # I am storing this because I can later plot it by putting a debugger here and saving it to a file + # Or in future might add like a return_all_steps flag + sol = [] + + # Do not use concat, it may cause memory format changed and trt infer with wrong results! + # NOTE when flow run in amp mode, x.dtype is float32, which cause nan in trt fp16 inference, so set dtype=spks.dtype + x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype) + mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=spks.dtype) + mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype) + t_in = torch.zeros([2], device=x.device, dtype=spks.dtype) + spks_in = torch.zeros([2, 80], device=x.device, dtype=spks.dtype) + cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype) + for step in range(1, len(t_span)): + # Classifier-Free Guidance inference introduced in VoiceBox + x_in[:] = x + mask_in[:] = mask + mu_in[0] = mu + t_in[:] = t.unsqueeze(0) + spks_in[0] = spks + cond_in[0] = cond + dphi_dt = self.forward_estimator( + x_in, mask_in, + mu_in, t_in, + spks_in, + cond_in, + streaming + ) + dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0) + dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt) + x = x + dt * dphi_dt + t = t + dt + sol.append(x) + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + return sol[-1].float() + + def forward_estimator(self, x, mask, mu, t, spks, cond, streaming=False): + if isinstance(self.estimator, torch.nn.Module): + return self.estimator(x, mask, mu, t, spks, cond, streaming=streaming) + else: + [estimator, stream], trt_engine = self.estimator.acquire_estimator() + # NOTE need to synchronize when switching stream + torch.cuda.current_stream().synchronize() + with stream: + estimator.set_input_shape('x', (2, 80, x.size(2))) + estimator.set_input_shape('mask', (2, 1, x.size(2))) + estimator.set_input_shape('mu', (2, 80, x.size(2))) + estimator.set_input_shape('t', (2,)) + estimator.set_input_shape('spks', (2, 80)) + estimator.set_input_shape('cond', (2, 80, x.size(2))) + data_ptrs = [x.contiguous().data_ptr(), + mask.contiguous().data_ptr(), + mu.contiguous().data_ptr(), + t.contiguous().data_ptr(), + spks.contiguous().data_ptr(), + cond.contiguous().data_ptr(), + x.data_ptr()] + for i, j in enumerate(data_ptrs): + estimator.set_tensor_address(trt_engine.get_tensor_name(i), j) + # run trt engine + assert estimator.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True + torch.cuda.current_stream().synchronize() + self.estimator.release_estimator(estimator, stream) + return x + + def compute_loss(self, x1, mask, mu, spks=None, cond=None, streaming=False): + """Computes diffusion loss + + Args: + x1 (torch.Tensor): Target + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): target mask + shape: (batch_size, 1, mel_timesteps) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + spks (torch.Tensor, optional): speaker embedding. Defaults to None. + shape: (batch_size, spk_emb_dim) + + Returns: + loss: conditional flow matching loss + y: conditional flow + shape: (batch_size, n_feats, mel_timesteps) + """ + b, _, t = mu.shape + + # random timestep + t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype) + + # sample noise p(x_0) + z = torch.randn_like(x1) + + y = (1 - (1 - self.sigma_min) * t) * z + t * x1 + u = x1 - (1 - self.sigma_min) * z + + # during training, we randomly drop condition to trade off mode coverage and sample fidelity + if self.training_cfg_rate > 0: + cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate + mu = mu * cfg_mask.view(-1, 1, 1) + spks = spks * cfg_mask.view(-1, 1) + cond = cond * cfg_mask.view(-1, 1, 1) + + pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond, streaming=streaming) + loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1]) + return loss, y + + +class CausalConditionalCFM(ConditionalCFM): + def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None): + super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator) + set_all_random_seed(0) + self.rand_noise = torch.randn([1, 80, 50 * 300]) + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, streaming=False): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + + z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature + # fix prompt and overlap part mu and z + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) + if self.t_scheduler == 'cosine': + t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, streaming=streaming), None diff --git a/src/voice/cosyvoice-src/cosyvoice/flow/length_regulator.py b/src/voice/cosyvoice-src/cosyvoice/flow/length_regulator.py new file mode 100644 index 0000000..e1b6c1b --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/flow/length_regulator.py @@ -0,0 +1,70 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple +import torch.nn as nn +import torch +from torch.nn import functional as F +from cosyvoice.utils.mask import make_pad_mask + + +class InterpolateRegulator(nn.Module): + def __init__( + self, + channels: int, + sampling_ratios: Tuple, + out_channels: int = None, + groups: int = 1, + ): + super().__init__() + self.sampling_ratios = sampling_ratios + out_channels = out_channels or channels + model = nn.ModuleList([]) + if len(sampling_ratios) > 0: + for _ in sampling_ratios: + module = nn.Conv1d(channels, channels, 3, 1, 1) + norm = nn.GroupNorm(groups, channels) + act = nn.Mish() + model.extend([module, norm, act]) + model.append( + nn.Conv1d(channels, out_channels, 1, 1) + ) + self.model = nn.Sequential(*model) + + def forward(self, x, ylens=None): + # x in (B, T, D) + mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1) + x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear') + out = self.model(x).transpose(1, 2).contiguous() + olens = ylens + return out * mask, olens + + def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50): + # in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel + # NOTE 20 corresponds to token_overlap_len in cosyvoice/cli/model.py + # x in (B, T, D) + if x2.shape[1] > 40: + x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear') + x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2, + mode='linear') + x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear') + x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2) + else: + x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear') + if x1.shape[1] != 0: + x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear') + x = torch.concat([x1, x2], dim=2) + else: + x = x2 + out = self.model(x).transpose(1, 2).contiguous() + return out, mel_len1 + mel_len2 diff --git a/src/voice/cosyvoice-src/cosyvoice/hifigan/discriminator.py b/src/voice/cosyvoice-src/cosyvoice/hifigan/discriminator.py new file mode 100644 index 0000000..bb8e85f --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/hifigan/discriminator.py @@ -0,0 +1,230 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from torch.nn.utils.parametrizations import weight_norm, spectral_norm +except ImportError: + from torch.nn.utils import weight_norm, spectral_norm +from typing import List, Optional, Tuple +from einops import rearrange +from torchaudio.transforms import Spectrogram + +LRELU_SLOPE = 0.1 + + +class MultipleDiscriminator(nn.Module): + def __init__( + self, mpd: nn.Module, mrd: nn.Module + ): + super().__init__() + self.mpd = mpd + self.mrd = mrd + + def forward(self, y: torch.Tensor, y_hat: torch.Tensor): + y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], [] + this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mpd(y.unsqueeze(dim=1), y_hat.unsqueeze(dim=1)) + y_d_rs += this_y_d_rs + y_d_gs += this_y_d_gs + fmap_rs += this_fmap_rs + fmap_gs += this_fmap_gs + this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mrd(y, y_hat) + y_d_rs += this_y_d_rs + y_d_gs += this_y_d_gs + fmap_rs += this_fmap_rs + fmap_gs += this_fmap_gs + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class MultiResolutionDiscriminator(nn.Module): + def __init__( + self, + fft_sizes: Tuple[int, ...] = (2048, 1024, 512), + num_embeddings: Optional[int] = None, + ): + """ + Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec. + Additionally, it allows incorporating conditional information with a learned embeddings table. + + Args: + fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512). + num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator. + Defaults to None. + """ + + super().__init__() + self.discriminators = nn.ModuleList( + [DiscriminatorR(window_length=w, num_embeddings=num_embeddings) for w in fft_sizes] + ) + + def forward( + self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None + ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]: + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for d in self.discriminators: + y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id) + y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorR(nn.Module): + def __init__( + self, + window_length: int, + num_embeddings: Optional[int] = None, + channels: int = 32, + hop_factor: float = 0.25, + bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)), + ): + super().__init__() + self.window_length = window_length + self.hop_factor = hop_factor + self.spec_fn = Spectrogram( + n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None + ) + n_fft = window_length // 2 + 1 + bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands] + self.bands = bands + convs = lambda: nn.ModuleList( + [ + weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))), + ] + ) + self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))]) + + if num_embeddings is not None: + self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels) + torch.nn.init.zeros_(self.emb.weight) + + self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1))) + + def spectrogram(self, x): + # Remove DC offset + x = x - x.mean(dim=-1, keepdims=True) + # Peak normalize the volume of input audio + x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9) + x = self.spec_fn(x) + x = torch.view_as_real(x) + x = rearrange(x, "b f t c -> b c t f") + # Split into bands + x_bands = [x[..., b[0]: b[1]] for b in self.bands] + return x_bands + + def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None): + x_bands = self.spectrogram(x) + fmap = [] + x = [] + for band, stack in zip(x_bands, self.band_convs): + for i, layer in enumerate(stack): + band = layer(band) + band = torch.nn.functional.leaky_relu(band, 0.1) + if i > 0: + fmap.append(band) + x.append(band) + x = torch.cat(x, dim=-1) + if cond_embedding_id is not None: + emb = self.emb(cond_embedding_id) + h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True) + else: + h = 0 + x = self.conv_post(x) + fmap.append(x) + x += h + + return x, fmap + + +class MultiResSpecDiscriminator(torch.nn.Module): + + def __init__(self, + fft_sizes=[1024, 2048, 512], + hop_sizes=[120, 240, 50], + win_lengths=[600, 1200, 240], + window="hann_window"): + + super(MultiResSpecDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), + SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), + SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for _, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def stft(x, fft_size, hop_size, win_length, window): + """Perform STFT and convert to magnitude spectrogram. + Args: + x (Tensor): Input signal tensor (B, T). + fft_size (int): FFT size. + hop_size (int): Hop size. + win_length (int): Window length. + window (str): Window function type. + Returns: + Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). + """ + x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True) + + # NOTE(kan-bayashi): clamp is needed to avoid nan or inf + return torch.abs(x_stft).transpose(2, 1) + + +class SpecDiscriminator(nn.Module): + """docstring for Discriminator.""" + + def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): + super(SpecDiscriminator, self).__init__() + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.fft_size = fft_size + self.shift_size = shift_size + self.win_length = win_length + self.window = getattr(torch, window)(win_length) + self.discriminators = nn.ModuleList([ + norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), + ]) + + self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) + + def forward(self, y): + + fmap = [] + y = y.squeeze(1) + y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.device)) + y = y.unsqueeze(1) + for _, d in enumerate(self.discriminators): + y = d(y) + y = F.leaky_relu(y, LRELU_SLOPE) + fmap.append(y) + + y = self.out(y) + fmap.append(y) + + return torch.flatten(y, 1, -1), fmap diff --git a/src/voice/cosyvoice-src/cosyvoice/hifigan/f0_predictor.py b/src/voice/cosyvoice-src/cosyvoice/hifigan/f0_predictor.py new file mode 100644 index 0000000..c896890 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/hifigan/f0_predictor.py @@ -0,0 +1,103 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +try: + from torch.nn.utils.parametrizations import weight_norm +except ImportError: + from torch.nn.utils import weight_norm +from cosyvoice.transformer.convolution import CausalConv1d + + +class ConvRNNF0Predictor(nn.Module): + def __init__(self, + num_class: int = 1, + in_channels: int = 80, + cond_channels: int = 512 + ): + super().__init__() + + self.num_class = num_class + self.condnet = nn.Sequential( + weight_norm( + nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + ) + self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.condnet(x) + x = x.transpose(1, 2) + return torch.abs(self.classifier(x).squeeze(-1)) + + +class CausalConvRNNF0Predictor(nn.Module): + def __init__(self, + num_class: int = 1, + in_channels: int = 80, + cond_channels: int = 512 + ): + super().__init__() + + self.num_class = num_class + self.condnet = nn.Sequential( + weight_norm( + CausalConv1d(in_channels, cond_channels, kernel_size=4, causal_type='right') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + ) + self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class) + + def forward(self, x: torch.Tensor, finalize: bool = True) -> torch.Tensor: + if finalize is True: + x = self.condnet[0](x) + else: + x = self.condnet[0](x[:, :, :-self.condnet[0].causal_padding], x[:, :, -self.condnet[0].causal_padding:]) + for i in range(1, len(self.condnet)): + x = self.condnet[i](x) + x = x.transpose(1, 2) + return torch.abs(self.classifier(x).squeeze(-1)) diff --git a/src/voice/cosyvoice-src/cosyvoice/hifigan/generator.py b/src/voice/cosyvoice-src/cosyvoice/hifigan/generator.py new file mode 100644 index 0000000..bbc2a21 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/hifigan/generator.py @@ -0,0 +1,746 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""HIFI-GAN""" + +from typing import Dict, Optional, List +import numpy as np +from scipy.signal import get_window +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Conv1d +from torch.nn import ConvTranspose1d +from torch.nn.utils import remove_weight_norm +try: + from torch.nn.utils.parametrizations import weight_norm +except ImportError: + from torch.nn.utils import weight_norm +from torch.distributions.uniform import Uniform +from cosyvoice.transformer.convolution import CausalConv1d, CausalConv1dDownSample, CausalConv1dUpsample +from cosyvoice.transformer.activation import Snake +from cosyvoice.utils.common import get_padding +from cosyvoice.utils.common import init_weights + + +"""hifigan based generator implementation. + +This code is modified from https://github.com/jik876/hifi-gan + ,https://github.com/kan-bayashi/ParallelWaveGAN and + https://github.com/NVIDIA/BigVGAN + +""" + + +class ResBlock(torch.nn.Module): + """Residual block module in HiFiGAN/BigVGAN.""" + def __init__( + self, + channels: int = 512, + kernel_size: int = 3, + dilations: List[int] = [1, 3, 5], + causal: bool = False, + ): + super(ResBlock, self).__init__() + self.causal = causal + self.convs1 = nn.ModuleList() + self.convs2 = nn.ModuleList() + + for dilation in dilations: + self.convs1.append( + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation, + padding=get_padding(kernel_size, dilation)) if causal is False else + CausalConv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation, + causal_type='left' + ) + ) + ) + self.convs2.append( + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1)) if causal is False else + CausalConv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + causal_type='left' + ) + ) + ) + self.convs1.apply(init_weights) + self.convs2.apply(init_weights) + self.activations1 = nn.ModuleList([ + Snake(channels, alpha_logscale=False) + for _ in range(len(self.convs1)) + ]) + self.activations2 = nn.ModuleList([ + Snake(channels, alpha_logscale=False) + for _ in range(len(self.convs2)) + ]) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + for idx in range(len(self.convs1)): + xt = self.activations1[idx](x) + xt = self.convs1[idx](xt) + xt = self.activations2[idx](xt) + xt = self.convs2[idx](xt) + x = xt + x + return x + + def remove_weight_norm(self): + for idx in range(len(self.convs1)): + remove_weight_norm(self.convs1[idx]) + remove_weight_norm(self.convs2[idx]) + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + @torch.no_grad() + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, dim=1, length) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + f0 = f0.transpose(1, 2) + F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device) + for i in range(self.harmonic_num + 1): + F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate + + theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1) + u_dist = Uniform(low=-np.pi, high=np.pi) + phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device) + phase_vec[:, 0, :] = 0 + + # generate sine waveforms + sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec) + + # generate uv signal + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves.transpose(1, 2), uv.transpose(1, 2), noise + + +class SineGen2(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, upsample_scale, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False, + causal=False): + super(SineGen2, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + self.upsample_scale = upsample_scale + self.causal = causal + if causal is True: + self.rand_ini = torch.rand(1, 9) + self.rand_ini[:, 0] = 0 + self.sine_waves = torch.rand(1, 300 * 24000, 9) + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + if self.training is False and self.causal is True: + rad_values[:, 0, :] = rad_values[:, 0, :] + self.rand_ini.to(rad_values.device) + else: + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), + scale_factor=1 / self.upsample_scale, + mode="linear").transpose(1, 2) + + phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi + phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, + scale_factor=self.upsample_scale, mode="nearest" if self.causal is True else 'linear').transpose(1, 2) + sines = torch.sin(phase) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + # fundamental component + fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) + + # generate sine waveforms + sine_waves = self._f02sine(fn) * self.sine_amp + + # generate uv signal + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + if self.training is False and self.causal is True: + noise = noise_amp * self.sine_waves[:, :sine_waves.shape[1]].to(sine_waves.device) + else: + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0, sinegen_type='1', causal=False): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + if sinegen_type == '1': + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod) + else: + self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num, sine_amp, add_noise_std, voiced_threshod, causal=causal) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + self.causal = causal + if causal is True: + self.uv = torch.rand(1, 300 * 24000, 1) + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + with torch.no_grad(): + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + if self.training is False and self.causal is True: + noise = self.uv[:, :uv.shape[1]] * self.sine_amp / 3 + else: + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +class HiFTGenerator(nn.Module): + """ + HiFTNet Generator: Neural Source Filter + ISTFTNet + https://arxiv.org/abs/2309.09493 + """ + def __init__( + self, + in_channels: int = 80, + base_channels: int = 512, + nb_harmonics: int = 8, + sampling_rate: int = 22050, + nsf_alpha: float = 0.1, + nsf_sigma: float = 0.003, + nsf_voiced_threshold: float = 10, + upsample_rates: List[int] = [8, 8], + upsample_kernel_sizes: List[int] = [16, 16], + istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4}, + resblock_kernel_sizes: List[int] = [3, 7, 11], + resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + source_resblock_kernel_sizes: List[int] = [7, 11], + source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]], + lrelu_slope: float = 0.1, + audio_limit: float = 0.99, + f0_predictor: torch.nn.Module = None, + ): + super(HiFTGenerator, self).__init__() + + self.out_channels = 1 + self.nb_harmonics = nb_harmonics + self.sampling_rate = sampling_rate + self.istft_params = istft_params + self.lrelu_slope = lrelu_slope + self.audio_limit = audio_limit + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + # NOTE in CosyVoice2, we use the original SineGen implementation + self.m_source = SourceModuleHnNSF( + sampling_rate=sampling_rate, + upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"], + harmonic_num=nb_harmonics, + sine_amp=nsf_alpha, + add_noise_std=nsf_sigma, + voiced_threshod=nsf_voiced_threshold, + sinegen_type='1' if self.sampling_rate == 22050 else '2', + causal=False) + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"]) + + self.conv_pre = weight_norm( + Conv1d(in_channels, base_channels, 7, 1, padding=3) + ) + + # Up + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + base_channels // (2**i), + base_channels // (2**(i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + # Down + self.source_downs = nn.ModuleList() + self.source_resblocks = nn.ModuleList() + downsample_rates = [1] + upsample_rates[::-1][:-1] + downsample_cum_rates = np.cumprod(downsample_rates) + for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)): + if u == 1: + self.source_downs.append( + Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1) + ) + else: + self.source_downs.append( + Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2)) + ) + + self.source_resblocks.append( + ResBlock(base_channels // (2 ** (i + 1)), k, d) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = base_channels // (2**(i + 1)) + for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): + self.resblocks.append(ResBlock(ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.reflection_pad = nn.ReflectionPad1d((1, 0)) + self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32)) + self.f0_predictor = f0_predictor + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + self.m_source.remove_weight_norm() + for l in self.source_downs: + remove_weight_norm(l) + for l in self.source_resblocks: + l.remove_weight_norm() + + def _stft(self, x): + spec = torch.stft( + x, + self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device), + return_complex=True) + spec = torch.view_as_real(spec) # [B, F, TT, 2] + return spec[..., 0], spec[..., 1] + + def _istft(self, magnitude, phase): + magnitude = torch.clip(magnitude, max=1e2) + real = magnitude * torch.cos(phase) + img = magnitude * torch.sin(phase) + inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"], + self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device)) + return inverse_transform + + def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) + s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1) + + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, self.lrelu_slope) + x = self.ups[i](x) + + if i == self.num_upsamples - 1: + x = self.reflection_pad(x) + + # fusion + si = self.source_downs[i](s_stft) + si = self.source_resblocks[i](si) + x = x + si + + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + x = F.leaky_relu(x) + x = self.conv_post(x) + magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :]) + phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy + + x = self._istft(magnitude, phase) + x = torch.clamp(x, -self.audio_limit, self.audio_limit) + return x + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + speech_feat = batch['speech_feat'].transpose(1, 2).to(device) + # mel->f0 + f0 = self.f0_predictor(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + # mel+source->speech + generated_speech = self.decode(x=speech_feat, s=s) + return generated_speech, f0 + + @torch.inference_mode() + def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + # mel->f0 + f0 = self.f0_predictor(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + # use cache_source to avoid glitch + if cache_source.shape[2] != 0: + s[:, :, :cache_source.shape[2]] = cache_source + generated_speech = self.decode(x=speech_feat, s=s) + return generated_speech, s + + +class CausalHiFTGenerator(HiFTGenerator): + """ + HiFTNet Generator: Neural Source Filter + ISTFTNet + https://arxiv.org/abs/2309.09493 + """ + def __init__( + self, + in_channels: int = 80, + base_channels: int = 512, + nb_harmonics: int = 8, + sampling_rate: int = 22050, + nsf_alpha: float = 0.1, + nsf_sigma: float = 0.003, + nsf_voiced_threshold: float = 10, + upsample_rates: List[int] = [8, 8], + upsample_kernel_sizes: List[int] = [16, 16], + istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4}, + resblock_kernel_sizes: List[int] = [3, 7, 11], + resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + source_resblock_kernel_sizes: List[int] = [7, 11], + source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]], + lrelu_slope: float = 0.1, + audio_limit: float = 0.99, + conv_pre_look_right: int = 4, + f0_predictor: torch.nn.Module = None, + ): + torch.nn.Module.__init__(self) + + self.out_channels = 1 + self.nb_harmonics = nb_harmonics + self.sampling_rate = sampling_rate + self.istft_params = istft_params + self.lrelu_slope = lrelu_slope + self.audio_limit = audio_limit + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + self.m_source = SourceModuleHnNSF( + sampling_rate=sampling_rate, + upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"], + harmonic_num=nb_harmonics, + sine_amp=nsf_alpha, + add_noise_std=nsf_sigma, + voiced_threshod=nsf_voiced_threshold, + sinegen_type='1' if self.sampling_rate == 22050 else '2', + causal=True) + self.upsample_rates = upsample_rates + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"]) + + self.conv_pre = weight_norm( + CausalConv1d(in_channels, base_channels, conv_pre_look_right + 1, 1, causal_type='right') + ) + + # Up + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + weight_norm( + CausalConv1dUpsample( + base_channels // (2**i), + base_channels // (2**(i + 1)), + k, + u, + ) + ) + ) + + # Down + self.source_downs = nn.ModuleList() + self.source_resblocks = nn.ModuleList() + downsample_rates = [1] + upsample_rates[::-1][:-1] + downsample_cum_rates = np.cumprod(downsample_rates) + for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)): + if u == 1: + self.source_downs.append( + CausalConv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1, causal_type='left') + ) + else: + self.source_downs.append( + CausalConv1dDownSample(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u) + ) + + self.source_resblocks.append( + ResBlock(base_channels // (2 ** (i + 1)), k, d, causal=True) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = base_channels // (2**(i + 1)) + for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): + self.resblocks.append(ResBlock(ch, k, d, causal=True)) + + self.conv_post = weight_norm(CausalConv1d(ch, istft_params["n_fft"] + 2, 7, 1, causal_type='left')) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.reflection_pad = nn.ReflectionPad1d((1, 0)) + self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32)) + self.conv_pre_look_right = conv_pre_look_right + self.f0_predictor = f0_predictor + + def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0), finalize: bool = True) -> torch.Tensor: + s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) + if finalize is True: + x = self.conv_pre(x) + else: + x = self.conv_pre(x[:, :, :-self.conv_pre_look_right], x[:, :, -self.conv_pre_look_right:]) + s_stft_real = s_stft_real[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)] + s_stft_imag = s_stft_imag[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)] + s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1) + + for i in range(self.num_upsamples): + x = F.leaky_relu(x, self.lrelu_slope) + x = self.ups[i](x) + + if i == self.num_upsamples - 1: + x = self.reflection_pad(x) + + # fusion + si = self.source_downs[i](s_stft) + si = self.source_resblocks[i](si) + x = x + si + + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + x = F.leaky_relu(x) + x = self.conv_post(x) + magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :]) + phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy + + x = self._istft(magnitude, phase) + if finalize is False: + x = x[:, :-int(np.prod(self.upsample_rates) * self.istft_params['hop_len'])] + x = torch.clamp(x, -self.audio_limit, self.audio_limit) + return x + + @torch.inference_mode() + def inference(self, speech_feat: torch.Tensor, finalize: bool = True) -> torch.Tensor: + # mel->f0 NOTE f0_predictor precision is crucial for causal inference, move self.f0_predictor to cpu if necessary + self.f0_predictor.to(torch.float64) + f0 = self.f0_predictor(speech_feat.to(torch.float64), finalize=finalize).to(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + if finalize is True: + generated_speech = self.decode(x=speech_feat, s=s, finalize=finalize) + else: + generated_speech = self.decode(x=speech_feat[:, :, :-self.f0_predictor.condnet[0].causal_padding], s=s, finalize=finalize) + return generated_speech, s + + +if __name__ == '__main__': + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + from hyperpyyaml import load_hyperpyyaml + with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f: + configs = load_hyperpyyaml(f, overrides={'llm': None, 'flow': None}) + model = configs['hift'] + device = 'cuda' if torch.cuda.is_available() else 'cpu' + model.to(device) + model.eval() + max_len, chunk_size, context_size = 300, 30, 8 + mel = torch.rand(1, 80, max_len).to(device) + pred_gt, _ = model.inference(mel) + for i in range(0, max_len, chunk_size): + finalize = True if i + chunk_size + context_size >= max_len else False + pred_chunk, _ = model.inference(mel[:, :, : i + chunk_size + context_size], finalize=finalize) + pred_chunk = pred_chunk[:, i * 480:] + print((pred_gt[:, i * 480:i * 480 + pred_chunk.shape[1]] - pred_chunk).abs().max().item()) diff --git a/src/voice/cosyvoice-src/cosyvoice/hifigan/hifigan.py b/src/voice/cosyvoice-src/cosyvoice/hifigan/hifigan.py new file mode 100644 index 0000000..046c2cf --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/hifigan/hifigan.py @@ -0,0 +1,67 @@ +from typing import Dict, Optional +import torch +import torch.nn as nn +import torch.nn.functional as F +from matcha.hifigan.models import feature_loss, generator_loss, discriminator_loss +from cosyvoice.utils.losses import tpr_loss, mel_loss + + +class HiFiGan(nn.Module): + def __init__(self, generator, discriminator, mel_spec_transform, + multi_mel_spectral_recon_loss_weight=45, feat_match_loss_weight=2.0, + tpr_loss_weight=1.0, tpr_loss_tau=0.04): + super(HiFiGan, self).__init__() + self.generator = generator + self.discriminator = discriminator + self.mel_spec_transform = mel_spec_transform + self.multi_mel_spectral_recon_loss_weight = multi_mel_spectral_recon_loss_weight + self.feat_match_loss_weight = feat_match_loss_weight + self.tpr_loss_weight = tpr_loss_weight + self.tpr_loss_tau = tpr_loss_tau + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + if batch['turn'] == 'generator': + return self.forward_generator(batch, device) + else: + return self.forward_discriminator(batch, device) + + def forward_generator(self, batch, device): + real_speech = batch['speech'].to(device) + pitch_feat = batch['pitch_feat'].to(device) + # 1. calculate generator outputs + generated_speech, generated_f0 = self.generator(batch, device) + # 2. calculate discriminator outputs + y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech) + # 3. calculate generator losses, feature loss, mel loss, tpr losses [Optional] + loss_gen, _ = generator_loss(y_d_gs) + loss_fm = feature_loss(fmap_rs, fmap_gs) + loss_mel = mel_loss(real_speech, generated_speech, self.mel_spec_transform) + if self.tpr_loss_weight != 0: + loss_tpr = tpr_loss(y_d_gs, y_d_rs, self.tpr_loss_tau) + else: + loss_tpr = torch.zeros(1).to(device) + loss_f0 = F.l1_loss(generated_f0, pitch_feat) + loss = loss_gen + self.feat_match_loss_weight * loss_fm + \ + self.multi_mel_spectral_recon_loss_weight * loss_mel + \ + self.tpr_loss_weight * loss_tpr + loss_f0 + return {'loss': loss, 'loss_gen': loss_gen, 'loss_fm': loss_fm, 'loss_mel': loss_mel, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0} + + def forward_discriminator(self, batch, device): + real_speech = batch['speech'].to(device) + # 1. calculate generator outputs + with torch.no_grad(): + generated_speech, generated_f0 = self.generator(batch, device) + # 2. calculate discriminator outputs + y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech.detach()) + # 3. calculate discriminator losses, tpr losses [Optional] + loss_disc, _, _ = discriminator_loss(y_d_rs, y_d_gs) + if self.tpr_loss_weight != 0: + loss_tpr = tpr_loss(y_d_rs, y_d_gs, self.tpr_loss_tau) + else: + loss_tpr = torch.zeros(1).to(device) + loss = loss_disc + self.tpr_loss_weight * loss_tpr + return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr} diff --git a/src/voice/cosyvoice-src/cosyvoice/llm/llm.py b/src/voice/cosyvoice-src/cosyvoice/llm/llm.py new file mode 100644 index 0000000..e8e81d9 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/llm/llm.py @@ -0,0 +1,706 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# 2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li, Qihua, Shengqiang Li) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os, queue +import random +import time +import threading +from typing import Dict, Optional, Callable, List, Generator +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +from transformers import Qwen2ForCausalLM +from torch.nn.utils.rnn import pad_sequence, unpad_sequence +from cosyvoice.utils.common import IGNORE_ID +from cosyvoice.transformer.label_smoothing_loss import LabelSmoothingLoss +from cosyvoice.utils.common import th_accuracy +from cosyvoice.utils.file_utils import logging +from cosyvoice.utils.mask import make_pad_mask +from cosyvoice.utils.onnx import SpeechTokenExtractor, online_feature, onnx_path + + +class TransformerLM(torch.nn.Module): + def __init__( + self, + text_encoder_input_size: int, + llm_input_size: int, + llm_output_size: int, + text_token_size: int, + speech_token_size: int, + text_encoder: torch.nn.Module, + llm: torch.nn.Module, + sampling: Callable, + length_normalized_loss: bool = True, + lsm_weight: float = 0.0, + spk_embed_dim: int = 192, + ): + super().__init__() + self.llm_input_size = llm_input_size + self.speech_token_size = speech_token_size + # 1. build text token inputs related modules + self.text_embedding = torch.nn.Embedding(text_token_size, text_encoder_input_size) + self.text_encoder = text_encoder + self.text_encoder_affine_layer = nn.Linear( + self.text_encoder.output_size(), + llm_input_size + ) + + # 2. build speech token language model related modules + self.sos = 0 + self.task_id = 1 + self.eos_token = self.speech_token_size + self.llm_embedding = torch.nn.Embedding(2, llm_input_size) + self.llm = llm + self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 1) + self.criterion_ce = LabelSmoothingLoss( + size=speech_token_size + 1, + padding_idx=IGNORE_ID, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + # 3. [Optional] build speech token related modules + self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size) + + # 4. sampling method + self.sampling = sampling + + def encode( + self, + text: torch.Tensor, + text_lengths: torch.Tensor, + ): + encoder_out, encoder_mask = self.text_encoder(text, text_lengths, decoding_chunk_size=1, num_decoding_left_chunks=-1) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + encoder_out = self.text_encoder_affine_layer(encoder_out) + return encoder_out, encoder_out_lens + + def pad_unpad_sequence(self, sos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len): + text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True) + speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) + lm_input = [torch.concat([sos_emb.squeeze(dim=0), embedding[i], text_token[i], task_id_emb.squeeze(dim=0), speech_token[i]], dim=0) + for i in range(len(text_token))] + lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32) + lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID) + return lm_input, lm_input_len + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + """ + Args: + text: (B, L, D) + text_lengths: (B,) + audio: (B, T, N) or (B, T) + audio_lengths: (B,) + """ + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + embedding = batch['embedding'].to(device) + + # 1. prepare llm_target + lm_target = [torch.tensor([IGNORE_ID] * (2 + text_token_len[i]) + speech_token[i, :speech_token_len[i]].tolist() + + [self.speech_token_size]) for i in range(text_token.size(0))] + lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID).to(device) + + # 1. encode text_token + text_token = self.text_embedding(text_token) + text_token, text_token_len = self.encode(text_token, text_token_len) + + # 2. embedding projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + embedding = embedding.unsqueeze(1) + + # 3. sos and task_id + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + + # 4. encode speech_token + speech_token = self.speech_embedding(speech_token) + + # 5. unpad and pad + lm_input, lm_input_len = self.pad_unpad_sequence(sos_emb, embedding, text_token, text_token_len, + task_id_emb, speech_token, speech_token_len) + + # 6. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + loss = self.criterion_ce(logits, lm_target) + acc = th_accuracy(logits.view(-1, self.speech_token_size + 1), lm_target, ignore_label=IGNORE_ID) + return {'loss': loss, 'acc': acc} + + def sampling_ids( + self, + weighted_scores: torch.Tensor, + decoded_tokens: List, + sampling: int, + ignore_eos: bool = True, + ): + if ignore_eos is True: + weighted_scores[self.speech_token_size] = -float('inf') + top_ids = self.sampling(weighted_scores, decoded_tokens, sampling) + return top_ids + + @torch.inference_mode() + def inference( + self, + text: torch.Tensor, + text_len: torch.Tensor, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + uuid: str = '', + ) -> Generator[torch.Tensor, None, None]: + device = text.device + text = torch.concat([prompt_text, text], dim=1) + text_len += prompt_text_len + text = self.text_embedding(text) + + # 1. encode text + text, text_len = self.encode(text, text_len) + + # 2. encode embedding + if embedding.shape[0] != 0: + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + embedding = embedding.unsqueeze(dim=1) + else: + embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device).to(text.dtype) + + # 3. concat llm_input + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) + lm_input = torch.concat([sos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1) + + # 4. cal min/max_length + min_len = int((text_len - prompt_text_len) * min_token_text_ratio) + max_len = int((text_len - prompt_text_len) * max_token_text_ratio) + + # 5. step by step decode + out_tokens = [] + offset = 0 + att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device) + for i in range(max_len): + y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1, + att_cache=att_cache, cnn_cache=cnn_cache, + att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), + device=lm_input.device)).to(torch.bool)) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False) + if top_ids == self.eos_token: + break + # in stream mode, yield token one by one + yield top_ids + out_tokens.append(top_ids) + offset += lm_input.size(1) + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + +class Qwen2Encoder(torch.nn.Module): + def __init__(self, pretrain_path): + super().__init__() + self.model = Qwen2ForCausalLM.from_pretrained(pretrain_path) + + def forward(self, xs: torch.Tensor, xs_lens: torch.Tensor): + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T) + outs = self.model( + inputs_embeds=xs, + attention_mask=masks, + output_hidden_states=True, + return_dict=True, + ) + return outs.hidden_states[-1], masks.unsqueeze(1) + + def forward_one_step(self, xs, masks, cache=None): + input_masks = masks[:, -1, :] + outs = self.model( + inputs_embeds=xs, + attention_mask=input_masks, + output_hidden_states=True, + return_dict=True, + use_cache=True, + past_key_values=cache, + ) + xs = outs.hidden_states[-1] + new_cache = outs.past_key_values + return xs, new_cache + + +class Qwen2LM(TransformerLM): + def __init__( + self, + llm_input_size: int, + llm_output_size: int, + speech_token_size: int, + llm: torch.nn.Module, + sampling: Callable, + length_normalized_loss: bool = True, + lsm_weight: float = 0.0, + mix_ratio: List[int] = [5, 15], + ): + torch.nn.Module.__init__(self) + self.llm_input_size = llm_input_size + self.llm_output_size = llm_output_size + self.speech_token_size = speech_token_size + # 2. build speech token language model related modules + self.sos = 0 + self.task_id = 1 + self.eos_token = speech_token_size + self.fill_token = speech_token_size + 2 + + self.llm_embedding = torch.nn.Embedding(2, llm_input_size) + self.llm = llm + self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 3) + self.criterion_ce = LabelSmoothingLoss( + size=speech_token_size + 3, + padding_idx=IGNORE_ID, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + # 3. [Optional] build speech token related modules + self.speech_embedding = torch.nn.Embedding(speech_token_size + 3, llm_input_size) + + # 4. sampling method + self.sampling = sampling + self.mix_ratio = mix_ratio + + # 5. vllm related + self.stop_token_ids = [speech_token_size + i for i in range(3)] + self.vllm_output_queue = {} + if online_feature is True: + self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v2.batch.onnx')) + + def prepare_lm_input_target(self, sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, speech_token, speech_token_emb, speech_token_len, instruct_token=None, instruct_token_emb=None, instruct_token_len=None): + lm_target, lm_input = [], [] + text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True) + speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) + text_token_emb = unpad_sequence(text_token_emb, text_token_len.cpu(), batch_first=True) + speech_token_emb = unpad_sequence(speech_token_emb, speech_token_len.cpu(), batch_first=True) + # NOTE add instruct_token in CosyVoice3 + if instruct_token is not None and instruct_token_emb is not None and instruct_token_len is not None: + instruct_token = unpad_sequence(instruct_token, instruct_token_len.cpu(), batch_first=True) + instruct_token_emb = unpad_sequence(instruct_token_emb, instruct_token_len.cpu(), batch_first=True) + else: + instruct_token = [torch.empty(0).to(text_token[0])] * len(text_token) + instruct_token_emb = [torch.empty(0, 896).to(text_token_emb[0])] * len(text_token) + instruct_token_len = torch.zeros(len(text_token)).to(text_token_len) + for i in range(len(text_token)): + # bistream sequence + if random.random() < 0.5 and speech_token_len[i] / text_token_len[i] > self.mix_ratio[1] / self.mix_ratio[0]: + this_lm_target, this_lm_input = [IGNORE_ID], [sos_emb.squeeze(dim=0)] + this_lm_target += [IGNORE_ID] * instruct_token_len[i] + this_lm_input.append(instruct_token_emb[i]) + for j in range(((text_token_len[i] + 1) / self.mix_ratio[0]).ceil().int().item()): + this_text_token = text_token[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]].tolist() + this_speech_token = speech_token[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]].tolist() + if len(this_text_token) == self.mix_ratio[0]: + assert len(this_speech_token) == self.mix_ratio[1] + this_lm_target += [IGNORE_ID] * (self.mix_ratio[0] - 1) + this_lm_target += this_speech_token + this_lm_target.append(self.fill_token) + this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]]) + this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]]) + else: + this_lm_target += [-1] * len(this_text_token) + this_lm_target += speech_token[i][j * self.mix_ratio[1]:].tolist() + this_lm_target.append(self.eos_token) + this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]:]) + this_lm_input.append(task_id_emb.squeeze(dim=0)) + this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]:]) + this_lm_target, this_lm_input = torch.tensor(this_lm_target), torch.concat(this_lm_input, dim=0) + # unistream sequence + else: + this_lm_target = torch.tensor([IGNORE_ID] * (1 + instruct_token_len[i] + text_token_len[i]) + speech_token[i].tolist() + [self.eos_token]) + this_lm_input = torch.concat([sos_emb.squeeze(dim=0), instruct_token_emb[i], text_token_emb[i], task_id_emb.squeeze(dim=0), speech_token_emb[i]], dim=0) + lm_target.append(this_lm_target) + lm_input.append(this_lm_input) + lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32) + lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID) + lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID) + return lm_target, lm_input, lm_input_len + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + """ + Args: + text: (B, L, D) + text_lengths: (B,) + audio: (B, T, N) or (B, T) + audio_lengths: (B,) + """ + # 1. encode text_token + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + text_token_emb = self.llm.model.model.embed_tokens(text_token) + + # 2. encode speech_token + if 'speech_token' not in batch: + speech_token, speech_token_len = self.speech_token_extractor.inference(batch['whisper_feat'], batch['whisper_feat_len'], device) + else: + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + speech_token_emb = self.speech_embedding(speech_token) + + # 3. sos and task_id + if self.__class__.__name__ == 'CosyVoice3LM': + sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1) + elif self.__class__.__name__ == 'Qwen2LM': + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + else: + raise ValueError + + # 4. prepare llm_input/target + if self.__class__.__name__ == 'CosyVoice3LM': + instruct_token = batch['instruct_token'].to(device) + instruct_token_len = batch['instruct_token_len'].to(device) + instruct_token_emb = self.llm.model.model.embed_tokens(instruct_token) + lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, + speech_token, speech_token_emb, speech_token_len, instruct_token, instruct_token_emb, instruct_token_len) + elif self.__class__.__name__ == 'Qwen2LM': + lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, + speech_token, speech_token_emb, speech_token_len) + else: + raise ValueError + lm_target = lm_target.to(device) + + # 4. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + loss = self.criterion_ce(logits, lm_target.to(device)) + acc = th_accuracy(logits.view(-1, self.llm_decoder.out_features), lm_target, ignore_label=IGNORE_ID) + return {'loss': loss, 'acc': acc} + + def forward_dpo( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + reject_speech_token = batch['reject_speech_token'].to(device) + reject_speech_token_len = batch['reject_speech_token_len'].to(device) + + # 1. encode text_token + text_token_emb = self.llm.model.model.embed_tokens(text_token) + + # 3. sos and task_id + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + + # 2. encode speech_token + speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) + reject_speech_token = unpad_sequence(reject_speech_token, reject_speech_token_len.cpu(), batch_first=True) + speech_token_combined = speech_token + reject_speech_token + speech_token_combined = pad_sequence(speech_token_combined, batch_first=True, padding_value=0) + speech_token_combined_len = torch.concat([speech_token_len, reject_speech_token_len], dim=0) + speech_token_combined_emb = self.speech_embedding(speech_token_combined) + + # 3. prepare llm_input/target + lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token.repeat(2, 1), text_token_emb.repeat(2, 1, 1), text_token_len.repeat(2), + task_id_emb, speech_token_combined, speech_token_combined_emb, speech_token_combined_len) + lm_target = lm_target.to(device) + + # 4. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + chosen_logits = logits[:text_token.shape[0]] + rejected_logits = logits[text_token.shape[0]:] + chosen_lm_target = lm_target[:text_token.shape[0]] + rejected_lm_target = lm_target[text_token.shape[0]:] + loss = self.criterion_ce(chosen_logits, chosen_lm_target.to(device)) + acc = th_accuracy(chosen_logits.view(-1, self.speech_token_size + 3), chosen_lm_target, ignore_label=IGNORE_ID) + + # 5. calculate dpo logits + chosen_lm_mask = chosen_lm_target == IGNORE_ID + rejected_lm_mask = rejected_lm_target == IGNORE_ID + chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1) + rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1) + chosen_logps = (chosen_logps * chosen_lm_mask).sum(dim=-1) / chosen_lm_mask.sum(dim=-1) + rejected_logps = (rejected_logps * rejected_lm_mask).sum(dim=-1) / rejected_lm_mask.sum(dim=-1) + return {'loss': loss, 'acc': acc, 'chosen_logps': chosen_logps, 'rejected_logps': rejected_logps} + + @torch.inference_mode() + def inference( + self, + text: torch.Tensor, + text_len: torch.Tensor, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + uuid: str = '', + ) -> Generator[torch.Tensor, None, None]: + device = text.device + text = torch.concat([prompt_text, text], dim=1) + text_len += prompt_text_len + text_emb = self.llm.model.model.embed_tokens(text) + if self.__class__.__name__ == 'CosyVoice3LM': + # NOTE temporary hardcode, 151646 is <|endofprompt|> token + assert 151646 in text, '<|endofprompt|> not detected in CosyVoice3 text or prompt_text, check your input!' + + # 3. concat llm_input + if self.__class__.__name__ == 'CosyVoice3LM': + sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1) + elif self.__class__.__name__ == 'Qwen2LM': + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + else: + raise ValueError + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text_emb.dtype).to(device) + lm_input = torch.concat([sos_emb, text_emb, task_id_emb, prompt_speech_token_emb], dim=1) + + # 4. cal min/max_length + min_len = int((text_len - prompt_text_len) * min_token_text_ratio) + max_len = int((text_len - prompt_text_len) * max_token_text_ratio) + + # 5. step by step decode + for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid): + yield token + + @torch.inference_mode() + def inference_wrapper(self, lm_input, sampling, min_len, max_len, uuid): + if hasattr(self, 'vllm'): + from vllm import SamplingParams, RequestOutput + sampling_params = SamplingParams(top_k=sampling, + stop_token_ids=self.stop_token_ids, + min_tokens=min_len, + max_tokens=max_len) + with self.lock: + self.vllm.add_request(uuid, {"prompt_embeds": lm_input.squeeze(0).to(torch.bfloat16).to(lm_input.device)}, sampling_params) + self.vllm_output_queue[uuid] = queue.Queue() + out_tokens = [] + while True: + with self.lock: + if self.vllm_output_queue[uuid].empty() is True: + request_outputs: List[RequestOutput] = self.vllm.step() + for request_output in request_outputs: + top_ids = list(request_output.outputs[0].token_ids)[-1] + self.vllm_output_queue[request_output.request_id].put(top_ids) + if self.vllm_output_queue[uuid].empty() is False: + top_ids = self.vllm_output_queue[uuid].get() + if top_ids in self.stop_token_ids: + break + # in stream mode, yield token one by one + yield top_ids + out_tokens.append(top_ids) + if len(out_tokens) == max_len: + break + time.sleep(0.001) + with self.lock: + self.vllm_output_queue.pop(uuid) + else: + out_tokens = [] + cache = None + for i in range(max_len): + y_pred, cache = self.llm.forward_one_step(lm_input, + masks=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool), + cache=cache) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False) + if top_ids in self.stop_token_ids: + break + # in stream mode, yield token one by one + yield top_ids + out_tokens.append(top_ids) + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + @torch.inference_mode() + def inference_bistream( + self, + text: Generator, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + ) -> Generator[torch.Tensor, None, None]: + + device = prompt_text.device + # 1. prepare input + if self.__class__.__name__ == 'CosyVoice3LM': + sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1) + elif self.__class__.__name__ == 'Qwen2LM': + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + else: + raise ValueError + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=prompt_text.dtype).to(device) + lm_input = torch.concat([sos_emb], dim=1) + + # 2. iterate text + out_tokens = [] + cache = None + # NOTE init prompt_text as text_cache as it is basically impossible prompt_speech_token/prompt_text < 15/5 + if self.__class__.__name__ == 'CosyVoice3LM': + # NOTE temporary hardcode, 151646 is <|endofprompt|> token + assert 151646 in prompt_text, '<|endofprompt|> not detected in CosyVoice3 prompt_text, check your input!' + eop_index = prompt_text.flatten().tolist().index(151646) + lm_input = torch.concat([lm_input, self.llm.model.model.embed_tokens(prompt_text[:, :eop_index + 1])], dim=1) + prompt_text = prompt_text[:, eop_index + 1:] + text_cache = self.llm.model.model.embed_tokens(prompt_text) + next_fill_index = (int(prompt_speech_token.shape[1] / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - prompt_speech_token.shape[1] + for this_text in text: + text_cache = torch.concat([text_cache, self.llm.model.model.embed_tokens(this_text)], dim=1) + # prompt_speech_token_emb not empty, try append to lm_input + while prompt_speech_token_emb.size(1) != 0: + if text_cache.size(1) >= self.mix_ratio[0]: + lm_input_text, lm_input_speech = text_cache[:, :self.mix_ratio[0]], prompt_speech_token_emb[:, :self.mix_ratio[1]] + logging.info('append {} text token {} speech token'.format(lm_input_text.size(1), lm_input_speech.size(1))) + lm_input = torch.concat([lm_input, lm_input_text, lm_input_speech], dim=1) + text_cache, prompt_speech_token_emb = text_cache[:, self.mix_ratio[0]:], prompt_speech_token_emb[:, self.mix_ratio[1]:] + else: + logging.info('not enough text token to decode, wait for more') + break + # no prompt_speech_token_emb remain, can decode some speech token + if prompt_speech_token_emb.size(1) == 0: + if (len(out_tokens) != 0 and out_tokens[-1] == self.fill_token) or (len(out_tokens) == 0 and lm_input.size(1) == 1): + logging.info('get fill token, need to append more text token') + if text_cache.size(1) >= self.mix_ratio[0]: + lm_input_text = text_cache[:, :self.mix_ratio[0]] + logging.info('append {} text token'.format(lm_input_text.size(1))) + if len(out_tokens) != 0 and out_tokens[-1] == self.fill_token: + lm_input = lm_input_text + else: + lm_input = torch.concat([lm_input, lm_input_text], dim=1) + text_cache = text_cache[:, self.mix_ratio[0]:] + else: + logging.info('not enough text token to decode, wait for more') + continue + while True: + seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2) + y_pred, cache = self.llm.forward_one_step(lm_input, + masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool), + cache=cache) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + if next_fill_index != -1 and len(out_tokens) == next_fill_index: + top_ids = self.fill_token + next_fill_index += (self.mix_ratio[1] + 1) + else: + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True) + if top_ids == self.fill_token: + next_fill_index = len(out_tokens) + self.mix_ratio[1] + 1 + logging.info('fill_token index {} next fill_token index {}'.format(len(out_tokens), next_fill_index)) + out_tokens.append(top_ids) + if top_ids >= self.speech_token_size: + if top_ids == self.fill_token: + break + else: + raise ValueError('should not get token {}'.format(top_ids)) + yield top_ids + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + # 3. final decode + lm_input = torch.concat([lm_input, text_cache, task_id_emb], dim=1) + logging.info('no more text token, decode until met eos') + while True: + seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2) + y_pred, cache = self.llm.forward_one_step(lm_input, + masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool), + cache=cache) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=False) + out_tokens.append(top_ids) + if top_ids >= self.speech_token_size: + if top_ids == self.eos_token: + break + else: + raise ValueError('should not get token {}'.format(top_ids)) + # in stream mode, yield token one by one + yield top_ids + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + +class CosyVoice3LM(Qwen2LM): + def __init__( + self, + llm_input_size: int, + llm_output_size: int, + speech_token_size: int, + llm: torch.nn.Module, + sampling: Callable, + length_normalized_loss: bool = True, + lsm_weight: float = 0.0, + mix_ratio: List[int] = [5, 15], + ): + torch.nn.Module.__init__(self) + self.llm_input_size = llm_input_size + self.llm_output_size = llm_output_size + self.speech_token_size = speech_token_size + # 2. build speech token language model related modules + self.sos = speech_token_size + 0 + self.eos_token = speech_token_size + 1 + self.task_id = speech_token_size + 2 + self.fill_token = speech_token_size + 3 + + self.llm = llm + self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 200, bias=False) + self.criterion_ce = LabelSmoothingLoss( + size=speech_token_size + 200, + padding_idx=IGNORE_ID, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + # 3. [Optional] build speech token related modules + self.speech_embedding = torch.nn.Embedding(speech_token_size + 200, llm_input_size) + + # 4. sampling method + self.sampling = sampling + self.mix_ratio = mix_ratio + + # 5. vllm related + self.stop_token_ids = [speech_token_size + i for i in range(200)] + self.vllm_output_queue = {} + if online_feature is True: + self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v3.batch.onnx')) \ No newline at end of file diff --git a/src/voice/cosyvoice-src/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken b/src/voice/cosyvoice-src/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken new file mode 100644 index 0000000..ac47fe6 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken @@ -0,0 +1,58836 @@ +IQ== 0 +Ig== 1 +Iw== 2 +JA== 3 +JQ== 4 +Jg== 5 +Jw== 6 +KA== 7 +KQ== 8 +Kg== 9 +Kw== 10 +LA== 11 +LQ== 12 +Lg== 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58815 +6b6Z 58816 +6b6a 58817 +6b6b 58818 +6b6c 58819 +6b6d 58820 +6b6f 58821 +6b6g 58822 +6b6i 58823 +77yB 58824 +77yI 58825 +77yJ 58826 +77yM 58827 +77yN 58828 +77ya 58829 +77yb 58830 +77yf 58831 +772Y 58832 +772Z 58833 +8KCx 58834 +8KCxgQ== 58835 diff --git a/src/voice/cosyvoice-src/cosyvoice/tokenizer/tokenizer.py b/src/voice/cosyvoice-src/cosyvoice/tokenizer/tokenizer.py new file mode 100644 index 0000000..6ecf4ae --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/tokenizer/tokenizer.py @@ -0,0 +1,327 @@ +import base64 +import os +from functools import lru_cache +from typing import Optional +import torch +from transformers import AutoTokenizer +from whisper.tokenizer import Tokenizer + +import tiktoken + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", + "yue": "cantonese", + "minnan": "minnan", + "wuyu": "wuyu", + "dialect": "dialect", + "zh/en": "zh/en", + "en/zh": "en/zh", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", + "mandarin": "zh", +} + +AUDIO_EVENT = { + "ASR": "ASR", + "AED": "AED", + "SER": "SER", + "Speech": "Speech", + "/Speech": "/Speech", + "BGM": "BGM", + "/BGM": "/BGM", + "Laughter": "Laughter", + "/Laughter": "/Laughter", + "Applause": "Applause", + "/Applause": "/Applause", +} + +EMOTION = { + "HAPPY": "HAPPY", + "SAD": "SAD", + "ANGRY": "ANGRY", + "NEUTRAL": "NEUTRAL", +} + +TTS_Vocal_Token = { + "TTS/B": "TTS/B", + "TTS/O": "TTS/O", + "TTS/Q": "TTS/Q", + "TTS/A": "TTS/A", + "TTS/CO": "TTS/CO", + "TTS/CL": "TTS/CL", + "TTS/H": "TTS/H", + **{f"TTS/SP{i:02d}": f"TTS/SP{i:02d}" for i in range(1, 14)} +} + + +@lru_cache(maxsize=None) +def get_encoding(name: str = "gpt2", num_languages: int = 99): + vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") + ranks = { + base64.b64decode(token): int(rank) + for token, rank in (line.split() for line in open(vocab_path) if line) + } + n_vocab = len(ranks) + special_tokens = {} + + specials = [ + "<|endoftext|>", + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]], + *[f"<|{audio_event}|>" for audio_event in list(AUDIO_EVENT.keys())], + *[f"<|{emotion}|>" for emotion in list(EMOTION.keys())], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + *[f"<|SPECIAL_TOKEN_{i}|>" for i in range(1, 31)], # register special tokens for ASR + *[f"<|{tts}|>" for tts in list(TTS_Vocal_Token.keys())], # register special tokens for TTS + *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], + ] + + for token in specials: + special_tokens[token] = n_vocab + n_vocab += 1 + + return tiktoken.Encoding( + name=os.path.basename(vocab_path), + explicit_n_vocab=n_vocab, + pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", + mergeable_ranks=ranks, + special_tokens=special_tokens, + ) + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + num_languages: int = 99, + language: Optional[str] = None, + task: Optional[str] = None, # Literal["transcribe", "translate", None] +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + encoding_name = "multilingual_zh_ja_yue_char_del" + language = language or "en" + task = task or "transcribe" + else: + encoding_name = "gpt2" + language = None + task = None + + encoding = get_encoding(name=encoding_name, num_languages=num_languages) + + return Tokenizer( + encoding=encoding, num_languages=num_languages, language=language, task=task + ) + + +class CosyVoice2Tokenizer(): + def __init__(self, token_path, skip_special_tokens=True): + super().__init__() + # NOTE: non-chat model, all these special tokens keep randomly initialized. + special_tokens = { + 'eos_token': '<|endoftext|>', + 'pad_token': '<|endoftext|>', + 'additional_special_tokens': [ + '<|im_start|>', '<|im_end|>', '<|endofprompt|>', + '[breath]', '', '', '[noise]', + '[laughter]', '[cough]', '[clucking]', '[accent]', + '[quick_breath]', + "", "", + "[hissing]", "[sigh]", "[vocalized-noise]", + "[lipsmack]", "[mn]" + ] + } + self.special_tokens = special_tokens + self.tokenizer = AutoTokenizer.from_pretrained(token_path) + self.tokenizer.add_special_tokens(special_tokens) + self.skip_special_tokens = skip_special_tokens + + def encode(self, text, **kwargs): + tokens = self.tokenizer([text], return_tensors="pt") + tokens = tokens["input_ids"][0].cpu().tolist() + return tokens + + def decode(self, tokens): + tokens = torch.tensor(tokens, dtype=torch.int64) + text = self.tokenizer.batch_decode([tokens], skip_special_tokens=self.skip_special_tokens)[0] + return text + + +class CosyVoice3Tokenizer(CosyVoice2Tokenizer): + def __init__(self, token_path, skip_special_tokens=True): + # NOTE: non-chat model, all these special tokens keep randomly initialized. + special_tokens = { + 'eos_token': '<|endoftext|>', + 'pad_token': '<|endoftext|>', + 'additional_special_tokens': [ + '<|im_start|>', '<|im_end|>', '<|endofprompt|>', + '[breath]', '', '', '[noise]', + '[laughter]', '[cough]', '[clucking]', '[accent]', + '[quick_breath]', + "", "", + "[hissing]", "[sigh]", "[vocalized-noise]", + "[lipsmack]", "[mn]", "<|endofsystem|>", + "[AA]", "[AA0]", "[AA1]", "[AA2]", "[AE]", "[AE0]", "[AE1]", "[AE2]", "[AH]", "[AH0]", "[AH1]", "[AH2]", + "[AO]", "[AO0]", "[AO1]", "[AO2]", "[AW]", "[AW0]", "[AW1]", "[AW2]", "[AY]", "[AY0]", "[AY1]", "[AY2]", + "[B]", "[CH]", "[D]", "[DH]", "[EH]", "[EH0]", "[EH1]", "[EH2]", "[ER]", "[ER0]", "[ER1]", "[ER2]", "[EY]", + "[EY0]", "[EY1]", "[EY2]", "[F]", "[G]", "[HH]", "[IH]", "[IH0]", "[IH1]", "[IH2]", "[IY]", "[IY0]", "[IY1]", + "[IY2]", "[JH]", "[K]", "[L]", "[M]", "[N]", "[NG]", "[OW]", "[OW0]", "[OW1]", "[OW2]", "[OY]", "[OY0]", + "[OY1]", "[OY2]", "[P]", "[R]", "[S]", "[SH]", "[T]", "[TH]", "[UH]", "[UH0]", "[UH1]", "[UH2]", "[UW]", + "[UW0]", "[UW1]", "[UW2]", "[V]", "[W]", "[Y]", "[Z]", "[ZH]", + "[a]", "[ai]", "[an]", "[ang]", "[ao]", "[b]", "[c]", "[ch]", "[d]", "[e]", "[ei]", "[en]", "[eng]", "[f]", + "[g]", "[h]", "[i]", "[ian]", "[in]", "[ing]", "[iu]", "[ià]", "[iàn]", "[iàng]", "[iào]", "[iá]", "[ián]", + "[iáng]", "[iáo]", "[iè]", "[ié]", "[iòng]", "[ióng]", "[iù]", "[iú]", "[iā]", "[iān]", "[iāng]", "[iāo]", + "[iē]", "[iě]", "[iōng]", "[iū]", "[iǎ]", "[iǎn]", "[iǎng]", "[iǎo]", "[iǒng]", "[iǔ]", "[j]", "[k]", "[l]", + "[m]", "[n]", "[o]", "[ong]", "[ou]", "[p]", "[q]", "[r]", "[s]", "[sh]", "[t]", "[u]", "[uang]", "[ue]", + "[un]", "[uo]", "[uà]", "[uài]", "[uàn]", "[uàng]", "[uá]", "[uái]", "[uán]", "[uáng]", "[uè]", "[ué]", "[uì]", + "[uí]", "[uò]", "[uó]", "[uā]", "[uāi]", "[uān]", "[uāng]", "[uē]", "[uě]", "[uī]", "[uō]", "[uǎ]", "[uǎi]", + "[uǎn]", "[uǎng]", "[uǐ]", "[uǒ]", "[vè]", "[w]", "[x]", "[y]", "[z]", "[zh]", "[à]", "[ài]", "[àn]", "[àng]", + "[ào]", "[á]", "[ái]", "[án]", "[áng]", "[áo]", "[è]", "[èi]", "[èn]", "[èng]", "[èr]", "[é]", "[éi]", "[én]", + "[éng]", "[ér]", "[ì]", "[ìn]", "[ìng]", "[í]", "[ín]", "[íng]", "[ò]", "[òng]", "[òu]", "[ó]", "[óng]", "[óu]", + "[ù]", "[ùn]", "[ú]", "[ún]", "[ā]", "[āi]", "[ān]", "[āng]", "[āo]", "[ē]", "[ēi]", "[ēn]", "[ēng]", "[ě]", + "[ěi]", "[ěn]", "[ěng]", "[ěr]", "[ī]", "[īn]", "[īng]", "[ō]", "[ōng]", "[ōu]", "[ū]", "[ūn]", "[ǎ]", "[ǎi]", + "[ǎn]", "[ǎng]", "[ǎo]", "[ǐ]", "[ǐn]", "[ǐng]", "[ǒ]", "[ǒng]", "[ǒu]", "[ǔ]", "[ǔn]", "[ǘ]", "[ǚ]", "[ǜ]" + ] + } + self.special_tokens = special_tokens + self.tokenizer = AutoTokenizer.from_pretrained(token_path) + self.tokenizer.add_special_tokens(special_tokens) + self.skip_special_tokens = skip_special_tokens + + +@lru_cache(maxsize=None) +def get_qwen_tokenizer( + token_path: str, + skip_special_tokens: bool, + version: str = 'cosyvoice2' +): + if version == 'cosyvoice2': + return CosyVoice2Tokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens) + elif version == 'cosyvoice3': + return CosyVoice3Tokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens) + else: + raise ValueError diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/__init__.py b/src/voice/cosyvoice-src/cosyvoice/transformer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/activation.py b/src/voice/cosyvoice-src/cosyvoice/transformer/activation.py new file mode 100644 index 0000000..8cea548 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/activation.py @@ -0,0 +1,84 @@ +# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe) +# 2020 Northwestern Polytechnical University (Pengcheng Guo) +# 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Swish() activation function for Conformer.""" + +import torch +from torch import nn, sin, pow +from torch.nn import Parameter + + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) + + +# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. +# LICENSE is in incl_licenses directory. +class Snake(nn.Module): + ''' + Implementation of a sine-based periodic activation function + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter + References: + - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha: trainable parameter + alpha is initialized to 1 by default, higher values = higher-frequency. + alpha will be trained along with the rest of your model. + ''' + super(Snake, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (xa) + ''' + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/attention.py b/src/voice/cosyvoice-src/cosyvoice/transformer/attention.py new file mode 100644 index 0000000..8c0c098 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/attention.py @@ -0,0 +1,330 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Multi-Head Attention layer definition.""" + +import math +from typing import Tuple + +import torch +from torch import nn + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be True? + # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the + # 1st chunk to ease the onnx export.] + # 2. pytorch training + if mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float('inf')) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0) # (batch, head, time1, time2) + # NOTE(xcsong): When will `if mask.size(2) > 0` be False? + # 1. onnx(16/-1, -1/-1, 16/0) + # 2. jit (16/-1, -1/-1, 16/0, 16/4) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = (x.transpose(1, 2).contiguous().view(n_batch, -1, + self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + CosyVoice. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate, key_bias) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used + if matrix_ac.shape != matrix_bd.shape: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/convolution.py b/src/voice/cosyvoice-src/cosyvoice/transformer/convolution.py new file mode 100644 index 0000000..edb32b2 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/convolution.py @@ -0,0 +1,258 @@ +# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""ConvolutionModule definition.""" + +from typing import Tuple + +import torch +from torch import nn +import torch.nn.functional as F + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__(self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + ) + + assert norm in ['batch_norm', 'layer_norm'] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = nn.LayerNorm(channels) + + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert (x.size(2) > self.lorder) + new_cache = x[:, :, -self.lorder:] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache + + +# NOTE(Xiang Lyu) causal conv module used in convolution-based vocoder +class CausalConv1d(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + causal_type: str = 'left', + device=None, + dtype=None + ) -> None: + super(CausalConv1d, self).__init__(in_channels, out_channels, + kernel_size, stride=1, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert stride == 1 + self.causal_padding = int((kernel_size * dilation - dilation) / 2) * 2 + (kernel_size + 1) % 2 + assert causal_type in ['left', 'right'] + self.causal_type = causal_type + + def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor]: + input_timestep = x.shape[2] + if cache.size(2) == 0: + cache = torch.zeros(x.shape[0], x.shape[1], self.causal_padding).to(x) + assert cache.size(2) == self.causal_padding + if self.causal_type == 'left': + x = torch.concat([cache, x], dim=2) + else: + x = torch.concat([x, cache], dim=2) + x = super(CausalConv1d, self).forward(x) + assert x.shape[2] == input_timestep + return x + + +class CausalConv1dDownSample(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None + ) -> None: + super(CausalConv1dDownSample, self).__init__(in_channels, out_channels, + kernel_size, stride, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert stride != 1 and dilation == 1 + assert kernel_size % stride == 0 + self.causal_padding = stride - 1 + + def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]: + if cache.size(2) == 0: + x = F.pad(x, (self.causal_padding, 0), value=0.0) + else: + assert cache.size(2) == self.causal_padding + x = torch.concat([cache, x], dim=2) + x = super(CausalConv1dDownSample, self).forward(x) + return x + + +class CausalConv1dUpsample(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None + ) -> None: + super(CausalConv1dUpsample, self).__init__(in_channels, out_channels, + kernel_size, 1, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert dilation == 1 + self.causal_padding = kernel_size - 1 + self.upsample = torch.nn.Upsample(scale_factor=stride, mode='nearest') + + def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]: + x = self.upsample(x) + input_timestep = x.shape[2] + if cache.size(2) == 0: + x = F.pad(x, (self.causal_padding, 0), value=0.0) + else: + assert cache.size(2) == self.causal_padding + x = torch.concat([cache, x], dim=2) + x = super(CausalConv1dUpsample, self).forward(x) + assert input_timestep == x.shape[2] + return x diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/decoder.py b/src/voice/cosyvoice-src/cosyvoice/transformer/decoder.py new file mode 100644 index 0000000..98f3a66 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/decoder.py @@ -0,0 +1,396 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Decoder definition.""" +from typing import Tuple, List, Optional + +import torch +import torch.utils.checkpoint as ckpt +import logging + +from cosyvoice.transformer.decoder_layer import DecoderLayer +from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward +from cosyvoice.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from cosyvoice.utils.mask import (subsequent_mask, make_pad_mask) + + +class TransformerDecoder(torch.nn.Module): + """Base class of Transfomer decoder module. + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the hidden units number of position-wise feedforward + num_blocks: the number of decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + src_attention: if false, encoder-decoder cross attention is not + applied, such as CIF model + key_bias: whether use bias in attention.linear_k, False for whisper models. + gradient_checkpointing: rerunning a forward-pass segment for each + checkpointed segment during backward. + tie_word_embedding: Tie or clone module weights depending of whether we are + using TorchScript or not + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + normalize_before: bool = True, + src_attention: bool = True, + key_bias: bool = True, + activation_type: str = "relu", + gradient_checkpointing: bool = False, + tie_word_embedding: bool = False, + ): + super().__init__() + attention_dim = encoder_output_size + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + + self.embed = torch.nn.Sequential( + torch.nn.Identity() if input_layer == "no_pos" else + torch.nn.Embedding(vocab_size, attention_dim), + COSYVOICE_EMB_CLASSES[input_layer](attention_dim, + positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5) + self.use_output_layer = use_output_layer + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = torch.nn.Identity() + self.num_blocks = num_blocks + self.decoders = torch.nn.ModuleList([ + DecoderLayer( + attention_dim, + COSYVOICE_ATTENTION_CLASSES["selfattn"]( + attention_heads, attention_dim, + self_attention_dropout_rate, key_bias), + COSYVOICE_ATTENTION_CLASSES["selfattn"]( + attention_heads, attention_dim, src_attention_dropout_rate, + key_bias) if src_attention else None, + PositionwiseFeedForward(attention_dim, linear_units, + dropout_rate, activation), + dropout_rate, + normalize_before, + ) for _ in range(self.num_blocks) + ]) + + self.gradient_checkpointing = gradient_checkpointing + self.tie_word_embedding = tie_word_embedding + + def forward( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + r_ys_in_pad: torch.Tensor = torch.empty(0), + reverse_weight: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward decoder. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoder memory mask, (batch, 1, maxlen_in) + ys_in_pad: padded input token ids, int64 (batch, maxlen_out) + ys_in_lens: input lengths of this batch (batch) + r_ys_in_pad: not used in transformer decoder, in order to unify api + with bidirectional decoder + reverse_weight: not used in transformer decoder, in order to unify + api with bidirectional decode + Returns: + (tuple): tuple containing: + x: decoded token score before softmax (batch, maxlen_out, + vocab_size) if use_output_layer is True, + torch.tensor(0.0), in order to unify api with bidirectional decoder + olens: (batch, ) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + tgt = ys_in_pad + maxlen = tgt.size(1) + # tgt_mask: (B, 1, L) + tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1) + tgt_mask = tgt_mask.to(tgt.device) + # m: (1, L, L) + m = subsequent_mask(tgt_mask.size(-1), + device=tgt_mask.device).unsqueeze(0) + # tgt_mask: (B, L, L) + tgt_mask = tgt_mask & m + x, _ = self.embed(tgt) + if self.gradient_checkpointing and self.training: + x = self.forward_layers_checkpointed(x, tgt_mask, memory, + memory_mask) + else: + x = self.forward_layers(x, tgt_mask, memory, memory_mask) + if self.normalize_before: + x = self.after_norm(x) + if self.use_output_layer: + x = self.output_layer(x) + olens = tgt_mask.sum(1) + return x, torch.tensor(0.0), olens + + def forward_layers(self, x: torch.Tensor, tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor) -> torch.Tensor: + for layer in self.decoders: + x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory, + memory_mask) + return x + + @torch.jit.unused + def forward_layers_checkpointed(self, x: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor) -> torch.Tensor: + for layer in self.decoders: + x, tgt_mask, memory, memory_mask = ckpt.checkpoint( + layer.__call__, x, tgt_mask, memory, memory_mask) + return x + + def forward_one_step( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + This is only used for decoding. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoded memory mask, (batch, 1, maxlen_in) + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + x, _ = self.embed(tgt) + new_cache = [] + for i, decoder in enumerate(self.decoders): + if cache is None: + c = None + else: + c = cache[i] + x, tgt_mask, memory, memory_mask = decoder(x, + tgt_mask, + memory, + memory_mask, + cache=c) + new_cache.append(x) + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.use_output_layer: + y = torch.log_softmax(self.output_layer(y), dim=-1) + return y, new_cache + + def tie_or_clone_weights(self, jit_mode: bool = True): + """Tie or clone module weights (between word_emb and output_layer) + depending of whether we are using TorchScript or not""" + if not self.use_output_layer: + return + if jit_mode: + logging.info("clone emb.weight to output.weight") + self.output_layer.weight = torch.nn.Parameter( + self.embed[0].weight.clone()) + else: + logging.info("tie emb.weight with output.weight") + self.output_layer.weight = self.embed[0].weight + + if getattr(self.output_layer, "bias", None) is not None: + self.output_layer.bias.data = torch.nn.functional.pad( + self.output_layer.bias.data, + ( + 0, + self.output_layer.weight.shape[0] - + self.output_layer.bias.shape[0], + ), + "constant", + 0, + ) + + +class BiTransformerDecoder(torch.nn.Module): + """Base class of Transfomer decoder module. + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the hidden units number of position-wise feedforward + num_blocks: the number of decoder blocks + r_num_blocks: the number of right to left decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + r_num_blocks: int = 0, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + normalize_before: bool = True, + key_bias: bool = True, + gradient_checkpointing: bool = False, + tie_word_embedding: bool = False, + ): + + super().__init__() + self.tie_word_embedding = tie_word_embedding + self.left_decoder = TransformerDecoder( + vocab_size, + encoder_output_size, + attention_heads, + linear_units, + num_blocks, + dropout_rate, + positional_dropout_rate, + self_attention_dropout_rate, + src_attention_dropout_rate, + input_layer, + use_output_layer, + normalize_before, + key_bias=key_bias, + gradient_checkpointing=gradient_checkpointing, + tie_word_embedding=tie_word_embedding) + + self.right_decoder = TransformerDecoder( + vocab_size, + encoder_output_size, + attention_heads, + linear_units, + r_num_blocks, + dropout_rate, + positional_dropout_rate, + self_attention_dropout_rate, + src_attention_dropout_rate, + input_layer, + use_output_layer, + normalize_before, + key_bias=key_bias, + gradient_checkpointing=gradient_checkpointing, + tie_word_embedding=tie_word_embedding) + + def forward( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + r_ys_in_pad: torch.Tensor, + reverse_weight: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward decoder. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoder memory mask, (batch, 1, maxlen_in) + ys_in_pad: padded input token ids, int64 (batch, maxlen_out) + ys_in_lens: input lengths of this batch (batch) + r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out), + used for right to left decoder + reverse_weight: used for right to left decoder + Returns: + (tuple): tuple containing: + x: decoded token score before softmax (batch, maxlen_out, + vocab_size) if use_output_layer is True, + r_x: x: decoded token score (right to left decoder) + before softmax (batch, maxlen_out, vocab_size) + if use_output_layer is True, + olens: (batch, ) + """ + l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad, + ys_in_lens) + r_x = torch.tensor(0.0) + if reverse_weight > 0.0: + r_x, _, olens = self.right_decoder(memory, memory_mask, + r_ys_in_pad, ys_in_lens) + return l_x, r_x, olens + + def forward_one_step( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + This is only used for decoding. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoded memory mask, (batch, 1, maxlen_in) + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + return self.left_decoder.forward_one_step(memory, memory_mask, tgt, + tgt_mask, cache) + + def tie_or_clone_weights(self, jit_mode: bool = True): + """Tie or clone module weights (between word_emb and output_layer) + depending of whether we are using TorchScript or not""" + self.left_decoder.tie_or_clone_weights(jit_mode) + self.right_decoder.tie_or_clone_weights(jit_mode) diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/decoder_layer.py b/src/voice/cosyvoice-src/cosyvoice/transformer/decoder_layer.py new file mode 100644 index 0000000..91c7c5d --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/decoder_layer.py @@ -0,0 +1,132 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Decoder self-attention layer definition.""" +from typing import Optional, Tuple + +import torch +from torch import nn + + +class DecoderLayer(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Inter-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + If `None` is passed, Inter-attention is not used, such as + CIF, GPT, and other decoder only model. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: to use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: nn.Module, + src_attn: Optional[nn.Module], + feed_forward: nn.Module, + dropout_rate: float, + normalize_before: bool = True, + ): + """Construct an DecoderLayer object.""" + super().__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = nn.LayerNorm(size, eps=1e-5) + self.norm2 = nn.LayerNorm(size, eps=1e-5) + self.norm3 = nn.LayerNorm(size, eps=1e-5) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + + def forward( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor, + cache: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor + (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory + (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask + (#batch, maxlen_in). + cache (torch.Tensor): cached tensors. + (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + + if cache is None: + tgt_q = tgt + tgt_q_mask = tgt_mask + else: + # compute only the last frame query keeping dim: max_time_out -> 1 + assert cache.shape == ( + tgt.shape[0], + tgt.shape[1] - 1, + self.size, + ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" + tgt_q = tgt[:, -1:, :] + residual = residual[:, -1:, :] + tgt_q_mask = tgt_mask[:, -1:, :] + + x = residual + self.dropout( + self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]) + if not self.normalize_before: + x = self.norm1(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.dropout( + self.src_attn(x, memory, memory, memory_mask)[0]) + if not self.normalize_before: + x = self.norm2(x) + + residual = x + if self.normalize_before: + x = self.norm3(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm3(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, tgt_mask, memory, memory_mask diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/embedding.py b/src/voice/cosyvoice-src/cosyvoice/transformer/embedding.py new file mode 100644 index 0000000..ba20d71 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/embedding.py @@ -0,0 +1,302 @@ +# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Positonal Encoding Module.""" + +import math +from typing import Tuple, Union + +import torch +import torch.nn.functional as F +import numpy as np + + +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + + :param int d_model: embedding dim + :param float dropout_rate: dropout rate + :param int max_len: maximum input length + + PE(pos, 2i) = sin(pos/(10000^(2i/dmodel))) + PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel))) + """ + + def __init__(self, + d_model: int, + dropout_rate: float, + max_len: int = 5000, + reverse: bool = False): + """Construct an PositionalEncoding object.""" + super().__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.max_len = max_len + + self.pe = torch.zeros(self.max_len, self.d_model) + position = torch.arange(0, self.max_len, + dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) * + -(math.log(10000.0) / self.d_model)) + self.pe[:, 0::2] = torch.sin(position * div_term) + self.pe[:, 1::2] = torch.cos(position * div_term) + self.pe = self.pe.unsqueeze(0) + + def forward(self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input. Its shape is (batch, time, ...) + offset (int, torch.tensor): position offset + + Returns: + torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) + torch.Tensor: for compatibility to RelPositionalEncoding + """ + + self.pe = self.pe.to(x.device) + pos_emb = self.position_encoding(offset, x.size(1), False) + x = x * self.xscale + pos_emb + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int, + apply_dropout: bool = True) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + # How to subscript a Union type: + # https://github.com/pytorch/pytorch/issues/69434 + if isinstance(offset, int): + assert offset + size <= self.max_len + pos_emb = self.pe[:, offset:offset + size] + elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar + assert offset + size <= self.max_len + pos_emb = self.pe[:, offset:offset + size] + else: # for batched streaming decoding on GPU + assert torch.max(offset) + size <= self.max_len + index = offset.unsqueeze(1) + \ + torch.arange(0, size).to(offset.device) # B X T + flag = index > 0 + # remove negative offset + index = index * flag + pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model + + if apply_dropout: + pos_emb = self.dropout(pos_emb) + return pos_emb + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward(self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.pe = self.pe.to(x.device) + x = x * self.xscale + pos_emb = self.position_encoding(offset, x.size(1), False) + return self.dropout(x), self.dropout(pos_emb) + + +class WhisperPositionalEncoding(PositionalEncoding): + """ Sinusoids position encoding used in openai-whisper.encoder + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 1500): + super().__init__(d_model, dropout_rate, max_len) + self.xscale = 1.0 + log_timescale_increment = np.log(10000) / (d_model // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * + torch.arange(d_model // 2)) + scaled_time = torch.arange(max_len)[:, np.newaxis] * \ + inv_timescales[np.newaxis, :] + pe = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + delattr(self, "pe") + self.register_buffer("pe", pe.unsqueeze(0)) + + +class LearnablePositionalEncoding(PositionalEncoding): + """ Learnable position encoding used in openai-whisper.decoder + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 448): + super().__init__(d_model, dropout_rate, max_len) + # NOTE(xcsong): overwrite self.pe & self.xscale + self.pe = torch.nn.Parameter(torch.empty(1, max_len, d_model)) + self.xscale = 1.0 + + +class NoPositionalEncoding(torch.nn.Module): + """ No position encoding + """ + + def __init__(self, d_model: int, dropout_rate: float): + super().__init__() + self.d_model = d_model + self.dropout = torch.nn.Dropout(p=dropout_rate) + + def forward(self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: + """ Just return zero vector for interface compatibility + """ + pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device) + return self.dropout(x), pos_emb + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return torch.zeros(1, size, self.d_model) + + +class EspnetRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Construct an PositionalEncoding object.""" + super(EspnetRelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: torch.Tensor): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.position_encoding(size=x.size(1), offset=offset) + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + # How to subscript a Union type: + # https://github.com/pytorch/pytorch/issues/69434 + if isinstance(offset, int): + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset, + ] + elif isinstance(offset, torch.Tensor): + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset, + ] + return pos_emb diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/encoder.py b/src/voice/cosyvoice-src/cosyvoice/transformer/encoder.py new file mode 100644 index 0000000..c5709d0 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/encoder.py @@ -0,0 +1,474 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder definition.""" +from typing import Tuple + +import torch +import torch.utils.checkpoint as ckpt + +from cosyvoice.transformer.convolution import ConvolutionModule +from cosyvoice.transformer.encoder_layer import TransformerEncoderLayer +from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer +from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward +from cosyvoice.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_SUBSAMPLE_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from cosyvoice.utils.mask import make_pad_mask +from cosyvoice.utils.mask import add_optional_chunk_mask + + +class BaseEncoder(torch.nn.Module): + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "abs_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + gradient_checkpointing: bool = False, + ): + """ + Args: + input_size (int): input dim + output_size (int): dimension of attention + attention_heads (int): the number of heads of multi head attention + linear_units (int): the hidden units number of position-wise feed + forward + num_blocks (int): the number of decoder blocks + dropout_rate (float): dropout rate + attention_dropout_rate (float): dropout rate in attention + positional_dropout_rate (float): dropout rate after adding + positional encoding + input_layer (str): input layer type. + optional [linear, conv2d, conv2d6, conv2d8] + pos_enc_layer_type (str): Encoder positional encoding layer type. + opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] + normalize_before (bool): + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + static_chunk_size (int): chunk size for static chunk training and + decoding + use_dynamic_chunk (bool): whether use dynamic chunk size for + training or not, You can only use fixed chunk(chunk_size > 0) + or dyanmic chunk size(use_dynamic_chunk = True) + global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module + use_dynamic_left_chunk (bool): whether use dynamic left chunk in + dynamic chunk training + key_bias: whether use bias in attention.linear_k, False for whisper models. + gradient_checkpointing: rerunning a forward-pass segment for each + checkpointed segment during backward. + """ + super().__init__() + self._output_size = output_size + + self.global_cmvn = global_cmvn + self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, + positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + self.gradient_checkpointing = gradient_checkpointing + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks) + if self.gradient_checkpointing and self.training: + xs = self.forward_layers_checkpointed(xs, chunk_masks, pos_emb, + mask_pad) + else: + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + @torch.jit.unused + def forward_layers_checkpointed(self, xs: torch.Tensor, + chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = ckpt.checkpoint(layer.__call__, xs, + chunk_masks, pos_emb, + mask_pad) + return xs + + @torch.jit.export + def forward_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ Forward just one chunk + + Args: + xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (int): current offset in encoder output time stamp + required_cache_size (int): cache size required for next chunk + compuation + >=0: actual cache size + <0: means all history cache is required + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (elayers, b=1, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + + Returns: + torch.Tensor: output of current input xs, + with shape (b=1, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + dynamic shape (elayers, head, ?, d_k * 2) + depending on required_cache_size. + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + + """ + assert xs.size(0) == 1 + # tmp_masks is just for interface compatibility + tmp_masks = torch.ones(1, + xs.size(1), + device=xs.device, + dtype=torch.bool) + tmp_masks = tmp_masks.unsqueeze(1) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) + xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) + # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) + elayers, cache_t1 = att_cache.size(0), att_cache.size(2) + chunk_size = xs.size(1) + attention_key_size = cache_t1 + chunk_size + pos_emb = self.embed.position_encoding(offset=offset - cache_t1, + size=attention_key_size) + if required_cache_size < 0: + next_cache_start = 0 + elif required_cache_size == 0: + next_cache_start = attention_key_size + else: + next_cache_start = max(attention_key_size - required_cache_size, 0) + r_att_cache = [] + r_cnn_cache = [] + for i, layer in enumerate(self.encoders): + # NOTE(xcsong): Before layer.forward + # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), + # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache, + cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache) + # NOTE(xcsong): After layer.forward + # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), + # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) + r_att_cache.append(new_att_cache[:, :, next_cache_start:, :]) + r_cnn_cache.append(new_cnn_cache.unsqueeze(0)) + if self.normalize_before: + xs = self.after_norm(xs) + + # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), + # ? may be larger than cache_t1, it depends on required_cache_size + r_att_cache = torch.cat(r_att_cache, dim=0) + # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) + r_cnn_cache = torch.cat(r_cnn_cache, dim=0) + + return (xs, r_att_cache, r_cnn_cache) + + @torch.jit.unused + def forward_chunk_by_chunk( + self, + xs: torch.Tensor, + decoding_chunk_size: int, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ Forward input chunk by chunk with chunk_size like a streaming + fashion + + Here we should pay special attention to computation cache in the + streaming style forward chunk by chunk. Three things should be taken + into account for computation in the current network: + 1. transformer/conformer encoder layers output cache + 2. convolution in conformer + 3. convolution in subsampling + + However, we don't implement subsampling cache for: + 1. We can control subsampling module to output the right result by + overlapping input instead of cache left context, even though it + wastes some computation, but subsampling only takes a very + small fraction of computation in the whole model. + 2. Typically, there are several covolution layers with subsampling + in subsampling module, it is tricky and complicated to do cache + with different convolution layers with different subsampling + rate. + 3. Currently, nn.Sequential is used to stack all the convolution + layers in subsampling, we need to rewrite it to make it work + with cache, which is not preferred. + Args: + xs (torch.Tensor): (1, max_len, dim) + chunk_size (int): decoding chunk size + """ + assert decoding_chunk_size > 0 + # The model is trained by static or dynamic chunk + assert self.static_chunk_size > 0 or self.use_dynamic_chunk + subsampling = self.embed.subsampling_rate + context = self.embed.right_context + 1 # Add current frame + stride = subsampling * decoding_chunk_size + decoding_window = (decoding_chunk_size - 1) * subsampling + context + num_frames = xs.size(1) + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + outputs = [] + offset = 0 + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + + # Feed forward overlap input step by step + for cur in range(0, num_frames - context + 1, stride): + end = min(cur + decoding_window, num_frames) + chunk_xs = xs[:, cur:end, :] + (y, att_cache, + cnn_cache) = self.forward_chunk(chunk_xs, offset, + required_cache_size, att_cache, + cnn_cache) + outputs.append(y) + offset += y.size(1) + ys = torch.cat(outputs, 1) + masks = torch.ones((1, 1, ys.size(1)), + device=ys.device, + dtype=torch.bool) + return ys, masks + + +class TransformerEncoder(BaseEncoder): + """Transformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "abs_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + key_bias: bool = True, + selfattention_layer_type: str = "selfattn", + activation_type: str = "relu", + gradient_checkpointing: bool = False, + ): + """ Construct TransformerEncoder + + See Encoder for the meaning of each parameter. + """ + super().__init__(input_size, output_size, attention_heads, + linear_units, num_blocks, dropout_rate, + positional_dropout_rate, attention_dropout_rate, + input_layer, pos_enc_layer_type, normalize_before, + static_chunk_size, use_dynamic_chunk, global_cmvn, + use_dynamic_left_chunk, gradient_checkpointing) + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + self.encoders = torch.nn.ModuleList([ + TransformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](attention_heads, + output_size, + attention_dropout_rate, + key_bias), + PositionwiseFeedForward(output_size, linear_units, + dropout_rate, activation), + dropout_rate, normalize_before) for _ in range(num_blocks) + ]) + + +class ConformerEncoder(BaseEncoder): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "rel_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool = True, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + gradient_checkpointing: bool = False, + ): + """Construct ConformerEncoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + positionwise_conv_kernel_size (int): Kernel size of positionwise + conv1d layer. + macaron_style (bool): Whether to use macaron style for + positionwise layer. + selfattention_layer_type (str): Encoder attention layer type, + the parameter has no effect now, it's just for configure + compatibility. + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + super().__init__(input_size, output_size, attention_heads, + linear_units, num_blocks, dropout_rate, + positional_dropout_rate, attention_dropout_rate, + input_layer, pos_enc_layer_type, normalize_before, + static_chunk_size, use_dynamic_chunk, global_cmvn, + use_dynamic_left_chunk, gradient_checkpointing) + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args), + PositionwiseFeedForward(*positionwise_layer_args), + PositionwiseFeedForward( + *positionwise_layer_args) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + ) for _ in range(num_blocks) + ]) diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/encoder_layer.py b/src/voice/cosyvoice-src/cosyvoice/transformer/encoder_layer.py new file mode 100644 index 0000000..efbb12d --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/encoder_layer.py @@ -0,0 +1,236 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder self-attention layer definition.""" + +from typing import Optional, Tuple + +import torch +from torch import nn + + +class TransformerEncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: to use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: torch.nn.Module, + dropout_rate: float, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = nn.LayerNorm(size, eps=1e-12) + self.norm2 = nn.LayerNorm(size, eps=1e-12) + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): just for interface compatibility + to ConformerEncoderLayer + mask_pad (torch.Tensor): does not used in transformer layer, + just for unified api with conformer. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2), not used here, it's for interface + compatibility to ConformerEncoderLayer. + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2). + + """ + residual = x + if self.normalize_before: + x = self.norm1(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb=pos_emb, cache=att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + return x, mask, new_att_cache, fake_cnn_cache + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = nn.LayerNorm(size, eps=1e-12) # for the FNN module + self.norm_mha = nn.LayerNorm(size, eps=1e-12) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = nn.LayerNorm(size, eps=1e-12) # for the CNN module + self.norm_final = nn.LayerNorm( + size, eps=1e-12) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, + att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/label_smoothing_loss.py b/src/voice/cosyvoice-src/cosyvoice/transformer/label_smoothing_loss.py new file mode 100644 index 0000000..feacabf --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/label_smoothing_loss.py @@ -0,0 +1,96 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Label smoothing module.""" + +import torch +from torch import nn + + +class LabelSmoothingLoss(nn.Module): + """Label-smoothing loss. + + In a standard CE loss, the label's data distribution is: + [0,1,2] -> + [ + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + + In the smoothing version CE Loss,some probabilities + are taken from the true label prob (1.0) and are divided + among other labels. + + e.g. + smoothing=0.1 + [0,1,2] -> + [ + [0.9, 0.05, 0.05], + [0.05, 0.9, 0.05], + [0.05, 0.05, 0.9], + ] + + Args: + size (int): the number of class + padding_idx (int): padding class id which will be ignored for loss + smoothing (float): smoothing rate (0.0 means the conventional CE) + normalize_length (bool): + normalize loss by sequence length if True + normalize loss by batch size if False + """ + + def __init__(self, + size: int, + padding_idx: int, + smoothing: float, + normalize_length: bool = False): + """Construct an LabelSmoothingLoss object.""" + super(LabelSmoothingLoss, self).__init__() + self.criterion = nn.KLDivLoss(reduction="none") + self.padding_idx = padding_idx + self.confidence = 1.0 - smoothing + self.smoothing = smoothing + self.size = size + self.normalize_length = normalize_length + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Compute loss between x and target. + + The model outputs and data labels tensors are flatten to + (batch*seqlen, class) shape and a mask is applied to the + padding part which should not be calculated for loss. + + Args: + x (torch.Tensor): prediction (batch, seqlen, class) + target (torch.Tensor): + target signal masked with self.padding_id (batch, seqlen) + Returns: + loss (torch.Tensor) : The KL loss, scalar float value + """ + assert x.size(2) == self.size + batch_size = x.size(0) + x = x.view(-1, self.size) + target = target.view(-1) + # use zeros_like instead of torch.no_grad() for true_dist, + # since no_grad() can not be exported by JIT + true_dist = torch.zeros_like(x) + true_dist.fill_(self.smoothing / (self.size - 1)) + ignore = target == self.padding_idx # (B,) + total = len(target) - ignore.sum().item() + target = target.masked_fill(ignore, 0) # avoid -1 index + true_dist.scatter_(1, target.unsqueeze(1), self.confidence) + kl = self.criterion(torch.log_softmax(x, dim=1), true_dist) + denom = total if self.normalize_length else batch_size + return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/positionwise_feed_forward.py b/src/voice/cosyvoice-src/cosyvoice/transformer/positionwise_feed_forward.py new file mode 100644 index 0000000..b7a2cf6 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/positionwise_feed_forward.py @@ -0,0 +1,115 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Positionwise feed forward layer definition.""" + +import torch + + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = torch.nn.Linear(hidden_units, idim) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) + + +class MoEFFNLayer(torch.nn.Module): + """ + Mixture of expert with Positionwise feed forward layer + See also figure 1 in https://arxiv.org/pdf/2305.15663.pdf + The output dim is same with the input dim. + + Modified from https://github.com/Lightning-AI/lit-gpt/pull/823 + https://github.com/mistralai/mistral-src/blob/b46d6/moe_one_file_ref.py#L203-L219 + Args: + n_expert: number of expert. + n_expert_per_token: The actual number of experts used for each frame + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + n_expert: int, + n_expert_per_token: int, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + ): + super(MoEFFNLayer, self).__init__() + self.gate = torch.nn.Linear(idim, n_expert, bias=False) + self.experts = torch.nn.ModuleList( + PositionwiseFeedForward(idim, hidden_units, dropout_rate, + activation) for _ in range(n_expert)) + self.n_expert_per_token = n_expert_per_token + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Foward function. + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + + """ + B, L, D = xs.size( + ) # batch size, sequence length, embedding dimension (idim) + xs = xs.view(-1, D) # (B*L, D) + router = self.gate(xs) # (B*L, n_expert) + logits, indices = torch.topk( + router, self.n_expert_per_token + ) # probs:(B*L, n_expert), indices: (B*L, n_expert) + weights = torch.nn.functional.softmax( + logits, dim=1, + dtype=torch.float).to(dtype=xs.dtype) # (B*L, n_expert_per_token) + output = torch.zeros_like(xs) # (B*L, D) + for i, expert in enumerate(self.experts): + mask = indices == i + batch_idx, ith_expert = torch.where(mask) + output[batch_idx] += weights[batch_idx, ith_expert, None] * expert( + xs[batch_idx]) + return output.view(B, L, D) diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/subsampling.py b/src/voice/cosyvoice-src/cosyvoice/transformer/subsampling.py new file mode 100644 index 0000000..e17c2e3 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/subsampling.py @@ -0,0 +1,383 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Subsampling layer definition.""" + +from typing import Tuple, Union + +import torch + + +class BaseSubsampling(torch.nn.Module): + + def __init__(self): + super().__init__() + self.right_context = 0 + self.subsampling_rate = 1 + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + +class EmbedinigNoSubsampling(BaseSubsampling): + """Embedding input without subsampling + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + super().__init__() + self.embed = torch.nn.Embedding(idim, odim) + self.pos_enc = pos_enc_class + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.embed(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask + + +class LinearNoSubsampling(BaseSubsampling): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + torch.nn.Linear(idim, odim), + torch.nn.LayerNorm(odim, eps=1e-5), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class + self.right_context = 0 + self.subsampling_rate = 1 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask + + +class Conv1dSubsampling2(BaseSubsampling): + """Convolutional 1D subsampling (to 1/2 length). + It is designed for Whisper, ref: + https://github.com/openai/whisper/blob/main/whisper/model.py + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv1dSubsampling2 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv1d(idim, odim, kernel_size=3, padding=1), + torch.nn.GELU(), + torch.nn.Conv1d(odim, odim, kernel_size=3, stride=2, padding=1), + torch.nn.GELU(), + ) + self.pos_enc = pos_enc_class + # The right context for every conv layer is computed by: + # (kernel_size - 1) * frame_rate_of_this_layer + self.subsampling_rate = 2 + # 4 = (3 - 1) * 1 + (3 - 1) * 1 + self.right_context = 4 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 2. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 2. + torch.Tensor: positional encoding + + """ + time = x.size(1) + x = x.transpose(1, 2) # (b, f, t) + x = self.conv(x) + x = x.transpose(1, 2) # (b, t, f) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, (time + 1) % 2::2] + + +class Conv2dSubsampling4(BaseSubsampling): + """Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv2dSubsampling4 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)) + self.pos_enc = pos_enc_class + # The right context for every conv layer is computed by: + # (kernel_size - 1) * frame_rate_of_this_layer + self.subsampling_rate = 4 + # 6 = (3 - 1) * 1 + (3 - 1) * 2 + self.right_context = 6 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 4. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 4. + torch.Tensor: positional encoding + + """ + x = x.unsqueeze(1) # (b, c=1, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2] + + +class Conv2dSubsampling6(BaseSubsampling): + """Convolutional 2D subsampling (to 1/6 length). + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv2dSubsampling6 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 5, 3), + torch.nn.ReLU(), + ) + self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), + odim) + self.pos_enc = pos_enc_class + # 10 = (3 - 1) * 1 + (5 - 1) * 2 + self.subsampling_rate = 6 + self.right_context = 10 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 6. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 6. + torch.Tensor: positional encoding + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3] + + +class Conv2dSubsampling8(BaseSubsampling): + """Convolutional 2D subsampling (to 1/8 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv2dSubsampling8 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.linear = torch.nn.Linear( + odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim) + self.pos_enc = pos_enc_class + self.subsampling_rate = 8 + # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4 + self.right_context = 14 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 8. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 8. + torch.Tensor: positional encoding + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2] + + +class LegacyLinearNoSubsampling(BaseSubsampling): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + torch.nn.Linear(idim, odim), + torch.nn.LayerNorm(odim, eps=1e-5), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + ) + self.pos_enc = pos_enc_class + self.right_context = 0 + self.subsampling_rate = 1 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask diff --git a/src/voice/cosyvoice-src/cosyvoice/transformer/upsample_encoder.py b/src/voice/cosyvoice-src/cosyvoice/transformer/upsample_encoder.py new file mode 100644 index 0000000..baf7481 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/transformer/upsample_encoder.py @@ -0,0 +1,321 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder definition.""" +from typing import Tuple + +import torch +from torch import nn +from torch.nn import functional as F + +from cosyvoice.transformer.convolution import ConvolutionModule +from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer +from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward +from cosyvoice.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_SUBSAMPLE_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from cosyvoice.utils.mask import make_pad_mask +from cosyvoice.utils.mask import add_optional_chunk_mask + + +class Upsample1D(nn.Module): + """A 1D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels: int, out_channels: int, stride: int = 2): + super().__init__() + self.channels = channels + self.out_channels = out_channels + self.stride = stride + # In this mode, first repeat interpolate, than conv with stride=1 + self.conv = nn.Conv1d(self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0) + + def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + outputs = F.interpolate(inputs, scale_factor=float(self.stride), mode="nearest") + outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0) + outputs = self.conv(outputs) + return outputs, input_lengths * self.stride + + +class PreLookaheadLayer(nn.Module): + def __init__(self, in_channels: int, channels: int, pre_lookahead_len: int = 1): + super().__init__() + self.in_channels = in_channels + self.channels = channels + self.pre_lookahead_len = pre_lookahead_len + self.conv1 = nn.Conv1d( + in_channels, channels, + kernel_size=pre_lookahead_len + 1, + stride=1, padding=0, + ) + self.conv2 = nn.Conv1d( + channels, in_channels, + kernel_size=3, stride=1, padding=0, + ) + + def forward(self, inputs: torch.Tensor, context: torch.Tensor = torch.zeros(0, 0, 0)) -> torch.Tensor: + """ + inputs: (batch_size, seq_len, channels) + """ + outputs = inputs.transpose(1, 2).contiguous() + context = context.transpose(1, 2).contiguous() + # look ahead + if context.size(2) == 0: + outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0) + else: + assert self.training is False, 'you have passed context, make sure that you are running inference mode' + assert context.size(2) == self.pre_lookahead_len + outputs = F.pad(torch.concat([outputs, context], dim=2), (0, self.pre_lookahead_len - context.size(2)), mode='constant', value=0.0) + outputs = F.leaky_relu(self.conv1(outputs)) + # outputs + outputs = F.pad(outputs, (self.conv2.kernel_size[0] - 1, 0), mode='constant', value=0.0) + outputs = self.conv2(outputs) + outputs = outputs.transpose(1, 2).contiguous() + + # residual connection + outputs = outputs + inputs + return outputs + + +class UpsampleConformerEncoder(torch.nn.Module): + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "rel_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool = True, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + gradient_checkpointing: bool = False, + ): + """ + Args: + input_size (int): input dim + output_size (int): dimension of attention + attention_heads (int): the number of heads of multi head attention + linear_units (int): the hidden units number of position-wise feed + forward + num_blocks (int): the number of decoder blocks + dropout_rate (float): dropout rate + attention_dropout_rate (float): dropout rate in attention + positional_dropout_rate (float): dropout rate after adding + positional encoding + input_layer (str): input layer type. + optional [linear, conv2d, conv2d6, conv2d8] + pos_enc_layer_type (str): Encoder positional encoding layer type. + opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] + normalize_before (bool): + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + static_chunk_size (int): chunk size for static chunk training and + decoding + use_dynamic_chunk (bool): whether use dynamic chunk size for + training or not, You can only use fixed chunk(chunk_size > 0) + or dyanmic chunk size(use_dynamic_chunk = True) + global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module + use_dynamic_left_chunk (bool): whether use dynamic left chunk in + dynamic chunk training + key_bias: whether use bias in attention.linear_k, False for whisper models. + gradient_checkpointing: rerunning a forward-pass segment for each + checkpointed segment during backward. + """ + super().__init__() + self._output_size = output_size + + self.global_cmvn = global_cmvn + self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, + positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + self.gradient_checkpointing = gradient_checkpointing + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + self.pre_lookahead_layer = PreLookaheadLayer(in_channels=512, channels=512, pre_lookahead_len=3) + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args), + PositionwiseFeedForward(*positionwise_layer_args), + PositionwiseFeedForward( + *positionwise_layer_args) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + ) for _ in range(num_blocks) + ]) + self.up_layer = Upsample1D(channels=512, out_channels=512, stride=2) + self.up_embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, + positional_dropout_rate), + ) + self.up_encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args), + PositionwiseFeedForward(*positionwise_layer_args), + PositionwiseFeedForward( + *positionwise_layer_args) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + ) for _ in range(4) + ]) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + context: torch.Tensor = torch.zeros(0, 0, 0), + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + if context.size(1) != 0: + assert self.training is False, 'you have passed context, make sure that you are running inference mode' + context_masks = torch.ones(1, 1, context.size(1)).to(masks) + context, _, _ = self.embed(context, context_masks, offset=xs.size(1)) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, False, False, 0, self.static_chunk_size if streaming is True else 0, -1) + # lookahead + conformer encoder + xs = self.pre_lookahead_layer(xs, context=context) + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + + # upsample + conformer encoder + xs = xs.transpose(1, 2).contiguous() + xs, xs_lens = self.up_layer(xs, xs_lens) + xs = xs.transpose(1, 2).contiguous() + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + xs, pos_emb, masks = self.up_embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, False, False, 0, self.static_chunk_size * self.up_layer.stride if streaming is True else 0, -1) + xs = self.forward_up_layers(xs, chunk_masks, pos_emb, mask_pad) + + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + def forward_up_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.up_encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/__init__.py b/src/voice/cosyvoice-src/cosyvoice/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/class_utils.py b/src/voice/cosyvoice-src/cosyvoice/utils/class_utils.py new file mode 100644 index 0000000..aab8326 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/class_utils.py @@ -0,0 +1,85 @@ +# Copyright [2023-11-28] +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from cosyvoice.transformer.activation import Swish +from cosyvoice.transformer.subsampling import ( + LinearNoSubsampling, + EmbedinigNoSubsampling, + Conv1dSubsampling2, + Conv2dSubsampling4, + Conv2dSubsampling6, + Conv2dSubsampling8, +) +from cosyvoice.transformer.embedding import (PositionalEncoding, + RelPositionalEncoding, + WhisperPositionalEncoding, + LearnablePositionalEncoding, + NoPositionalEncoding) +from cosyvoice.transformer.attention import (MultiHeadedAttention, + RelPositionMultiHeadedAttention) +from cosyvoice.transformer.embedding import EspnetRelPositionalEncoding +from cosyvoice.transformer.subsampling import LegacyLinearNoSubsampling +from cosyvoice.llm.llm import TransformerLM, Qwen2LM, CosyVoice3LM +from cosyvoice.flow.flow import MaskedDiffWithXvec, CausalMaskedDiffWithXvec, CausalMaskedDiffWithDiT +from cosyvoice.hifigan.generator import HiFTGenerator, CausalHiFTGenerator +from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model + + +COSYVOICE_ACTIVATION_CLASSES = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": getattr(torch.nn, "SiLU", Swish), + "gelu": torch.nn.GELU, +} + +COSYVOICE_SUBSAMPLE_CLASSES = { + "linear": LinearNoSubsampling, + "linear_legacy": LegacyLinearNoSubsampling, + "embed": EmbedinigNoSubsampling, + "conv1d2": Conv1dSubsampling2, + "conv2d": Conv2dSubsampling4, + "conv2d6": Conv2dSubsampling6, + "conv2d8": Conv2dSubsampling8, + 'paraformer_dummy': torch.nn.Identity +} + +COSYVOICE_EMB_CLASSES = { + "embed": PositionalEncoding, + "abs_pos": PositionalEncoding, + "rel_pos": RelPositionalEncoding, + "rel_pos_espnet": EspnetRelPositionalEncoding, + "no_pos": NoPositionalEncoding, + "abs_pos_whisper": WhisperPositionalEncoding, + "embed_learnable_pe": LearnablePositionalEncoding, +} + +COSYVOICE_ATTENTION_CLASSES = { + "selfattn": MultiHeadedAttention, + "rel_selfattn": RelPositionMultiHeadedAttention, +} + + +def get_model_type(configs): + # NOTE CosyVoice2Model inherits CosyVoiceModel + if isinstance(configs['llm'], TransformerLM) and isinstance(configs['flow'], MaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator): + return CosyVoiceModel + if isinstance(configs['llm'], Qwen2LM) and isinstance(configs['flow'], CausalMaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator): + return CosyVoice2Model + if isinstance(configs['llm'], CosyVoice3LM) and isinstance(configs['flow'], CausalMaskedDiffWithDiT) and isinstance(configs['hift'], CausalHiFTGenerator): + return CosyVoice3Model + raise TypeError('No valid model type found!') diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/common.py b/src/voice/cosyvoice-src/cosyvoice/utils/common.py new file mode 100644 index 0000000..3f235a6 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/common.py @@ -0,0 +1,214 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Unility functions for Transformer.""" + +import queue +import random +from typing import List + +import numpy as np +import torch + +IGNORE_ID = -1 + +instruct_list = ["You are a helpful assistant. 请用广东话表达。<|endofprompt|>", + "You are a helpful assistant. 请用东北话表达。<|endofprompt|>", + "You are a helpful assistant. 请用甘肃话表达。<|endofprompt|>", + "You are a helpful assistant. 请用贵州话表达。<|endofprompt|>", + "You are a helpful assistant. 请用河南话表达。<|endofprompt|>", + "You are a helpful assistant. 请用湖北话表达。<|endofprompt|>", + "You are a helpful assistant. 请用湖南话表达。<|endofprompt|>", + "You are a helpful assistant. 请用江西话表达。<|endofprompt|>", + "You are a helpful assistant. 请用闽南话表达。<|endofprompt|>", + "You are a helpful assistant. 请用宁夏话表达。<|endofprompt|>", + "You are a helpful assistant. 请用山西话表达。<|endofprompt|>", + "You are a helpful assistant. 请用陕西话表达。<|endofprompt|>", + "You are a helpful assistant. 请用山东话表达。<|endofprompt|>", + "You are a helpful assistant. 请用上海话表达。<|endofprompt|>", + "You are a helpful assistant. 请用四川话表达。<|endofprompt|>", + "You are a helpful assistant. 请用天津话表达。<|endofprompt|>", + "You are a helpful assistant. 请用云南话表达。<|endofprompt|>", + "You are a helpful assistant. Please say a sentence as loudly as possible.<|endofprompt|>", + "You are a helpful assistant. Please say a sentence in a very soft voice.<|endofprompt|>", + "You are a helpful assistant. 请用尽可能慢地语速说一句话。<|endofprompt|>", + "You are a helpful assistant. 请用尽可能快地语速说一句话。<|endofprompt|>", + "You are a helpful assistant. 请非常开心地说一句话。<|endofprompt|>", + "You are a helpful assistant. 请非常伤心地说一句话。<|endofprompt|>", + "You are a helpful assistant. 请非常生气地说一句话。<|endofprompt|>", + "You are a helpful assistant. 我想体验一下小猪佩奇风格，可以吗？<|endofprompt|>", + "You are a helpful assistant. 你可以尝试用机器人的方式解答吗？<|endofprompt|>"] + + +def pad_list(xs: List[torch.Tensor], pad_value: int): + """Perform padding for the list of tensors. + + Args: + xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. + pad_value (float): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tmax, `*`). + + Examples: + >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] + >>> x + [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] + >>> pad_list(x, 0) + tensor([[1., 1., 1., 1.], + [1., 1., 0., 0.], + [1., 0., 0., 0.]]) + + """ + max_len = max([len(item) for item in xs]) + batchs = len(xs) + ndim = xs[0].ndim + if ndim == 1: + pad_res = torch.zeros(batchs, + max_len, + dtype=xs[0].dtype, + device=xs[0].device) + elif ndim == 2: + pad_res = torch.zeros(batchs, + max_len, + xs[0].shape[1], + dtype=xs[0].dtype, + device=xs[0].device) + elif ndim == 3: + pad_res = torch.zeros(batchs, + max_len, + xs[0].shape[1], + xs[0].shape[2], + dtype=xs[0].dtype, + device=xs[0].device) + else: + raise ValueError(f"Unsupported ndim: {ndim}") + pad_res.fill_(pad_value) + for i in range(batchs): + pad_res[i, :len(xs[i])] = xs[i] + return pad_res + + +def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor, + ignore_label: int) -> torch.Tensor: + """Calculate accuracy. + + Args: + pad_outputs (Tensor): Prediction tensors (B * Lmax, D). + pad_targets (LongTensor): Target label tensors (B, Lmax). + ignore_label (int): Ignore label id. + + Returns: + torch.Tensor: Accuracy value (0.0 - 1.0). + + """ + pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1), + pad_outputs.size(1)).argmax(2) + mask = pad_targets != ignore_label + numerator = torch.sum( + pad_pred.masked_select(mask) == pad_targets.masked_select(mask)) + denominator = torch.sum(mask) + return (numerator / denominator).detach() + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +# Repetition Aware Sampling in VALL-E 2 +def ras_sampling(weighted_scores, decoded_tokens, sampling, top_p=0.8, top_k=25, win_size=10, tau_r=0.1): + top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k) + rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item() + if rep_num >= win_size * tau_r: + weighted_scores[top_ids] = -float('inf') + top_ids = random_sampling(weighted_scores, decoded_tokens, sampling) + return top_ids + + +def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25): + prob, indices = [], [] + cum_prob = 0.0 + sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True) + for i in range(len(sorted_idx)): + # sampling both top-p and numbers. + if cum_prob < top_p and len(prob) < top_k: + cum_prob += sorted_value[i] + prob.append(sorted_value[i]) + indices.append(sorted_idx[i]) + else: + break + prob = torch.tensor(prob).to(weighted_scores) + indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device) + top_ids = indices[prob.multinomial(1, replacement=True)].item() + return top_ids + + +def random_sampling(weighted_scores, decoded_tokens, sampling): + top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True).item() + return top_ids + + +def fade_in_out(fade_in_mel, fade_out_mel, window): + device = fade_in_mel.device + fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu() + mel_overlap_len = int(window.shape[0] / 2) + if fade_in_mel.device == torch.device('cpu'): + fade_in_mel = fade_in_mel.clone() + fade_in_mel[..., :mel_overlap_len] = fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \ + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:] + return fade_in_mel.to(device) + + +def set_all_random_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor: + assert mask.dtype == torch.bool + assert dtype in [torch.float32, torch.bfloat16, torch.float16] + mask = mask.to(dtype) + # attention mask bias + # NOTE(Mddct): torch.finfo jit issues + # chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min + mask = (1.0 - mask) * -1.0e+10 + return mask + + +class TrtContextWrapper: + def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'): + self.trt_context_pool = queue.Queue(maxsize=trt_concurrent) + self.trt_engine = trt_engine + for _ in range(trt_concurrent): + trt_context = trt_engine.create_execution_context() + trt_stream = torch.cuda.stream(torch.cuda.Stream(device)) + assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent) + self.trt_context_pool.put([trt_context, trt_stream]) + assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context' + + def acquire_estimator(self): + return self.trt_context_pool.get(), self.trt_engine + + def release_estimator(self, context, stream): + self.trt_context_pool.put([context, stream]) diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/executor.py b/src/voice/cosyvoice-src/cosyvoice/utils/executor.py new file mode 100644 index 0000000..f08fa09 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/executor.py @@ -0,0 +1,176 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from contextlib import nullcontext +import os + +import torch +import torch.distributed as dist + +from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, log_per_save, batch_forward, batch_backward, save_model, cosyvoice_join + + +class Executor: + + def __init__(self, gan: bool = False, ref_model: torch.nn.Module = None, dpo_loss: torch.nn.Module = None): + self.gan = gan + self.ref_model = ref_model + self.dpo_loss = dpo_loss + self.step = 0 + self.epoch = 0 + self.rank = int(os.environ.get('RANK', 0)) + self.device = torch.device('cuda:{}'.format(self.rank)) + + def train_one_epoc(self, model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=None): + ''' Train one epoch + ''' + + lr = optimizer.param_groups[0]['lr'] + logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank)) + logging.info('using accumulate grad, new batch size is {} times' + ' larger than before'.format(info_dict['accum_grad'])) + # A context manager to be used in conjunction with an instance of + # torch.nn.parallel.DistributedDataParallel to be able to train + # with uneven inputs across participating processes. + model.train() + if self.ref_model is not None: + self.ref_model.eval() + model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext + with model_context(): + for batch_idx, batch_dict in enumerate(train_data_loader): + info_dict["tag"] = "TRAIN" + info_dict["step"] = self.step + info_dict["epoch"] = self.epoch + info_dict["batch_idx"] = batch_idx + if cosyvoice_join(group_join, info_dict): + break + + # Disable gradient synchronizations across DDP processes. + # Within this context, gradients will be accumulated on module + # variables, which will later be synchronized. + if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0: + context = model.no_sync + # Used for single gpu training and DDP gradient synchronization + # processes. + else: + context = nullcontext + + with context(): + info_dict = batch_forward(model, batch_dict, scaler, info_dict, ref_model=self.ref_model, dpo_loss=self.dpo_loss) + info_dict = batch_backward(model, scaler, info_dict) + + info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict) + log_per_step(writer, info_dict) + # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save + if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \ + (batch_idx + 1) % info_dict["accum_grad"] == 0: + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False) + model.train() + if (batch_idx + 1) % info_dict["accum_grad"] == 0: + self.step += 1 + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True) + + def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, + writer, info_dict, scaler, group_join): + ''' Train one epoch + ''' + + lr = optimizer.param_groups[0]['lr'] + logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank)) + logging.info('using accumulate grad, new batch size is {} times' + ' larger than before'.format(info_dict['accum_grad'])) + # A context manager to be used in conjunction with an instance of + # torch.nn.parallel.DistributedDataParallel to be able to train + # with uneven inputs across participating processes. + model.train() + model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext + with model_context(): + for batch_idx, batch_dict in enumerate(train_data_loader): + info_dict["tag"] = "TRAIN" + info_dict["step"] = self.step + info_dict["epoch"] = self.epoch + info_dict["batch_idx"] = batch_idx + if cosyvoice_join(group_join, info_dict): + break + + # Disable gradient synchronizations across DDP processes. + # Within this context, gradients will be accumulated on module + # variables, which will later be synchronized. + if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0: + context = model.no_sync + # Used for single gpu training and DDP gradient synchronization + # processes. + else: + context = nullcontext + + with context(): + batch_dict['turn'] = 'discriminator' + info_dict = batch_forward(model, batch_dict, scaler, info_dict) + info_dict = batch_backward(model, scaler, info_dict) + info_dict = update_parameter_and_lr(model, optimizer_d, scheduler_d, scaler, info_dict) + optimizer.zero_grad() + log_per_step(writer, info_dict) + with context(): + batch_dict['turn'] = 'generator' + info_dict = batch_forward(model, batch_dict, scaler, info_dict) + info_dict = batch_backward(model, scaler, info_dict) + info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict) + optimizer_d.zero_grad() + log_per_step(writer, info_dict) + # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save + if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \ + (batch_idx + 1) % info_dict["accum_grad"] == 0: + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False) + model.train() + if (batch_idx + 1) % info_dict["accum_grad"] == 0: + self.step += 1 + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True) + + @torch.inference_mode() + def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True): + ''' Cross validation on + ''' + logging.info('Epoch {} Step {} on_batch_end {} CV rank {}'.format(self.epoch, self.step + 1, on_batch_end, self.rank)) + model.eval() + total_num_utts, total_loss_dict = 0, {} # avoid division by 0 + for batch_idx, batch_dict in enumerate(cv_data_loader): + info_dict["tag"] = "CV" + info_dict["step"] = self.step + info_dict["epoch"] = self.epoch + info_dict["batch_idx"] = batch_idx + + num_utts = len(batch_dict["utts"]) + total_num_utts += num_utts + + if self.gan is True: + batch_dict['turn'] = 'generator' + info_dict = batch_forward(model, batch_dict, None, info_dict) + + for k, v in info_dict['loss_dict'].items(): + if k not in total_loss_dict: + total_loss_dict[k] = [] + total_loss_dict[k].append(v.mean().item() * num_utts) + log_per_step(None, info_dict) + for k, v in total_loss_dict.items(): + total_loss_dict[k] = sum(v) / total_num_utts + info_dict['loss_dict'] = total_loss_dict + log_per_save(writer, info_dict) + model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1) + save_model(model, model_name, info_dict) diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/file_utils.py b/src/voice/cosyvoice-src/cosyvoice/utils/file_utils.py new file mode 100644 index 0000000..1bab999 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/file_utils.py @@ -0,0 +1,125 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu, Zetao Hu) +# 2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import json +import torch +import torchaudio +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + +def read_lists(list_file): + lists = [] + with open(list_file, 'r', encoding='utf8') as fin: + for line in fin: + lists.append(line.strip()) + return lists + + +def read_json_lists(list_file): + lists = read_lists(list_file) + results = {} + for fn in lists: + with open(fn, 'r', encoding='utf8') as fin: + results.update(json.load(fin)) + return results + + +def load_wav(wav, target_sr, min_sr=16000): + # Windows 本地改造：torchaudio 2.9 仅剩 torchcodec 后端（需 FFmpeg DLL）， + # 改用 soundfile 读取，语义不变（多声道取均值 + 重采样） + import soundfile as sf + data, sample_rate = sf.read(wav, dtype='float32') + speech = torch.from_numpy(data) + if speech.dim() == 1: + speech = speech.unsqueeze(0) + else: + speech = speech.mean(dim=1, keepdim=True).t() # (frames, ch) -> (1, frames) + if sample_rate != target_sr: + assert sample_rate >= min_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr) + speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech) + return speech + + +def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, fp16): + import tensorrt as trt + logging.info("Converting onnx to trt...") + network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + logger = trt.Logger(trt.Logger.INFO) + builder = trt.Builder(logger) + network = builder.create_network(network_flags) + parser = trt.OnnxParser(network, logger) + config = builder.create_builder_config() + config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB + if fp16: + config.set_flag(trt.BuilderFlag.FP16) + profile = builder.create_optimization_profile() + # load onnx model + with open(onnx_model, "rb") as f: + if not parser.parse(f.read()): + for error in range(parser.num_errors): + print(parser.get_error(error)) + raise ValueError('failed to parse {}'.format(onnx_model)) + # set input shapes + for i in range(len(trt_kwargs['input_names'])): + profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i]) + tensor_dtype = trt.DataType.HALF if fp16 else trt.DataType.FLOAT + # set input and output data type + for i in range(network.num_inputs): + input_tensor = network.get_input(i) + input_tensor.dtype = tensor_dtype + for i in range(network.num_outputs): + output_tensor = network.get_output(i) + output_tensor.dtype = tensor_dtype + config.add_optimization_profile(profile) + engine_bytes = builder.build_serialized_network(network, config) + # save trt engine + with open(trt_model, "wb") as f: + f.write(engine_bytes) + logging.info("Succesfully convert onnx to trt...") + + +# NOTE do not support bistream inference as only speech token embedding/head is kept +def export_cosyvoice2_vllm(model, model_path, device): + if os.path.exists(model_path): + return + + dtype = torch.bfloat16 + # lm_head + use_bias = True if model.llm_decoder.bias is not None else False + model.llm.model.lm_head = model.llm_decoder + # embed_tokens + embed_tokens = model.llm.model.model.embed_tokens + model.llm.model.set_input_embeddings(model.speech_embedding) + model.llm.model.to(device) + model.llm.model.to(dtype) + tmp_vocab_size = model.llm.model.config.vocab_size + tmp_tie_embedding = model.llm.model.config.tie_word_embeddings + del model.llm.model.generation_config.eos_token_id + del model.llm.model.config.bos_token_id + del model.llm.model.config.eos_token_id + model.llm.model.config.vocab_size = model.speech_embedding.num_embeddings + model.llm.model.config.tie_word_embeddings = False + model.llm.model.config.use_bias = use_bias + model.llm.model.save_pretrained(model_path) + if use_bias is True: + os.system('sed -i s@Qwen2ForCausalLM@CosyVoice2ForCausalLM@g {}/config.json'.format(os.path.abspath(model_path))) + model.llm.model.config.vocab_size = tmp_vocab_size + model.llm.model.config.tie_word_embeddings = tmp_tie_embedding + model.llm.model.set_input_embeddings(embed_tokens) diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/frontend_utils.py b/src/voice/cosyvoice-src/cosyvoice/utils/frontend_utils.py new file mode 100644 index 0000000..ea1c9fc --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/frontend_utils.py @@ -0,0 +1,136 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +import regex +chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]+') + + +# whether contain chinese character +def contains_chinese(text): + return bool(chinese_char_pattern.search(text)) + + +# replace special symbol +def replace_corner_mark(text): + text = text.replace('²', '平方') + text = text.replace('³', '立方') + return text + + +# remove meaningless symbol +def remove_bracket(text): + text = text.replace('（', '').replace('）', '') + text = text.replace('【', '').replace('】', '') + text = text.replace('`', '').replace('`', '') + text = text.replace("——", " ") + return text + + +# spell Arabic numerals +def spell_out_number(text: str, inflect_parser): + new_text = [] + st = None + for i, c in enumerate(text): + if not c.isdigit(): + if st is not None: + num_str = inflect_parser.number_to_words(text[st: i]) + new_text.append(num_str) + st = None + new_text.append(c) + else: + if st is None: + st = i + if st is not None and st < len(text): + num_str = inflect_parser.number_to_words(text[st:]) + new_text.append(num_str) + return ''.join(new_text) + + +# split paragrah logic： +# 1. per sentence max len token_max_n, min len token_min_n, merge if last sentence len less than merge_len +# 2. cal sentence len according to lang +# 3. split sentence according to puncatation +def split_paragraph(text: str, tokenize, lang="zh", token_max_n=80, token_min_n=60, merge_len=20, comma_split=False): + def calc_utt_length(_text: str): + if lang == "zh": + return len(_text) + else: + return len(tokenize(_text)) + + def should_merge(_text: str): + if lang == "zh": + return len(_text) < merge_len + else: + return len(tokenize(_text)) < merge_len + + if lang == "zh": + pounc = ['。', '？', '！', '；', '：', '、', '.', '?', '!', ';'] + else: + pounc = ['.', '?', '!', ';', ':'] + if comma_split: + pounc.extend(['，', ',']) + + if text[-1] not in pounc: + if lang == "zh": + text += "。" + else: + text += "." + + st = 0 + utts = [] + for i, c in enumerate(text): + if c in pounc: + if len(text[st: i]) > 0: + utts.append(text[st: i] + c) + if i + 1 < len(text) and text[i + 1] in ['"', '”']: + tmp = utts.pop(-1) + utts.append(tmp + text[i + 1]) + st = i + 2 + else: + st = i + 1 + + final_utts = [] + cur_utt = "" + for utt in utts: + if calc_utt_length(cur_utt + utt) > token_max_n and calc_utt_length(cur_utt) > token_min_n: + final_utts.append(cur_utt) + cur_utt = "" + cur_utt = cur_utt + utt + if len(cur_utt) > 0: + if should_merge(cur_utt) and len(final_utts) != 0: + final_utts[-1] = final_utts[-1] + cur_utt + else: + final_utts.append(cur_utt) + + return final_utts + + +# remove blank between chinese character +def replace_blank(text: str): + out_str = [] + for i, c in enumerate(text): + if c == " ": + if ((text[i + 1].isascii() and text[i + 1] != " ") and + (text[i - 1].isascii() and text[i - 1] != " ")): + out_str.append(c) + else: + out_str.append(c) + return "".join(out_str) + + +def is_only_punctuation(text): + # Regular expression: Match strings that consist only of punctuation marks or are empty. + punctuation_pattern = r'^[\p{P}\p{S}]*$' + return bool(regex.fullmatch(punctuation_pattern, text)) diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/losses.py b/src/voice/cosyvoice-src/cosyvoice/utils/losses.py new file mode 100644 index 0000000..2429fdc --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/losses.py @@ -0,0 +1,57 @@ +import torch +import torch.nn.functional as F +from typing import Tuple + + +def tpr_loss(disc_real_outputs, disc_generated_outputs, tau): + loss = 0 + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + m_DG = torch.median((dr - dg)) + L_rel = torch.mean((((dr - dg) - m_DG) ** 2)[dr < dg + m_DG]) + loss += tau - F.relu(tau - L_rel) + return loss + + +def mel_loss(real_speech, generated_speech, mel_transforms): + loss = 0 + for transform in mel_transforms: + mel_r = transform(real_speech) + mel_g = transform(generated_speech) + loss += F.l1_loss(mel_g, mel_r) + return loss + + +class DPOLoss(torch.nn.Module): + """ + DPO Loss + """ + + def __init__(self, beta: float, label_smoothing: float = 0.0, ipo: bool = False) -> None: + super().__init__() + self.beta = beta + self.label_smoothing = label_smoothing + self.ipo = ipo + + def forward( + self, + policy_chosen_logps: torch.Tensor, + policy_rejected_logps: torch.Tensor, + reference_chosen_logps: torch.Tensor, + reference_rejected_logps: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + pi_logratios = policy_chosen_logps - policy_rejected_logps + ref_logratios = reference_chosen_logps - reference_rejected_logps + logits = pi_logratios - ref_logratios + if self.ipo: + losses = (logits - 1 / (2 * self.beta)) ** 2 # Eq. 17 of https://arxiv.org/pdf/2310.12036v2.pdf + else: + # Eq. 3 https://ericmitchell.ai/cdpo.pdf; label_smoothing=0 gives original DPO (Eq. 7 of https://arxiv.org/pdf/2305.18290.pdf) + losses = ( + -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * logits) * self.label_smoothing + ) + loss = losses.mean() + chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach() + rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach() + + return loss, chosen_rewards, rejected_rewards diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/mask.py b/src/voice/cosyvoice-src/cosyvoice/utils/mask.py new file mode 100644 index 0000000..5d3dfd6 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/mask.py @@ -0,0 +1,265 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +''' +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + ret = torch.ones(size, size, device=device, dtype=torch.bool) + return torch.tril(ret) +''' + + +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + arange = torch.arange(size, device=device) + mask = arange.expand(size, size) + arange = arange.unsqueeze(-1) + mask = mask <= arange + return mask + + +def subsequent_chunk_mask_deprecated( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + + +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + # NOTE this modified implementation meets onnx export requirements, but it doesn't support num_left_chunks + pos_idx = torch.arange(size, device=device) + block_value = (torch.div(pos_idx, chunk_size, rounding_mode='trunc') + 1) * chunk_size + ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1) + return ret + + +def add_optional_chunk_mask(xs: torch.Tensor, + masks: torch.Tensor, + use_dynamic_chunk: bool, + use_dynamic_left_chunk: bool, + decoding_chunk_size: int, + static_chunk_size: int, + num_decoding_left_chunks: int, + enable_full_context: bool = True): + """ Apply optional mask for encoder. + + Args: + xs (torch.Tensor): padded input, (B, L, D), L for max length + mask (torch.Tensor): mask for xs, (B, 1, L) + use_dynamic_chunk (bool): whether to use dynamic chunk or not + use_dynamic_left_chunk (bool): whether to use dynamic left chunk for + training. + decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + static_chunk_size (int): chunk size for static chunk training/decoding + if it's greater than 0, if use_dynamic_chunk is true, + this parameter will be ignored + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + enable_full_context (bool): + True: chunk size is either [1, 25] or full context(max_len) + False: chunk size ~ U[1, 25] + + Returns: + torch.Tensor: chunk mask of the input xs. + """ + # Whether to use chunk mask or not + if use_dynamic_chunk: + max_len = xs.size(1) + if decoding_chunk_size < 0: + chunk_size = max_len + num_left_chunks = -1 + elif decoding_chunk_size > 0: + chunk_size = decoding_chunk_size + num_left_chunks = num_decoding_left_chunks + else: + # chunk size is either [1, 25] or full context(max_len). + # Since we use 4 times subsampling and allow up to 1s(100 frames) + # delay, the maximum frame is 100 / 4 = 25. + chunk_size = torch.randint(1, max_len, (1, )).item() + num_left_chunks = -1 + if chunk_size > max_len // 2 and enable_full_context: + chunk_size = max_len + else: + chunk_size = chunk_size % 25 + 1 + if use_dynamic_left_chunk: + max_left_chunks = (max_len - 1) // chunk_size + num_left_chunks = torch.randint(0, max_left_chunks, + (1, )).item() + chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + elif static_chunk_size > 0: + num_left_chunks = num_decoding_left_chunks + chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + else: + chunk_masks = masks + assert chunk_masks.dtype == torch.bool + if (chunk_masks.sum(dim=-1) == 0).sum().item() != 0: + print('get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!') + chunk_masks[chunk_masks.sum(dim=-1) == 0] = True + return chunk_masks + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, + max_len, + dtype=torch.int64, + device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/onnx.py b/src/voice/cosyvoice-src/cosyvoice/utils/onnx.py new file mode 100644 index 0000000..b459eb5 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/onnx.py @@ -0,0 +1,54 @@ +import onnxruntime +import torch, random +import os +import torchaudio.compliance.kaldi as kaldi + + +class SpeechTokenExtractor(): + def __init__(self, model_path): + self.local_rank = int(os.environ.get("LOCAL_RANK", 0)) + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.speech_tokenizer_session = onnxruntime.InferenceSession(model_path, + sess_options=option, + providers=[("CUDAExecutionProvider", {'device_id': self.local_rank})]) + + def inference(self, feat, feat_lengths, device): + speech_token = self.speech_tokenizer_session.run(None, + {self.speech_tokenizer_session.get_inputs()[0].name: + feat.transpose(1, 2).detach().cpu().numpy(), + self.speech_tokenizer_session.get_inputs()[1].name: + feat_lengths.detach().cpu().numpy()})[0] + return torch.tensor(speech_token).to(torch.int32).to(device), (feat_lengths / 4).to(torch.int32).to(device) + + +class EmbeddingExtractor(): + def __init__(self, model_path): + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.max_len = 10 * 16000 + self.campplus_session = onnxruntime.InferenceSession(model_path, + sess_options=option, + providers=["CPUExecutionProvider"]) + + def inference(self, speech): + if speech.shape[1] > self.max_len: + start_index = random.randint(0, speech.shape[1] - self.max_len) + speech = speech[:, start_index: start_index + self.max_len] + feat = kaldi.fbank(speech, + num_mel_bins=80, + dither=0, + sample_frequency=16000) + feat = feat - feat.mean(dim=0, keepdim=True) + embedding = self.campplus_session.run(None, + {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() + return torch.tensor(embedding).to(speech.device) + +# singleton mode, only initialized once +onnx_path = os.environ.get('onnx_path') +if onnx_path is not None: + embedding_extractor, online_feature = EmbeddingExtractor(model_path=os.path.join(onnx_path, 'campplus.onnx')), True +else: + embedding_extractor, online_feature = None, False \ No newline at end of file diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/scheduler.py b/src/voice/cosyvoice-src/cosyvoice/utils/scheduler.py new file mode 100644 index 0000000..06e7f3d --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/scheduler.py @@ -0,0 +1,738 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2022 Ximalaya Inc (Yuguang Yang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +# NeMo(https://github.com/NVIDIA/NeMo) + +from typing import Union + +import math +import warnings +import torch +from torch.optim.lr_scheduler import _LRScheduler + + +class WarmupLR(_LRScheduler): + """The WarmupLR scheduler + + This scheduler is almost same as NoamLR Scheduler except for following + difference: + + NoamLR: + lr = optimizer.lr * model_size ** -0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + WarmupLR: + lr = optimizer.lr * warmup_step ** 0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + + Note that the maximum lr equals to optimizer.lr in this scheduler. + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + warmup_steps: Union[int, float] = 25000, + last_epoch: int = -1, + ): + self.warmup_steps = warmup_steps + + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer, last_epoch) + + def __repr__(self): + return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})" + + def get_lr(self): + step_num = self.last_epoch + 1 + if self.warmup_steps == 0: + return [lr * step_num**-0.5 for lr in self.base_lrs] + else: + return [ + lr * self.warmup_steps**0.5 * + min(step_num**-0.5, step_num * self.warmup_steps**-1.5) + for lr in self.base_lrs + ] + + def set_step(self, step: int): + self.last_epoch = step + + +class WarmupPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__(self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1): + assert not (warmup_steps is not None and warmup_ratio is not None),\ + "Either use particular number of step or ratio" + assert warmup_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + if step <= self.warmup_steps and self.warmup_steps > 0: + return self._get_warmup_lr(step) + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_warmup_lr(self, step): + lr_val = (step + 1) / (self.warmup_steps + 1) + return [initial_lr * lr_val for initial_lr in self.base_lrs] + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +class SquareRootConstantPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__(self, + optimizer, + *, + constant_steps=None, + constant_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1): + assert not (constant_steps is not None + and constant_ratio is not None), \ + "Either use particular number of step or ratio" + assert constant_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if constant_steps is not None: + self.constant_steps = constant_steps + elif constant_ratio is not None: + self.constant_steps = int(constant_ratio * max_steps) + else: + self.constant_steps = 0 + + self.constant_lr = 1 / (constant_steps**0.5) + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + if step <= self.constant_steps: + return [self.constant_lr for _ in self.base_lrs] + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +class WarmupHoldPolicy(WarmupPolicy): + """Variant of WarmupPolicy which maintains high + learning rate for a defined number of steps. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + hold_steps: Number of training steps to + hold the learning rate after warm up + hold_ratio: Ratio of hold steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + hold_steps=None, + hold_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not (hold_steps is not None and hold_ratio is not None), \ + "Either use particular number of step or ratio" + assert hold_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + self.min_lr = min_lr + self._last_warmup_lr = 0.0 + + # Necessary to duplicate as class attributes are hidden in inner class + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + if hold_steps is not None: + self.hold_steps = hold_steps + self.warmup_steps + elif hold_ratio is not None: + self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps + else: + self.hold_steps = 0 + + super().__init__( + optimizer, + warmup_steps=warmup_steps, + warmup_ratio=warmup_ratio, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + ) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed by the scheduler," + " " + "please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + # Warmup phase + if step <= self.warmup_steps and self.warmup_steps > 0: + return self._get_warmup_lr(step) + + # Hold phase + if (step >= self.warmup_steps) and (step < self.hold_steps): + return self.base_lrs + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + +class WarmupAnnealHoldPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + min_lr: Minimum lr to hold the learning rate after decay at. + constant_steps: Number of steps to keep lr constant at. + constant_ratio: Ratio of steps to keep lr constant. + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + constant_steps=None, + constant_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not (warmup_steps is not None + and warmup_ratio is not None), \ + "Either use particular number of step or ratio" + assert not (constant_steps is not None + and constant_ratio is not None), \ + "Either use constant_steps or constant_ratio" + assert warmup_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + if constant_steps is not None: + self.constant_steps = constant_steps + elif constant_ratio is not None: + self.constant_steps = int(constant_ratio * max_steps) + else: + self.constant_steps = 0 + + self.decay_steps = max_steps - (self.constant_steps + + self.warmup_steps) + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + # Warmup steps + if self.warmup_steps > 0 and step <= self.warmup_steps: + return self._get_warmup_lr(step) + + # Constant steps after warmup and decay + if self.constant_steps > 0 and ( + self.warmup_steps + self.decay_steps) < step <= self.max_steps: + return self._get_constant_lr(step) + + # Min lr after max steps of updates + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_warmup_lr(self, step): + lr_val = (step + 1) / (self.warmup_steps + 1) + return [initial_lr * lr_val for initial_lr in self.base_lrs] + + def _get_constant_lr(self, step): + return [self.min_lr for _ in self.base_lrs] + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +def _squareroot_annealing(initial_lr, step, max_steps, min_lr): + mult = ((max_steps - step) / max_steps)**0.5 + out_lr = initial_lr * mult + out_lr = max(out_lr, min_lr) + return out_lr + + +def _square_annealing(initial_lr, step, max_steps, min_lr): + mult = ((max_steps - step) / max_steps)**2 + out_lr = initial_lr * mult + out_lr = max(out_lr, min_lr) + return out_lr + + +def _cosine_annealing(initial_lr, step, max_steps, min_lr): + mult = 0.5 * (1 + math.cos(math.pi * step / max_steps)) + out_lr = (initial_lr - min_lr) * mult + min_lr + return out_lr + + +def _linear_warmup_with_cosine_annealing(max_lr, warmup_steps, step, + decay_steps, min_lr): + assert max_lr > min_lr + # Use linear warmup for the initial part. + if warmup_steps > 0 and step <= warmup_steps: + return max_lr * float(step) / float(warmup_steps) + + # For any steps larger than `decay_steps`, use `min_lr`. + if step > warmup_steps + decay_steps: + return min_lr + + # If we are done with the warmup period, use the decay style. + num_steps_ = step - warmup_steps + decay_steps_ = decay_steps + decay_ratio = float(num_steps_) / float(decay_steps_) + assert decay_ratio >= 0.0 + assert decay_ratio <= 1.0 + delta_lr = max_lr - min_lr + + coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0) + + return min_lr + coeff * delta_lr + + +def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle): + if cycle: + multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps) + decay_steps *= multiplier + else: + step = min(step, decay_steps) + p = step / decay_steps + lr = (initial_lr - min_lr) * math.pow(1.0 - p, power) + lr += min_lr + return lr + + +def _noam_hold_annealing(initial_lr, step, warmup_steps, hold_steps, + decay_rate, min_lr): + # hold_steps = total number of steps + # to hold the LR, not the warmup + hold steps. + T_warmup_decay = max(1, warmup_steps**decay_rate) + T_hold_decay = max(1, (step - hold_steps)**decay_rate) + lr = (initial_lr * T_warmup_decay) / T_hold_decay + lr = max(lr, min_lr) + return lr + + +class SquareAnnealing(WarmupPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + min_lr=1e-5, + last_epoch=-1, + **kwargs): + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + new_lrs = [ + _square_annealing( + initial_lr=initial_lr, + step=step - self.warmup_steps, + max_steps=self.max_steps - self.warmup_steps, + min_lr=self.min_lr, + ) for initial_lr in self.base_lrs + ] + return new_lrs + + +class SquareRootAnnealing(WarmupPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + min_lr=0, + last_epoch=-1, + **kwargs): + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + new_lrs = [ + _squareroot_annealing(initial_lr=initial_lr, + step=step, + max_steps=self.max_steps, + min_lr=self.min_lr) + for initial_lr in self.base_lrs + ] + return new_lrs + + +class CosineAnnealing(WarmupAnnealHoldPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + min_lr=0, + last_epoch=-1, + **kwargs): + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + for initial_lr in self.base_lrs: + if initial_lr < self.min_lr: + raise ValueError( + f"{self} received an initial learning rate " + f"that was lower than the minimum learning rate.") + + if self.constant_steps is None or self.constant_steps == 0: + new_lrs = [ + _cosine_annealing( + initial_lr=initial_lr, + step=step - self.warmup_steps, + max_steps=self.max_steps - self.warmup_steps, + min_lr=self.min_lr, + ) for initial_lr in self.base_lrs + ] + else: + new_lrs = self._get_linear_warmup_with_cosine_annealing_lr(step) + return new_lrs + + def _get_warmup_lr(self, step): + if self.constant_steps is None or self.constant_steps == 0: + return super()._get_warmup_lr(step) + else: + # Use linear warmup for the initial part. + return self._get_linear_warmup_with_cosine_annealing_lr(step) + + def _get_constant_lr(self, step): + # Only called when `constant_steps` > 0. + return self._get_linear_warmup_with_cosine_annealing_lr(step) + + def _get_linear_warmup_with_cosine_annealing_lr(self, step): + # Cosine Schedule for Megatron LM, + # slightly different warmup schedule + constant LR at the end. + new_lrs = [ + _linear_warmup_with_cosine_annealing( + max_lr=self.base_lrs[0], + warmup_steps=self.warmup_steps, + step=step, + decay_steps=self.decay_steps, + min_lr=self.min_lr, + ) for _ in self.base_lrs + ] + return new_lrs + + +class NoamAnnealing(_LRScheduler): + + def __init__(self, + optimizer, + *, + d_model, + warmup_steps=None, + warmup_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1): + self._normalize = d_model**(-0.5) + assert not (warmup_steps is not None and warmup_ratio is not None), \ + "Either use particular number of step or ratio" + assert warmup_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = max(1, self.last_epoch) + + for initial_lr in self.base_lrs: + if initial_lr < self.min_lr: + raise ValueError( + f"{self} received an initial learning rate " + f"that was lower than the minimum learning rate.") + + new_lrs = [ + self._noam_annealing(initial_lr=initial_lr, step=step) + for initial_lr in self.base_lrs + ] + return new_lrs + + def _noam_annealing(self, initial_lr, step): + if self.warmup_steps > 0: + mult = self._normalize * min(step**(-0.5), + step * (self.warmup_steps**(-1.5))) + else: + mult = self._normalize * step**(-0.5) + + out_lr = initial_lr * mult + if step > self.warmup_steps: + out_lr = max(out_lr, self.min_lr) + return out_lr + + +class NoamHoldAnnealing(WarmupHoldPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + decay_rate=0.5, + min_lr=0.0, + last_epoch=-1, + **kwargs): + """ + From Nemo: + Implementation of the Noam Hold Annealing policy + from the SqueezeFormer paper. + + Unlike NoamAnnealing, the peak learning rate + can be explicitly set for this scheduler. + The schedule first performs linear warmup, + then holds the peak LR, then decays with some schedule for + the remainder of the steps. + Therefore the min-lr is still dependent + on the hyper parameters selected. + + It's schedule is determined by three factors- + + Warmup Steps: Initial stage, where linear warmup + occurs uptil the peak LR is reached. Unlike NoamAnnealing, + the peak LR is explicitly stated here instead of a scaling factor. + + Hold Steps: Intermediate stage, where the peak LR + is maintained for some number of steps. In this region, + the high peak LR allows the model to converge faster + if training is stable. However the high LR + may also cause instability during training. + Should usually be a significant fraction of training + steps (around 30-40% of the entire training steps). + + Decay Steps: Final stage, where the LR rapidly decays + with some scaling rate (set by decay rate). + To attain Noam decay, use 0.5, + for Squeezeformer recommended decay, use 1.0. + The fast decay after prolonged high LR during + hold phase allows for rapid convergence. + + References: + - [Squeezeformer: + An Efficient Transformer for Automatic Speech Recognition] + (https://arxiv.org/abs/2206.00888) + + Args: + optimizer: Pytorch compatible Optimizer object. + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + hold_steps: Number of training steps to + hold the learning rate after warm up + hold_ratio: Ratio of hold steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + decay_rate: Float value describing the polynomial decay + after the hold period. Default value + of 0.5 corresponds to Noam decay. + min_lr: Minimum learning rate. + """ + self.decay_rate = decay_rate + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + if self.warmup_steps is None or self.warmup_steps == 0: + raise ValueError( + "Noam scheduler cannot be used without warmup steps") + + if self.hold_steps > 0: + hold_steps = self.hold_steps - self.warmup_steps + else: + hold_steps = 0 + + new_lrs = [ + _noam_hold_annealing( + initial_lr, + step=step, + warmup_steps=self.warmup_steps, + hold_steps=hold_steps, + decay_rate=self.decay_rate, + min_lr=self.min_lr, + ) for initial_lr in self.base_lrs + ] + return new_lrs + + def set_step(self, step: int): + self.last_epoch = step + + +class ConstantLR(_LRScheduler): + """The ConstantLR scheduler + + This scheduler keeps a constant lr + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + ): + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer) + + def get_lr(self): + return self.base_lrs + + def set_step(self, step: int): + self.last_epoch = step diff --git a/src/voice/cosyvoice-src/cosyvoice/utils/train_utils.py b/src/voice/cosyvoice-src/cosyvoice/utils/train_utils.py new file mode 100644 index 0000000..2ab5ebe --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/utils/train_utils.py @@ -0,0 +1,367 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) +# 2023 Horizon Inc. (authors: Xingchen Song) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import torch +import json +import re +import datetime +import yaml + +import deepspeed +import torch.optim as optim +import torch.distributed as dist + +from torch.utils.tensorboard import SummaryWriter +from torch.utils.data import DataLoader +from torch.nn.utils import clip_grad_norm_ + +from deepspeed.runtime.zero.stage_1_and_2 import estimate_zero2_model_states_mem_needs_all_live + +from cosyvoice.dataset.dataset import Dataset +from cosyvoice.utils.scheduler import WarmupLR, NoamHoldAnnealing, ConstantLR + + +def init_distributed(args): + world_size = int(os.environ.get('WORLD_SIZE', 1)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + rank = int(os.environ.get('RANK', 0)) + logging.info('training on multiple gpus, this gpu {}'.format(local_rank) + + ', rank {}, world_size {}'.format(rank, world_size)) + if args.train_engine == 'torch_ddp': + torch.cuda.set_device(local_rank) + dist.init_process_group(args.dist_backend) + else: + deepspeed.init_distributed(dist_backend=args.dist_backend) + return world_size, local_rank, rank + + +def init_dataset_and_dataloader(args, configs, gan, dpo): + data_pipeline = configs['data_pipeline_gan'] if gan is True else configs['data_pipeline'] + train_dataset = Dataset(args.train_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=True, partition=True) + cv_dataset = Dataset(args.cv_data, data_pipeline=data_pipeline, mode='dev', gan=gan, dpo=dpo, shuffle=False, partition=False) + + # do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts + train_data_loader = DataLoader(train_dataset, + batch_size=None, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + prefetch_factor=args.prefetch) + cv_data_loader = DataLoader(cv_dataset, + batch_size=None, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + prefetch_factor=args.prefetch) + return train_dataset, cv_dataset, train_data_loader, cv_data_loader + + +def check_modify_and_save_config(args, configs): + if args.train_engine == "torch_ddp": + configs['train_conf']["dtype"] = 'bf16' if args.use_amp is True else 'fp32' + else: + with open(args.deepspeed_config, 'r') as fin: + ds_configs = json.load(fin) + if "fp16" in ds_configs and ds_configs["fp16"]["enabled"]: + configs['train_conf']["dtype"] = "fp16" + elif "bf16" in ds_configs and ds_configs["bf16"]["enabled"]: + configs['train_conf']["dtype"] = "bf16" + else: + configs['train_conf']["dtype"] = "fp32" + assert ds_configs["train_micro_batch_size_per_gpu"] == 1 + # if use deepspeed, override ddp config + configs['train_conf']['save_per_step'] = int(configs['train_conf']['save_per_step'] * + configs['train_conf']['accum_grad'] / ds_configs["gradient_accumulation_steps"]) + configs['train_conf']['accum_grad'] = ds_configs["gradient_accumulation_steps"] + configs['train_conf']['grad_clip'] = ds_configs["gradient_clipping"] + configs['train_conf']['log_interval'] = ds_configs["steps_per_print"] + return configs + + +def wrap_cuda_model(args, model): + local_world_size = int(os.environ.get('LOCAL_WORLD_SIZE', 1)) + world_size = int(os.environ.get('WORLD_SIZE', 1)) + if args.train_engine == "torch_ddp": # native pytorch ddp + assert (torch.cuda.is_available()) + model.cuda() + model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True) + else: + if int(os.environ.get('RANK', 0)) == 0: + logging.info("Estimating model states memory needs (zero2)...") + estimate_zero2_model_states_mem_needs_all_live( + model, + num_gpus_per_node=local_world_size, + num_nodes=world_size // local_world_size) + return model + + +def init_optimizer_and_scheduler(args, configs, model, gan): + if gan is False: + if configs['train_conf']['optim'] == 'adam': + optimizer = optim.Adam(model.parameters(), **configs['train_conf']['optim_conf']) + elif configs['train_conf']['optim'] == 'adamw': + optimizer = optim.AdamW(model.parameters(), **configs['train_conf']['optim_conf']) + else: + raise ValueError("unknown optimizer: " + configs['train_conf']) + + if configs['train_conf']['scheduler'] == 'warmuplr': + scheduler_type = WarmupLR + scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing': + scheduler_type = NoamHoldAnnealing + scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'constantlr': + scheduler_type = ConstantLR + scheduler = ConstantLR(optimizer) + else: + raise ValueError("unknown scheduler: " + configs['train_conf']) + + # use deepspeed optimizer for speedup + if args.train_engine == "deepspeed": + def scheduler(opt): + return scheduler_type(opt, **configs['train_conf']['scheduler_conf']) + model, optimizer, _, scheduler = deepspeed.initialize( + args=args, + model=model, + optimizer=None, + lr_scheduler=scheduler, + model_parameters=model.parameters()) + + optimizer_d, scheduler_d = None, None + + else: + # currently we wrap generator and discriminator in one model, so we cannot use deepspeed + if configs['train_conf']['optim'] == 'adam': + optimizer = optim.Adam(model.module.generator.parameters(), **configs['train_conf']['optim_conf']) + elif configs['train_conf']['optim'] == 'adamw': + optimizer = optim.AdamW(model.module.generator.parameters(), **configs['train_conf']['optim_conf']) + else: + raise ValueError("unknown optimizer: " + configs['train_conf']) + + if configs['train_conf']['scheduler'] == 'warmuplr': + scheduler_type = WarmupLR + scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing': + scheduler_type = NoamHoldAnnealing + scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'constantlr': + scheduler_type = ConstantLR + scheduler = ConstantLR(optimizer) + else: + raise ValueError("unknown scheduler: " + configs['train_conf']) + + if configs['train_conf']['optim_d'] == 'adam': + optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf_d']) + elif configs['train_conf']['optim_d'] == 'adamw': + optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf_d']) + else: + raise ValueError("unknown optimizer: " + configs['train_conf']) + + if configs['train_conf']['scheduler_d'] == 'warmuplr': + scheduler_type = WarmupLR + scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_d']) + elif configs['train_conf']['scheduler_d'] == 'NoamHoldAnnealing': + scheduler_type = NoamHoldAnnealing + scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_d']) + elif configs['train_conf']['scheduler'] == 'constantlr': + scheduler_type = ConstantLR + scheduler_d = ConstantLR(optimizer_d) + else: + raise ValueError("unknown scheduler: " + configs['train_conf']) + return model, optimizer, scheduler, optimizer_d, scheduler_d + + +def init_summarywriter(args): + writer = None + if int(os.environ.get('RANK', 0)) == 0: + os.makedirs(args.model_dir, exist_ok=True) + writer = SummaryWriter(args.tensorboard_dir) + return writer + + +def save_model(model, model_name, info_dict): + rank = int(os.environ.get('RANK', 0)) + model_dir = info_dict["model_dir"] + save_model_path = os.path.join(model_dir, '{}.pt'.format(model_name)) + + if info_dict["train_engine"] == "torch_ddp": + if rank == 0: + torch.save({**model.module.state_dict(), 'epoch': info_dict['epoch'], 'step': info_dict['step']}, save_model_path) + else: + with torch.no_grad(): + model.save_checkpoint(save_dir=model_dir, + tag=model_name, + client_state=info_dict) + if rank == 0: + info_path = re.sub('.pt$', '.yaml', save_model_path) + info_dict['save_time'] = datetime.datetime.now().strftime('%d/%m/%Y %H:%M:%S') + with open(info_path, 'w') as fout: + data = yaml.dump(info_dict) + fout.write(data) + logging.info('[Rank {}] Checkpoint: save to checkpoint {}'.format(rank, save_model_path)) + + +def cosyvoice_join(group_join, info_dict): + world_size = int(os.environ.get('WORLD_SIZE', 1)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + rank = int(os.environ.get('RANK', 0)) + + if info_dict["batch_idx"] != 0: + # we try to join all rank in both ddp and deepspeed mode, in case different rank has different lr + try: + dist.monitored_barrier(group=group_join, + timeout=group_join.options._timeout) + return False + except RuntimeError as e: + logging.info("Detected uneven workload distribution: {}\n".format(e) + + "Break current worker to manually join all workers, " + + "world_size {}, current rank {}, current local_rank {}\n". + format(world_size, rank, local_rank)) + return True + else: + return False + + +def batch_forward(model, batch, scaler, info_dict, ref_model=None, dpo_loss=None): + device = int(os.environ.get('LOCAL_RANK', 0)) + + dtype = info_dict["dtype"] + if dtype == "fp16": + dtype = torch.float16 + elif dtype == "bf16": + dtype = torch.bfloat16 + else: # fp32 + dtype = torch.float32 + + if info_dict['train_engine'] == 'torch_ddp': + autocast = torch.cuda.amp.autocast(enabled=scaler is not None, dtype=dtype) + else: + autocast = torch.cuda.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False) + + with autocast: + info_dict['loss_dict'] = model(batch, device) + if ref_model is not None and dpo_loss is not None: + chosen_logps = info_dict['loss_dict']["chosen_logps"] + rejected_logps = info_dict['loss_dict']["rejected_logps"] + sft_loss = info_dict['loss_dict']['loss'] + with torch.no_grad(): + ref_loss_dict = ref_model(batch, device) + reference_chosen_logps = ref_loss_dict["chosen_logps"] + reference_rejected_logps = ref_loss_dict["rejected_logps"] + preference_loss, chosen_reward, reject_reward = dpo_loss( + chosen_logps, rejected_logps, reference_chosen_logps, reference_rejected_logps + ) + dpo_acc = (chosen_reward > reject_reward).float().mean() + info_dict['loss_dict']["loss"] = preference_loss + sft_loss + info_dict['loss_dict']["sft_loss"] = sft_loss + info_dict['loss_dict']["dpo_loss"] = preference_loss + info_dict['loss_dict']["dpo_acc"] = dpo_acc + info_dict['loss_dict']["chosen_reward"] = chosen_reward.mean() + info_dict['loss_dict']["reject_reward"] = reject_reward.mean() + return info_dict + + +def batch_backward(model, scaler, info_dict): + if info_dict["train_engine"] == "deepspeed": + scaled_loss = model.backward(info_dict['loss_dict']['loss']) + else: + scaled_loss = info_dict['loss_dict']['loss'] / info_dict['accum_grad'] + if scaler is not None: + scaler.scale(scaled_loss).backward() + else: + scaled_loss.backward() + + info_dict['loss_dict']['loss'] = scaled_loss + return info_dict + + +def update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict): + grad_norm = 0.0 + if info_dict['train_engine'] == "deepspeed": + info_dict["is_gradient_accumulation_boundary"] = model.is_gradient_accumulation_boundary() + model.step() + grad_norm = model.get_global_grad_norm() + elif (info_dict['batch_idx'] + 1) % info_dict["accum_grad"] == 0: + # Use mixed precision training + if scaler is not None: + scaler.unscale_(optimizer) + grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip']) + # We don't check grad here since that if the gradient + # has inf/nan values, scaler.step will skip + # optimizer.step(). + if torch.isfinite(grad_norm): + scaler.step(optimizer) + else: + logging.warning('get infinite grad_norm, check your code/data if it appears frequently') + scaler.update() + else: + grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip']) + if torch.isfinite(grad_norm): + optimizer.step() + else: + logging.warning('get infinite grad_norm, check your code/data if it appears frequently') + optimizer.zero_grad() + scheduler.step() + info_dict["lr"] = optimizer.param_groups[0]['lr'] + info_dict["grad_norm"] = grad_norm + return info_dict + + +def log_per_step(writer, info_dict): + tag = info_dict["tag"] + epoch = info_dict.get('epoch', 0) + step = info_dict["step"] + batch_idx = info_dict["batch_idx"] + loss_dict = info_dict['loss_dict'] + rank = int(os.environ.get('RANK', 0)) + + # only rank 0 write to tensorboard to avoid multi-process write + if writer is not None: + if (info_dict['train_engine'] == 'deepspeed' and info_dict['is_gradient_accumulation_boundary'] is True) or \ + (info_dict['train_engine'] == 'torch_ddp' and (info_dict['batch_idx'] + 1) % info_dict['accum_grad'] == 0): + for k in ['epoch', 'lr', 'grad_norm']: + writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1) + for k, v in loss_dict.items(): + writer.add_scalar('{}/{}'.format(tag, k), v, step + 1) + + # TRAIN & CV, Shell log (stdout) + if (info_dict['batch_idx'] + 1) % info_dict['log_interval'] == 0: + log_str = '{} Batch {}/{} '.format(tag, epoch, batch_idx + 1) + for name, value in loss_dict.items(): + log_str += '{} {:.6f} '.format(name, value) + if tag == "TRAIN": + log_str += 'lr {:.8f} grad_norm {:.6f}'.format( + info_dict["lr"], info_dict['grad_norm']) + log_str += ' rank {}'.format(rank) + logging.debug(log_str) + + +def log_per_save(writer, info_dict): + tag = info_dict["tag"] + epoch = info_dict["epoch"] + step = info_dict["step"] + loss_dict = info_dict["loss_dict"] + lr = info_dict['lr'] + rank = int(os.environ.get('RANK', 0)) + logging.info( + 'Epoch {} Step {} CV info lr {} {} rank {}'.format( + epoch, step + 1, lr, rank, ' '.join(['{} {}'.format(k, v) for k, v in loss_dict.items()]))) + + if writer is not None: + for k in ['epoch', 'lr']: + writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1) + for k, v in loss_dict.items(): + writer.add_scalar('{}/{}'.format(tag, k), v, step + 1) diff --git a/src/voice/cosyvoice-src/cosyvoice/vllm/cosyvoice2.py b/src/voice/cosyvoice-src/cosyvoice/vllm/cosyvoice2.py new file mode 100644 index 0000000..fff0543 --- /dev/null +++ b/src/voice/cosyvoice-src/cosyvoice/vllm/cosyvoice2.py @@ -0,0 +1,116 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Adapted from +# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py +# Copyright 2024 The Qwen team. +# Copyright 2023 The vLLM team. +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only Qwen2 model compatible with HuggingFace weights.""" +from typing import Optional +from packaging.version import parse as vparse +import vllm + +# vLLM-0.11.0+ only support V1 engine +VLLM_V1_ENGINE_ONLY: bool = vparse(vllm.__version__) >= vparse("0.11.0") +if VLLM_V1_ENGINE_ONLY: + from vllm.v1.sample.metadata import SamplingMetadata + +from vllm.model_executor.models.qwen2 import * + + +class CosyVoice2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP): + packed_modules_mapping = { + "qkv_proj": [ + "q_proj", + "k_proj", + "v_proj", + ], + "gate_up_proj": [ + "gate_proj", + "up_proj", + ], + } + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + lora_config = vllm_config.lora_config + + self.config = config + self.lora_config = lora_config + + self.quant_config = quant_config + self.model = Qwen2Model(vllm_config=vllm_config, + prefix=maybe_prefix(prefix, "model")) + + if get_pp_group().is_last_rank: + if config.tie_word_embeddings: + self.lm_head = self.model.embed_tokens + else: + self.lm_head = ParallelLMHead(config.vocab_size, + config.hidden_size, + True, + quant_config=quant_config, + prefix=maybe_prefix( + prefix, "lm_head")) + else: + self.lm_head = PPMissingLayer() + + self.logits_processor = LogitsProcessor(config.vocab_size) + + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + hidden_states = self.model(input_ids, positions, intermediate_tensors, + inputs_embeds) + return hidden_states + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: Optional[SamplingMetadata] = None, + ) -> Optional[torch.Tensor]: + if VLLM_V1_ENGINE_ONLY: + logits = self.logits_processor(self.lm_head, hidden_states, + self.lm_head.bias) + else: + logits = self.logits_processor(self.lm_head, hidden_states, + sampling_metadata, self.lm_head.bias) + return logits + + def load_weights(self, weights: Iterable[tuple[str, + torch.Tensor]]) -> set[str]: + loader = AutoWeightsLoader( + self, + skip_prefixes=(["lm_head."] + if self.config.tie_word_embeddings else None), + ) + return loader.load_weights(weights) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.env.example b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.env.example new file mode 100644 index 0000000..a790e32 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.env.example @@ -0,0 +1,6 @@ +# example of file for storing private and user specific environment variables, like keys or system paths +# rename it to ".env" (excluded from version control by default) +# .env is loaded by train.py automatically +# hydra allows you to reference variables in .yaml configs with special syntax: ${oc.env:MY_VAR} + +MY_VAR="/home/user/my/system/path" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/PULL_REQUEST_TEMPLATE.md b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..410bcd8 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,22 @@ +## What does this PR do? + + + +Fixes #\ + +## Before submitting + +- [ ] Did you make sure **title is self-explanatory** and **the description concisely explains the PR**? +- [ ] Did you make sure your **PR does only one thing**, instead of bundling different changes together? +- [ ] Did you list all the **breaking changes** introduced by this pull request? +- [ ] Did you **test your PR locally** with `pytest` command? +- [ ] Did you **run pre-commit hooks** with `pre-commit run -a` command? + +## Did you have fun? + +Make sure you had fun coding 🙃 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/codecov.yml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/codecov.yml new file mode 100644 index 0000000..c66853c --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/codecov.yml @@ -0,0 +1,15 @@ +coverage: + status: + # measures overall project coverage + project: + default: + threshold: 100% # how much decrease in coverage is needed to not consider success + + # measures PR or single commit coverage + patch: + default: + threshold: 100% # how much decrease in coverage is needed to not consider success + + + # project: off + # patch: off diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/dependabot.yml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/dependabot.yml new file mode 100644 index 0000000..b19ccab --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/dependabot.yml @@ -0,0 +1,17 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +version: 2 +updates: + - package-ecosystem: "pip" # See documentation for possible values + directory: "/" # Location of package manifests + target-branch: "dev" + schedule: + interval: "daily" + ignore: + - dependency-name: "pytorch-lightning" + update-types: ["version-update:semver-patch"] + - dependency-name: "torchmetrics" + update-types: ["version-update:semver-patch"] diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/release-drafter.yml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/release-drafter.yml new file mode 100644 index 0000000..59af159 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.github/release-drafter.yml @@ -0,0 +1,44 @@ +name-template: "v$RESOLVED_VERSION" +tag-template: "v$RESOLVED_VERSION" + +categories: + - title: "🚀 Features" + labels: + - "feature" + - "enhancement" + - title: "🐛 Bug Fixes" + labels: + - "fix" + - "bugfix" + - "bug" + - title: "🧹 Maintenance" + labels: + - "maintenance" + - "dependencies" + - "refactoring" + - "cosmetic" + - "chore" + - title: "📝️ Documentation" + labels: + - "documentation" + - "docs" + +change-template: "- $TITLE @$AUTHOR (#$NUMBER)" +change-title-escapes: '\<*_&' # You can add # and @ to disable mentions + +version-resolver: + major: + labels: + - "major" + minor: + labels: + - "minor" + patch: + labels: + - "patch" + default: patch + +template: | + ## Changes + + $CHANGES diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.gitignore b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.gitignore new file mode 100644 index 0000000..f353a3c --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.gitignore @@ -0,0 +1,164 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +### VisualStudioCode +.vscode/* +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +*.code-workspace +**/.vscode + +# JetBrains +.idea/ + +# Data & Models +*.h5 +*.tar +*.tar.gz + +# Lightning-Hydra-Template +configs/local/default.yaml +/data/ +/logs/ +.env + +# Aim logging +.aim + +# Cython complied files +matcha/utils/monotonic_align/core.c + +# Ignoring hifigan checkpoint +generator_v1 +g_02500000 +gradio_cached_examples/ +synth_output/ +/data diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.pre-commit-config.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.pre-commit-config.yaml new file mode 100644 index 0000000..7618671 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.pre-commit-config.yaml @@ -0,0 +1,59 @@ +default_language_version: + python: python3.11 + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + # list of supported hooks: https://pre-commit.com/hooks.html + - id: trailing-whitespace + - id: end-of-file-fixer + # - id: check-docstring-first + - id: check-yaml + - id: debug-statements + - id: detect-private-key + - id: check-toml + - id: check-case-conflict + - id: check-added-large-files + + # python code formatting + - repo: https://github.com/psf/black + rev: 23.12.1 + hooks: + - id: black + args: [--line-length, "120"] + + # python import sorting + - repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + args: ["--profile", "black", "--filter-files"] + + # python upgrading syntax to newer version + - repo: https://github.com/asottile/pyupgrade + rev: v3.15.0 + hooks: + - id: pyupgrade + args: [--py38-plus] + + # python check (PEP8), programming errors and code complexity + - repo: https://github.com/PyCQA/flake8 + rev: 7.0.0 + hooks: + - id: flake8 + args: + [ + "--max-line-length", "120", + "--extend-ignore", + "E203,E402,E501,F401,F841,RST2,RST301", + "--exclude", + "logs/*,data/*,matcha/hifigan/*", + ] + additional_dependencies: [flake8-rst-docstrings==0.3.0] + + # pylint + - repo: https://github.com/pycqa/pylint + rev: v3.0.3 + hooks: + - id: pylint diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.project-root b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.project-root new file mode 100644 index 0000000..63eab77 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.project-root @@ -0,0 +1,2 @@ +# this file is required for inferring the project root directory +# do not delete diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/.pylintrc b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.pylintrc new file mode 100644 index 0000000..9628641 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/.pylintrc @@ -0,0 +1,525 @@ +[MASTER] + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. +extension-pkg-whitelist= + +# Add files or directories to the blacklist. They should be base names, not +# paths. +ignore=CVS + +# Add files or directories matching the regex patterns to the blacklist. The +# regex matches against base names, not paths. +ignore-patterns= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use. +jobs=1 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# List of plugins (as comma separated values of python modules names) to load, +# usually to register additional checkers. +load-plugins= + +# Pickle collected data for later comparisons. +persistent=yes + +# Specify a configuration file. +#rcfile= + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED. +confidence= + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once). You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use "--disable=all --enable=classes +# --disable=W". +disable=missing-docstring, + too-many-public-methods, + too-many-lines, + bare-except, + ## for avoiding weird p3.6 CI linter error + ## TODO: see later if we can remove this + assigning-non-slot, + unsupported-assignment-operation, + ## end + line-too-long, + fixme, + wrong-import-order, + ungrouped-imports, + wrong-import-position, + import-error, + invalid-name, + too-many-instance-attributes, + arguments-differ, + arguments-renamed, + no-name-in-module, + no-member, + unsubscriptable-object, + raw-checker-failed, + bad-inline-option, + locally-disabled, + file-ignored, + suppressed-message, + useless-suppression, + deprecated-pragma, + use-symbolic-message-instead, + useless-object-inheritance, + too-few-public-methods, + too-many-branches, + too-many-arguments, + too-many-locals, + too-many-statements, + duplicate-code, + not-callable, + import-outside-toplevel, + logging-fstring-interpolation, + logging-not-lazy, + unused-argument, + no-else-return, + chained-comparison, + redefined-outer-name + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +enable=c-extension-no-member + + +[REPORTS] + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details. +#msg-template= + +# Set the output format. Available formats are text, parseable, colorized, json +# and msvs (visual studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages. +reports=no + +# Activate the evaluation score. +score=yes + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit + + +[LOGGING] + +# Format style used to check logging format string. `old` means using % +# formatting, while `new` is for `{}` formatting. +logging-format-style=old + +# Logging modules to check that the string format arguments are in logging +# function parameter format. +logging-modules=logging + + +[SPELLING] + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=4 + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package.. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME, + XXX, + TODO + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members=numpy.*,torch.* + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis. It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + + +[VARIABLES] + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_, + _cb + +# A regular expression matching the name of dummy variables (i.e. expected to +# not be used). +dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore. +ignored-argument-names=_.*|^ignored_|^unused_ + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io + + +[FORMAT] + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Maximum number of characters on a single line. +max-line-length=120 + +# Maximum number of lines in a module. +max-module-lines=1000 + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + + +[SIMILARITIES] + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + +# Minimum lines number of a similarity. +min-similarity-lines=4 + + +[BASIC] + +# Naming style matching correct argument names. +argument-naming-style=snake_case + +# Regular expression matching correct argument names. Overrides argument- +# naming-style. +argument-rgx=[a-z_][a-z0-9_]{0,30}$ + +# Naming style matching correct attribute names. +attr-naming-style=snake_case + +# Regular expression matching correct attribute names. Overrides attr-naming- +# style. +#attr-rgx= + +# Bad variable names which should always be refused, separated by a comma. +bad-names= + +# Naming style matching correct class attribute names. +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names. Overrides class- +# attribute-naming-style. +#class-attribute-rgx= + +# Naming style matching correct class names. +class-naming-style=PascalCase + +# Regular expression matching correct class names. Overrides class-naming- +# style. +#class-rgx= + +# Naming style matching correct constant names. +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names. Overrides const-naming- +# style. +#const-rgx= + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + +# Naming style matching correct function names. +function-naming-style=snake_case + +# Regular expression matching correct function names. Overrides function- +# naming-style. +#function-rgx= + +# Good variable names which should always be accepted, separated by a comma. +good-names=i, + j, + k, + x, + ex, + Run, + _ + +# Include a hint for the correct naming format with invalid-name. +include-naming-hint=no + +# Naming style matching correct inline iteration names. +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names. Overrides +# inlinevar-naming-style. +#inlinevar-rgx= + +# Naming style matching correct method names. +method-naming-style=snake_case + +# Regular expression matching correct method names. Overrides method-naming- +# style. +#method-rgx= + +# Naming style matching correct module names. +module-naming-style=snake_case + +# Regular expression matching correct module names. Overrides module-naming- +# style. +#module-rgx= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=^_ + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +# These decorators are taken in consideration only for invalid-name. +property-classes=abc.abstractproperty + +# Naming style matching correct variable names. +variable-naming-style=snake_case + +# Regular expression matching correct variable names. Overrides variable- +# naming-style. +variable-rgx=[a-z_][a-z0-9_]{0,30}$ + + +[STRING] + +# This flag controls whether the implicit-str-concat-in-sequence should +# generate a warning on implicit string concatenation in sequences defined over +# several lines. +check-str-concat-over-line-jumps=no + + +[IMPORTS] + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Deprecated modules which should not be used, separated by a comma. +deprecated-modules=optparse,tkinter.tix + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled). +ext-import-graph= + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled). +import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled). +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + + +[CLASSES] + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict, + _fields, + _replace, + _source, + _make + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=cls + + +[DESIGN] + +# Maximum number of arguments for function / method. +max-args=5 + +# Maximum number of attributes for a class (see R0902). +max-attributes=7 + +# Maximum number of boolean expressions in an if statement. +max-bool-expr=5 + +# Maximum number of branch for function / method body. +max-branches=12 + +# Maximum number of locals for function / method body. +max-locals=15 + +# Maximum number of parents for a class (see R0901). +max-parents=15 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=20 + +# Maximum number of return / yield for function / method body. +max-returns=6 + +# Maximum number of statements in function / method body. +max-statements=50 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "BaseException, Exception". +overgeneral-exceptions=builtins.BaseException, + builtins.Exception diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/LICENSE b/src/voice/cosyvoice-src/third_party/Matcha-TTS/LICENSE new file mode 100644 index 0000000..858018e --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Shivam Mehta + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/MANIFEST.in b/src/voice/cosyvoice-src/third_party/Matcha-TTS/MANIFEST.in new file mode 100644 index 0000000..c013140 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/MANIFEST.in @@ -0,0 +1,14 @@ +include README.md +include LICENSE.txt +include requirements.*.txt +include *.cff +include requirements.txt +include matcha/VERSION +recursive-include matcha *.json +recursive-include matcha *.html +recursive-include matcha *.png +recursive-include matcha *.md +recursive-include matcha *.py +recursive-include matcha *.pyx +recursive-exclude tests * +prune tests* diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/Makefile b/src/voice/cosyvoice-src/third_party/Matcha-TTS/Makefile new file mode 100644 index 0000000..4b523dd --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/Makefile @@ -0,0 +1,42 @@ + +help: ## Show help + @grep -E '^[.a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' + +clean: ## Clean autogenerated files + rm -rf dist + find . -type f -name "*.DS_Store" -ls -delete + find . | grep -E "(__pycache__|\.pyc|\.pyo)" | xargs rm -rf + find . | grep -E ".pytest_cache" | xargs rm -rf + find . | grep -E ".ipynb_checkpoints" | xargs rm -rf + rm -f .coverage + +clean-logs: ## Clean logs + rm -rf logs/** + +create-package: ## Create wheel and tar gz + rm -rf dist/ + python setup.py bdist_wheel --plat-name=manylinux1_x86_64 + python setup.py sdist + python -m twine upload dist/* --verbose --skip-existing + +format: ## Run pre-commit hooks + pre-commit run -a + +sync: ## Merge changes from main branch to your current branch + git pull + git pull origin main + +test: ## Run not slow tests + pytest -k "not slow" + +test-full: ## Run all tests + pytest + +train-ljspeech: ## Train the model + python matcha/train.py experiment=ljspeech + +train-ljspeech-min: ## Train the model with minimum memory + python matcha/train.py experiment=ljspeech_min_memory + +start_app: ## Start the app + python matcha/app.py diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/README.md b/src/voice/cosyvoice-src/third_party/Matcha-TTS/README.md new file mode 100644 index 0000000..a9bf8a5 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/README.md @@ -0,0 +1,315 @@ +
+ +# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching + +### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/) + +[![python](https://img.shields.io/badge/-Python_3.10-blue?logo=python&logoColor=white)](https://www.python.org/downloads/release/python-3100/) +[![pytorch](https://img.shields.io/badge/PyTorch_2.0+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/) +[![lightning](https://img.shields.io/badge/-Lightning_2.0+-792ee5?logo=pytorchlightning&logoColor=white)](https://pytorchlightning.ai/) +[![hydra](https://img.shields.io/badge/Config-Hydra_1.3-89b8cd)](https://hydra.cc/) +[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/) +[![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) +[![PyPI Downloads](https://static.pepy.tech/personalized-badge/matcha-tts?period=total&units=INTERNATIONAL_SYSTEM&left_color=BLACK&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/matcha-tts) +

+ +

+ +
+ +> This is the official code implementation of 🍵 Matcha-TTS [ICASSP 2024]. + +We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses [conditional flow matching](https://arxiv.org/abs/2210.02747) (similar to [rectified flows](https://arxiv.org/abs/2209.03003)) to speed up ODE-based speech synthesis. Our method: + +- Is probabilistic +- Has compact memory footprint +- Sounds highly natural +- Is very fast to synthesise from + +Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS) and read [our ICASSP 2024 paper](https://arxiv.org/abs/2309.03199) for more details. + +[Pre-trained models](https://drive.google.com/drive/folders/17C_gYgEHOxI5ZypcfE_k1piKCtyR0isJ?usp=sharing) will be automatically downloaded with the CLI or gradio interface. + +You can also [try 🍵 Matcha-TTS in your browser on HuggingFace 🤗 spaces](https://huggingface.co/spaces/shivammehta25/Matcha-TTS). + +## Teaser video + +[![Watch the video](https://img.youtube.com/vi/xmvJkz3bqw0/hqdefault.jpg)](https://youtu.be/xmvJkz3bqw0) + +## Installation + +1. Create an environment (suggested but optional) + +``` +conda create -n matcha-tts python=3.10 -y +conda activate matcha-tts +``` + +2. Install Matcha TTS using pip or from source + +```bash +pip install matcha-tts +``` + +from source + +```bash +pip install git+https://github.com/shivammehta25/Matcha-TTS.git +cd Matcha-TTS +pip install -e . +``` + +3. Run CLI / gradio app / jupyter notebook + +```bash +# This will download the required models +matcha-tts --text "" +``` + +or + +```bash +matcha-tts-app +``` + +or open `synthesis.ipynb` on jupyter notebook + +### CLI Arguments + +- To synthesise from given text, run: + +```bash +matcha-tts --text "" +``` + +- To synthesise from a file, run: + +```bash +matcha-tts --file +``` + +- To batch synthesise from a file, run: + +```bash +matcha-tts --file --batched +``` + +Additional arguments + +- Speaking rate + +```bash +matcha-tts --text "" --speaking_rate 1.0 +``` + +- Sampling temperature + +```bash +matcha-tts --text "" --temperature 0.667 +``` + +- Euler ODE solver steps + +```bash +matcha-tts --text "" --steps 10 +``` + +## Train with your own dataset + +Let's assume we are training with LJ Speech + +1. Download the dataset from [here](https://keithito.com/LJ-Speech-Dataset/), extract it to `data/LJSpeech-1.1`, and prepare the file lists to point to the extracted data like for [item 5 in the setup of the NVIDIA Tacotron 2 repo](https://github.com/NVIDIA/tacotron2#setup). + +2. Clone and enter the Matcha-TTS repository + +```bash +git clone https://github.com/shivammehta25/Matcha-TTS.git +cd Matcha-TTS +``` + +3. Install the package from source + +```bash +pip install -e . +``` + +4. Go to `configs/data/ljspeech.yaml` and change + +```yaml +train_filelist_path: data/filelists/ljs_audio_text_train_filelist.txt +valid_filelist_path: data/filelists/ljs_audio_text_val_filelist.txt +``` + +5. Generate normalisation statistics with the yaml file of dataset configuration + +```bash +matcha-data-stats -i ljspeech.yaml +# Output: +#{'mel_mean': -5.53662231756592, 'mel_std': 2.1161014277038574} +``` + +Update these values in `configs/data/ljspeech.yaml` under `data_statistics` key. + +```bash +data_statistics: # Computed for ljspeech dataset + mel_mean: -5.536622 + mel_std: 2.116101 +``` + +to the paths of your train and validation filelists. + +6. Run the training script + +```bash +make train-ljspeech +``` + +or + +```bash +python matcha/train.py experiment=ljspeech +``` + +- for a minimum memory run + +```bash +python matcha/train.py experiment=ljspeech_min_memory +``` + +- for multi-gpu training, run + +```bash +python matcha/train.py experiment=ljspeech trainer.devices=[0,1] +``` + +7. Synthesise from the custom trained model + +```bash +matcha-tts --text "" --checkpoint_path +``` + +## ONNX support + +> Special thanks to [@mush42](https://github.com/mush42) for implementing ONNX export and inference support. + +It is possible to export Matcha checkpoints to [ONNX](https://onnx.ai/), and run inference on the exported ONNX graph. + +### ONNX export + +To export a checkpoint to ONNX, first install ONNX with + +```bash +pip install onnx +``` + +then run the following: + +```bash +python3 -m matcha.onnx.export matcha.ckpt model.onnx --n-timesteps 5 +``` + +Optionally, the ONNX exporter accepts **vocoder-name** and **vocoder-checkpoint** arguments. This enables you to embed the vocoder in the exported graph and generate waveforms in a single run (similar to end-to-end TTS systems). + +**Note** that `n_timesteps` is treated as a hyper-parameter rather than a model input. This means you should specify it during export (not during inference). If not specified, `n_timesteps` is set to **5**. + +**Important**: for now, torch>=2.1.0 is needed for export since the `scaled_product_attention` operator is not exportable in older versions. Until the final version is released, those who want to export their models must install torch>=2.1.0 manually as a pre-release. + +### ONNX Inference + +To run inference on the exported model, first install `onnxruntime` using + +```bash +pip install onnxruntime +pip install onnxruntime-gpu # for GPU inference +``` + +then use the following: + +```bash +python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs +``` + +You can also control synthesis parameters: + +```bash +python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --temperature 0.4 --speaking_rate 0.9 --spk 0 +``` + +To run inference on **GPU**, make sure to install **onnxruntime-gpu** package, and then pass `--gpu` to the inference command: + +```bash +python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --gpu +``` + +If you exported only Matcha to ONNX, this will write mel-spectrogram as graphs and `numpy` arrays to the output directory. +If you embedded the vocoder in the exported graph, this will write `.wav` audio files to the output directory. + +If you exported only Matcha to ONNX, and you want to run a full TTS pipeline, you can pass a path to a vocoder model in `ONNX` format: + +```bash +python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --vocoder hifigan.small.onnx +``` + +This will write `.wav` audio files to the output directory. + +## Extract phoneme alignments from Matcha-TTS + +If the dataset is structured as + +```bash +data/ +└── LJSpeech-1.1 + ├── metadata.csv + ├── README + ├── test.txt + ├── train.txt + ├── val.txt + └── wavs +``` +Then you can extract the phoneme level alignments from a Trained Matcha-TTS model using: +```bash +python matcha/utils/get_durations_from_trained_model.py -i dataset_yaml -c +``` +Example: +```bash +python matcha/utils/get_durations_from_trained_model.py -i ljspeech.yaml -c matcha_ljspeech.ckpt +``` +or simply: +```bash +matcha-tts-get-durations -i ljspeech.yaml -c matcha_ljspeech.ckpt +``` +--- +## Train using extracted alignments + +In the datasetconfig turn on load duration. +Example: `ljspeech.yaml` +``` +load_durations: True +``` +or see an examples in configs/experiment/ljspeech_from_durations.yaml + + +## Citation information + +If you use our code or otherwise find this work useful, please cite our paper: + +```text +@inproceedings{mehta2024matcha, + title={Matcha-{TTS}: A fast {TTS} architecture with conditional flow matching}, + author={Mehta, Shivam and Tu, Ruibo and Beskow, Jonas and Sz{\'e}kely, {\'E}va and Henter, Gustav Eje}, + booktitle={Proc. ICASSP}, + year={2024} +} +``` + +## Acknowledgements + +Since this code uses [Lightning-Hydra-Template](https://github.com/ashleve/lightning-hydra-template), you have all the powers that come with it. + +Other source code we would like to acknowledge: + +- [Coqui-TTS](https://github.com/coqui-ai/TTS/tree/dev): For helping me figure out how to make cython binaries pip installable and encouragement +- [Hugging Face Diffusers](https://huggingface.co/): For their awesome diffusers library and its components +- [Grad-TTS](https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS): For the monotonic alignment search source code +- [torchdyn](https://github.com/DiffEqML/torchdyn): Useful for trying other ODE solvers during research and development +- [labml.ai](https://nn.labml.ai/transformers/rope/index.html): For the RoPE implementation diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/__init__.py new file mode 100644 index 0000000..56bf7f4 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/__init__.py @@ -0,0 +1 @@ +# this file is needed here to include configs when building project as a package diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/default.yaml new file mode 100644 index 0000000..ebaa3ed --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/default.yaml @@ -0,0 +1,5 @@ +defaults: + - model_checkpoint.yaml + - model_summary.yaml + - rich_progress_bar.yaml + - _self_ diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/model_checkpoint.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/model_checkpoint.yaml new file mode 100644 index 0000000..3d085c7 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/model_checkpoint.yaml @@ -0,0 +1,17 @@ +# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html + +model_checkpoint: + _target_: lightning.pytorch.callbacks.ModelCheckpoint + dirpath: ${paths.output_dir}/checkpoints # directory to save the model file + filename: checkpoint_{epoch:03d} # checkpoint filename + monitor: epoch # name of the logged metric which determines when model is improving + verbose: False # verbosity mode + save_last: true # additionally always save an exact copy of the last checkpoint to a file last.ckpt + save_top_k: 10 # save k best models (determined by above metric) + mode: "max" # "max" means higher metric value is better, can be also "min" + auto_insert_metric_name: True # when True, the checkpoints filenames will contain the metric name + save_weights_only: False # if True, then only the model’s weights will be saved + every_n_train_steps: null # number of training steps between checkpoints + train_time_interval: null # checkpoints are monitored at the specified time interval + every_n_epochs: 100 # number of epochs between checkpoints + save_on_train_epoch_end: null # whether to run checkpointing at the end of the training epoch or the end of validation diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/model_summary.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/model_summary.yaml new file mode 100644 index 0000000..6e5368d --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/model_summary.yaml @@ -0,0 +1,5 @@ +# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html + +model_summary: + _target_: lightning.pytorch.callbacks.RichModelSummary + max_depth: 3 # the maximum depth of layer nesting that the summary will include diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/none.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/none.yaml new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/rich_progress_bar.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/rich_progress_bar.yaml new file mode 100644 index 0000000..de6f1cc --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/callbacks/rich_progress_bar.yaml @@ -0,0 +1,4 @@ +# https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html + +rich_progress_bar: + _target_: lightning.pytorch.callbacks.RichProgressBar diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/default.yaml new file mode 100644 index 0000000..e3932c8 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/default.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +# default debugging setup, runs 1 full epoch +# other debugging configs can inherit from this one + +# overwrite task name so debugging logs are stored in separate folder +task_name: "debug" + +# disable callbacks and loggers during debugging +# callbacks: null +# logger: null + +extras: + ignore_warnings: False + enforce_tags: False + +# sets level of all command line loggers to 'DEBUG' +# https://hydra.cc/docs/tutorials/basic/running_your_app/logging/ +hydra: + job_logging: + root: + level: DEBUG + + # use this to also set hydra loggers to 'DEBUG' + # verbose: True + +trainer: + max_epochs: 1 + accelerator: cpu # debuggers don't like gpus + devices: 1 # debuggers don't like multiprocessing + detect_anomaly: true # raise exception if NaN or +/-inf is detected in any tensor + +data: + num_workers: 0 # debuggers don't like multiprocessing + pin_memory: False # disable gpu memory pin diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/fdr.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/fdr.yaml new file mode 100644 index 0000000..7f2d34f --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/fdr.yaml @@ -0,0 +1,9 @@ +# @package _global_ + +# runs 1 train, 1 validation and 1 test step + +defaults: + - default + +trainer: + fast_dev_run: true diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/limit.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/limit.yaml new file mode 100644 index 0000000..514d77f --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/limit.yaml @@ -0,0 +1,12 @@ +# @package _global_ + +# uses only 1% of the training data and 5% of validation/test data + +defaults: + - default + +trainer: + max_epochs: 3 + limit_train_batches: 0.01 + limit_val_batches: 0.05 + limit_test_batches: 0.05 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/overfit.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/overfit.yaml new file mode 100644 index 0000000..9906586 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/overfit.yaml @@ -0,0 +1,13 @@ +# @package _global_ + +# overfits to 3 batches + +defaults: + - default + +trainer: + max_epochs: 20 + overfit_batches: 3 + +# model ckpt and early stopping need to be disabled during overfitting +callbacks: null diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/profiler.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/profiler.yaml new file mode 100644 index 0000000..266295f --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/debug/profiler.yaml @@ -0,0 +1,15 @@ +# @package _global_ + +# runs with execution time profiling + +defaults: + - default + +trainer: + max_epochs: 1 + # profiler: "simple" + profiler: "advanced" + # profiler: "pytorch" + accelerator: gpu + + limit_train_batches: 0.02 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/eval.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/eval.yaml new file mode 100644 index 0000000..be31299 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/eval.yaml @@ -0,0 +1,18 @@ +# @package _global_ + +defaults: + - _self_ + - data: mnist # choose datamodule with `test_dataloader()` for evaluation + - model: mnist + - logger: null + - trainer: default + - paths: default + - extras: default + - hydra: default + +task_name: "eval" + +tags: ["dev"] + +# passing checkpoint path is necessary for evaluation +ckpt_path: ??? diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/hifi_dataset_piper_phonemizer.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/hifi_dataset_piper_phonemizer.yaml new file mode 100644 index 0000000..7e6c57a --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/hifi_dataset_piper_phonemizer.yaml @@ -0,0 +1,14 @@ +# @package _global_ + +# to execute this experiment run: +# python train.py experiment=multispeaker + +defaults: + - override /data: hi-fi_en-US_female.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +tags: ["hi-fi", "single_speaker", "piper_phonemizer", "en_US", "female"] + +run_name: hi-fi_en-US_female_piper_phonemizer diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech.yaml new file mode 100644 index 0000000..d5723f4 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech.yaml @@ -0,0 +1,14 @@ +# @package _global_ + +# to execute this experiment run: +# python train.py experiment=multispeaker + +defaults: + - override /data: ljspeech.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +tags: ["ljspeech"] + +run_name: ljspeech diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech_from_durations.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech_from_durations.yaml new file mode 100644 index 0000000..63f7d29 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech_from_durations.yaml @@ -0,0 +1,19 @@ +# @package _global_ + +# to execute this experiment run: +# python train.py experiment=multispeaker + +defaults: + - override /data: ljspeech.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +tags: ["ljspeech"] + +run_name: ljspeech + + +data: + load_durations: True + batch_size: 64 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech_min_memory.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech_min_memory.yaml new file mode 100644 index 0000000..ef554dc --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/ljspeech_min_memory.yaml @@ -0,0 +1,18 @@ +# @package _global_ + +# to execute this experiment run: +# python train.py experiment=multispeaker + +defaults: + - override /data: ljspeech.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +tags: ["ljspeech"] + +run_name: ljspeech_min + + +model: + out_size: 172 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/multispeaker.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/multispeaker.yaml new file mode 100644 index 0000000..553842f --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/experiment/multispeaker.yaml @@ -0,0 +1,14 @@ +# @package _global_ + +# to execute this experiment run: +# python train.py experiment=multispeaker + +defaults: + - override /data: vctk.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +tags: ["multispeaker"] + +run_name: multispeaker diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/extras/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/extras/default.yaml new file mode 100644 index 0000000..b9c6b62 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/extras/default.yaml @@ -0,0 +1,8 @@ +# disable python warnings if they annoy you +ignore_warnings: False + +# ask user for tags if none are provided in the config +enforce_tags: True + +# pretty print config tree at the start of the run using Rich library +print_config: True diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/hparams_search/mnist_optuna.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/hparams_search/mnist_optuna.yaml new file mode 100644 index 0000000..1391183 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/hparams_search/mnist_optuna.yaml @@ -0,0 +1,52 @@ +# @package _global_ + +# example hyperparameter optimization of some experiment with Optuna: +# python train.py -m hparams_search=mnist_optuna experiment=example + +defaults: + - override /hydra/sweeper: optuna + +# choose metric which will be optimized by Optuna +# make sure this is the correct name of some metric logged in lightning module! +optimized_metric: "val/acc_best" + +# here we define Optuna hyperparameter search +# it optimizes for value returned from function with @hydra.main decorator +# docs: https://hydra.cc/docs/next/plugins/optuna_sweeper +hydra: + mode: "MULTIRUN" # set hydra to multirun by default if this config is attached + + sweeper: + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + + # storage URL to persist optimization results + # for example, you can use SQLite if you set 'sqlite:///example.db' + storage: null + + # name of the study to persist optimization results + study_name: null + + # number of parallel workers + n_jobs: 1 + + # 'minimize' or 'maximize' the objective + direction: maximize + + # total number of runs that will be executed + n_trials: 20 + + # choose Optuna hyperparameter sampler + # you can choose bayesian sampler (tpe), random search (without optimization), grid sampler, and others + # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html + sampler: + _target_: optuna.samplers.TPESampler + seed: 1234 + n_startup_trials: 10 # number of random sampling runs before optimization starts + + # define hyperparameter search space + params: + model.optimizer.lr: interval(0.0001, 0.1) + data.batch_size: choice(32, 64, 128, 256) + model.net.lin1_size: choice(64, 128, 256) + model.net.lin2_size: choice(64, 128, 256) + model.net.lin3_size: choice(32, 64, 128, 256) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/hydra/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/hydra/default.yaml new file mode 100644 index 0000000..1533136 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/hydra/default.yaml @@ -0,0 +1,19 @@ +# https://hydra.cc/docs/configure_hydra/intro/ + +# enable color logging +defaults: + - override hydra_logging: colorlog + - override job_logging: colorlog + +# output directory, generated dynamically on each run +run: + dir: ${paths.log_dir}/${task_name}/${run_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S} +sweep: + dir: ${paths.log_dir}/${task_name}/${run_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S} + subdir: ${hydra.job.num} + +job_logging: + handlers: + file: + # Incorporates fix from https://github.com/facebookresearch/hydra/pull/2242 + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/local/.gitkeep b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/local/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/aim.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/aim.yaml new file mode 100644 index 0000000..8f9f6ad --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/aim.yaml @@ -0,0 +1,28 @@ +# https://aimstack.io/ + +# example usage in lightning module: +# https://github.com/aimhubio/aim/blob/main/examples/pytorch_lightning_track.py + +# open the Aim UI with the following command (run in the folder containing the `.aim` folder): +# `aim up` + +aim: + _target_: aim.pytorch_lightning.AimLogger + repo: ${paths.root_dir} # .aim folder will be created here + # repo: "aim://ip_address:port" # can instead provide IP address pointing to Aim remote tracking server which manages the repo, see https://aimstack.readthedocs.io/en/latest/using/remote_tracking.html# + + # aim allows to group runs under experiment name + experiment: null # any string, set to "default" if not specified + + train_metric_prefix: "train/" + val_metric_prefix: "val/" + test_metric_prefix: "test/" + + # sets the tracking interval in seconds for system usage metrics (CPU, GPU, memory, etc.) + system_tracking_interval: 10 # set to null to disable system metrics tracking + + # enable/disable logging of system params such as installed packages, git info, env vars, etc. + log_system_params: true + + # enable/disable tracking console logs (default value is true) + capture_terminal_logs: false # set to false to avoid infinite console log loop issue https://github.com/aimhubio/aim/issues/2550 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/comet.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/comet.yaml new file mode 100644 index 0000000..e078927 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/comet.yaml @@ -0,0 +1,12 @@ +# https://www.comet.ml + +comet: + _target_: lightning.pytorch.loggers.comet.CometLogger + api_key: ${oc.env:COMET_API_TOKEN} # api key is loaded from environment variable + save_dir: "${paths.output_dir}" + project_name: "lightning-hydra-template" + rest_api_key: null + # experiment_name: "" + experiment_key: null # set to resume experiment + offline: False + prefix: "" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/csv.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/csv.yaml new file mode 100644 index 0000000..fa028e9 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/csv.yaml @@ -0,0 +1,7 @@ +# csv logger built in lightning + +csv: + _target_: lightning.pytorch.loggers.csv_logs.CSVLogger + save_dir: "${paths.output_dir}" + name: "csv/" + prefix: "" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/many_loggers.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/many_loggers.yaml new file mode 100644 index 0000000..dd58680 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/many_loggers.yaml @@ -0,0 +1,9 @@ +# train with many loggers at once + +defaults: + # - comet + - csv + # - mlflow + # - neptune + - tensorboard + - wandb diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/mlflow.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/mlflow.yaml new file mode 100644 index 0000000..f8fb7e6 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/mlflow.yaml @@ -0,0 +1,12 @@ +# https://mlflow.org + +mlflow: + _target_: lightning.pytorch.loggers.mlflow.MLFlowLogger + # experiment_name: "" + # run_name: "" + tracking_uri: ${paths.log_dir}/mlflow/mlruns # run `mlflow ui` command inside the `logs/mlflow/` dir to open the UI + tags: null + # save_dir: "./mlruns" + prefix: "" + artifact_location: null + # run_id: "" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/neptune.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/neptune.yaml new file mode 100644 index 0000000..8233c14 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/neptune.yaml @@ -0,0 +1,9 @@ +# https://neptune.ai + +neptune: + _target_: lightning.pytorch.loggers.neptune.NeptuneLogger + api_key: ${oc.env:NEPTUNE_API_TOKEN} # api key is loaded from environment variable + project: username/lightning-hydra-template + # name: "" + log_model_checkpoints: True + prefix: "" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/tensorboard.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/tensorboard.yaml new file mode 100644 index 0000000..2bd31f6 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/tensorboard.yaml @@ -0,0 +1,10 @@ +# https://www.tensorflow.org/tensorboard/ + +tensorboard: + _target_: lightning.pytorch.loggers.tensorboard.TensorBoardLogger + save_dir: "${paths.output_dir}/tensorboard/" + name: null + log_graph: False + default_hp_metric: True + prefix: "" + # version: "" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/wandb.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/wandb.yaml new file mode 100644 index 0000000..ece1658 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/logger/wandb.yaml @@ -0,0 +1,16 @@ +# https://wandb.ai + +wandb: + _target_: lightning.pytorch.loggers.wandb.WandbLogger + # name: "" # name of the run (normally generated by wandb) + save_dir: "${paths.output_dir}" + offline: False + id: null # pass correct id to resume experiment! + anonymous: null # enable anonymous logging + project: "lightning-hydra-template" + log_model: False # upload lightning ckpts + prefix: "" # a string to put at the beginning of metric keys + # entity: "" # set to name of your wandb team + group: "" + tags: [] + job_type: "" diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/cfm/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/cfm/default.yaml new file mode 100644 index 0000000..0d1d960 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/cfm/default.yaml @@ -0,0 +1,3 @@ +name: CFM +solver: euler +sigma_min: 1e-4 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/decoder/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/decoder/default.yaml new file mode 100644 index 0000000..aaa00e6 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/decoder/default.yaml @@ -0,0 +1,7 @@ +channels: [256, 256] +dropout: 0.05 +attention_head_dim: 64 +n_blocks: 1 +num_mid_blocks: 2 +num_heads: 2 +act_fn: snakebeta diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/encoder/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/encoder/default.yaml new file mode 100644 index 0000000..d4d5e5a --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/encoder/default.yaml @@ -0,0 +1,18 @@ +encoder_type: RoPE Encoder +encoder_params: + n_feats: ${model.n_feats} + n_channels: 192 + filter_channels: 768 + filter_channels_dp: 256 + n_heads: 2 + n_layers: 6 + kernel_size: 3 + p_dropout: 0.1 + spk_emb_dim: 64 + n_spks: 1 + prenet: true + +duration_predictor_params: + filter_channels_dp: ${model.encoder.encoder_params.filter_channels_dp} + kernel_size: 3 + p_dropout: ${model.encoder.encoder_params.p_dropout} diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/matcha.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/matcha.yaml new file mode 100644 index 0000000..e2b5c78 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/matcha.yaml @@ -0,0 +1,16 @@ +defaults: + - _self_ + - encoder: default.yaml + - decoder: default.yaml + - cfm: default.yaml + - optimizer: adam.yaml + +_target_: matcha.models.matcha_tts.MatchaTTS +n_vocab: 178 +n_spks: ${data.n_spks} +spk_emb_dim: 64 +n_feats: 80 +data_statistics: ${data.data_statistics} +out_size: null # Must be divisible by 4 +prior_loss: true +use_precomputed_durations: ${data.load_durations} diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/optimizer/adam.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/optimizer/adam.yaml new file mode 100644 index 0000000..4279557 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/model/optimizer/adam.yaml @@ -0,0 +1,4 @@ +_target_: torch.optim.Adam +_partial_: true +lr: 1e-4 +weight_decay: 0.0 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/paths/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/paths/default.yaml new file mode 100644 index 0000000..ec81db2 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/paths/default.yaml @@ -0,0 +1,18 @@ +# path to root directory +# this requires PROJECT_ROOT environment variable to exist +# you can replace it with "." if you want the root to be the current working directory +root_dir: ${oc.env:PROJECT_ROOT} + +# path to data directory +data_dir: ${paths.root_dir}/data/ + +# path to logging directory +log_dir: ${paths.root_dir}/logs/ + +# path to output directory, created dynamically by hydra +# path generation pattern is specified in `configs/hydra/default.yaml` +# use it to store all files generated during the run, like ckpts and metrics +output_dir: ${hydra:runtime.output_dir} + +# path to working directory +work_dir: ${hydra:runtime.cwd} diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/train.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/train.yaml new file mode 100644 index 0000000..e6f5c2e --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/train.yaml @@ -0,0 +1,51 @@ +# @package _global_ + +# specify here default configuration +# order of defaults determines the order in which configs override each other +defaults: + - _self_ + - data: ljspeech + - model: matcha + - callbacks: default + - logger: tensorboard # set logger here or use command line (e.g. `python train.py logger=tensorboard`) + - trainer: default + - paths: default + - extras: default + - hydra: default + + # experiment configs allow for version control of specific hyperparameters + # e.g. best hyperparameters for given model and datamodule + - experiment: null + + # config for hyperparameter optimization + - hparams_search: null + + # optional local config for machine/user specific settings + # it's optional since it doesn't need to exist and is excluded from version control + - optional local: default + + # debugging config (enable through command line, e.g. `python train.py debug=default) + - debug: null + +# task name, determines output directory path +task_name: "train" + +run_name: ??? + +# tags to help you identify your experiments +# you can overwrite this in experiment configs +# overwrite from command line with `python train.py tags="[first_tag, second_tag]"` +tags: ["dev"] + +# set False to skip model training +train: True + +# evaluate on test set, using best model weights achieved during training +# lightning chooses best weights based on the metric specified in checkpoint callback +test: True + +# simply provide checkpoint path to resume training +ckpt_path: null + +# seed for random number generators in pytorch, numpy and python.random +seed: 1234 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/cpu.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/cpu.yaml new file mode 100644 index 0000000..b7d6767 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/cpu.yaml @@ -0,0 +1,5 @@ +defaults: + - default + +accelerator: cpu +devices: 1 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/ddp.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/ddp.yaml new file mode 100644 index 0000000..94b43e2 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/ddp.yaml @@ -0,0 +1,9 @@ +defaults: + - default + +strategy: ddp + +accelerator: gpu +devices: [0,1] +num_nodes: 1 +sync_batchnorm: True diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/ddp_sim.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/ddp_sim.yaml new file mode 100644 index 0000000..8404419 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/ddp_sim.yaml @@ -0,0 +1,7 @@ +defaults: + - default + +# simulate DDP on CPU, useful for debugging +accelerator: cpu +devices: 2 +strategy: ddp_spawn diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/default.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/default.yaml new file mode 100644 index 0000000..ee3d370 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/default.yaml @@ -0,0 +1,20 @@ +_target_: lightning.pytorch.trainer.Trainer + +default_root_dir: ${paths.output_dir} + +max_epochs: -1 + +accelerator: gpu +devices: [0] + +# mixed precision for extra speed-up +precision: 16-mixed + +# perform a validation loop every N training epochs +check_val_every_n_epoch: 1 + +# set True to to ensure deterministic results +# makes training slower but gives more reproducibility than just setting seeds +deterministic: False + +gradient_clip_val: 5.0 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/gpu.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/gpu.yaml new file mode 100644 index 0000000..b238951 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/gpu.yaml @@ -0,0 +1,5 @@ +defaults: + - default + +accelerator: gpu +devices: 1 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/mps.yaml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/mps.yaml new file mode 100644 index 0000000..1ecf6d5 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/configs/trainer/mps.yaml @@ -0,0 +1,5 @@ +defaults: + - default + +accelerator: mps +devices: 1 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/VERSION b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/VERSION new file mode 100644 index 0000000..4d81495 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/VERSION @@ -0,0 +1 @@ +0.0.7.2 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/app.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/app.py new file mode 100644 index 0000000..d68fbaa --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/app.py @@ -0,0 +1,357 @@ +import tempfile +from argparse import Namespace +from pathlib import Path + +import gradio as gr +import soundfile as sf +import torch + +from matcha.cli import ( + MATCHA_URLS, + VOCODER_URLS, + assert_model_downloaded, + get_device, + load_matcha, + load_vocoder, + process_text, + to_waveform, +) +from matcha.utils.utils import get_user_data_dir, plot_tensor + +LOCATION = Path(get_user_data_dir()) + +args = Namespace( + cpu=False, + model="matcha_vctk", + vocoder="hifigan_univ_v1", + spk=0, +) + +CURRENTLY_LOADED_MODEL = args.model + + +def MATCHA_TTS_LOC(x): + return LOCATION / f"{x}.ckpt" + + +def VOCODER_LOC(x): + return LOCATION / f"{x}" + + +LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png" +RADIO_OPTIONS = { + "Multi Speaker (VCTK)": { + "model": "matcha_vctk", + "vocoder": "hifigan_univ_v1", + }, + "Single Speaker (LJ Speech)": { + "model": "matcha_ljspeech", + "vocoder": "hifigan_T2_v1", + }, +} + +# Ensure all the required models are downloaded +assert_model_downloaded(MATCHA_TTS_LOC("matcha_ljspeech"), MATCHA_URLS["matcha_ljspeech"]) +assert_model_downloaded(VOCODER_LOC("hifigan_T2_v1"), VOCODER_URLS["hifigan_T2_v1"]) +assert_model_downloaded(MATCHA_TTS_LOC("matcha_vctk"), MATCHA_URLS["matcha_vctk"]) +assert_model_downloaded(VOCODER_LOC("hifigan_univ_v1"), VOCODER_URLS["hifigan_univ_v1"]) + +device = get_device(args) + +# Load default model +model = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device) +vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device) + + +def load_model(model_name, vocoder_name): + model = load_matcha(model_name, MATCHA_TTS_LOC(model_name), device) + vocoder, denoiser = load_vocoder(vocoder_name, VOCODER_LOC(vocoder_name), device) + return model, vocoder, denoiser + + +def load_model_ui(model_type, textbox): + model_name, vocoder_name = RADIO_OPTIONS[model_type]["model"], RADIO_OPTIONS[model_type]["vocoder"] + + global model, vocoder, denoiser, CURRENTLY_LOADED_MODEL # pylint: disable=global-statement + if CURRENTLY_LOADED_MODEL != model_name: + model, vocoder, denoiser = load_model(model_name, vocoder_name) + CURRENTLY_LOADED_MODEL = model_name + + if model_name == "matcha_ljspeech": + spk_slider = gr.update(visible=False, value=-1) + single_speaker_examples = gr.update(visible=True) + multi_speaker_examples = gr.update(visible=False) + length_scale = gr.update(value=0.95) + else: + spk_slider = gr.update(visible=True, value=0) + single_speaker_examples = gr.update(visible=False) + multi_speaker_examples = gr.update(visible=True) + length_scale = gr.update(value=0.85) + + return ( + textbox, + gr.update(interactive=True), + spk_slider, + single_speaker_examples, + multi_speaker_examples, + length_scale, + ) + + +@torch.inference_mode() +def process_text_gradio(text): + output = process_text(1, text, device) + return output["x_phones"][1::2], output["x"], output["x_lengths"] + + +@torch.inference_mode() +def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk): + spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None + output = model.synthesise( + text, + text_length, + n_timesteps=n_timesteps, + temperature=temperature, + spks=spk, + length_scale=length_scale, + ) + output["waveform"] = to_waveform(output["mel"], vocoder, denoiser) + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: + sf.write(fp.name, output["waveform"], 22050, "PCM_24") + + return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy()) + + +def multispeaker_example_cacher(text, n_timesteps, mel_temp, length_scale, spk): + global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement + if CURRENTLY_LOADED_MODEL != "matcha_vctk": + global model, vocoder, denoiser # pylint: disable=global-statement + model, vocoder, denoiser = load_model("matcha_vctk", "hifigan_univ_v1") + CURRENTLY_LOADED_MODEL = "matcha_vctk" + + phones, text, text_lengths = process_text_gradio(text) + audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk) + return phones, audio, mel_spectrogram + + +def ljspeech_example_cacher(text, n_timesteps, mel_temp, length_scale, spk=-1): + global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement + if CURRENTLY_LOADED_MODEL != "matcha_ljspeech": + global model, vocoder, denoiser # pylint: disable=global-statement + model, vocoder, denoiser = load_model("matcha_ljspeech", "hifigan_T2_v1") + CURRENTLY_LOADED_MODEL = "matcha_ljspeech" + + phones, text, text_lengths = process_text_gradio(text) + audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk) + return phones, audio, mel_spectrogram + + +def main(): + description = """# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching + ### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/) + We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up ODE-based speech synthesis. Our method: + + + * Is probabilistic + * Has compact memory footprint + * Sounds highly natural + * Is very fast to synthesise from + + + Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS). Read our [arXiv preprint for more details](https://arxiv.org/abs/2309.03199). + Code is available in our [GitHub repository](https://github.com/shivammehta25/Matcha-TTS), along with pre-trained models. + + Cached examples are available at the bottom of the page. + """ + + with gr.Blocks(title="🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching") as demo: + processed_text = gr.State(value=None) + processed_text_len = gr.State(value=None) + + with gr.Box(): + with gr.Row(): + gr.Markdown(description, scale=3) + with gr.Column(): + gr.Image(LOGO_URL, label="Matcha-TTS logo", height=50, width=50, scale=1, show_label=False) + html = '
' + gr.HTML(html) + + with gr.Box(): + radio_options = list(RADIO_OPTIONS.keys()) + model_type = gr.Radio( + radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False + ) + + with gr.Row(): + gr.Markdown("# Text Input") + with gr.Row(): + text = gr.Textbox(value="", lines=2, label="Text to synthesise", scale=3) + spk_slider = gr.Slider( + minimum=0, maximum=107, step=1, value=args.spk, label="Speaker ID", interactive=True, scale=1 + ) + + with gr.Row(): + gr.Markdown("### Hyper parameters") + with gr.Row(): + n_timesteps = gr.Slider( + label="Number of ODE steps", + minimum=1, + maximum=100, + step=1, + value=10, + interactive=True, + ) + length_scale = gr.Slider( + label="Length scale (Speaking rate)", + minimum=0.5, + maximum=1.5, + step=0.05, + value=1.0, + interactive=True, + ) + mel_temp = gr.Slider( + label="Sampling temperature", + minimum=0.00, + maximum=2.001, + step=0.16675, + value=0.667, + interactive=True, + ) + + synth_btn = gr.Button("Synthesise") + + with gr.Box(): + with gr.Row(): + gr.Markdown("### Phonetised text") + phonetised_text = gr.Textbox(interactive=False, scale=10, label="Phonetised text") + + with gr.Box(): + with gr.Row(): + mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram") + + # with gr.Row(): + audio = gr.Audio(interactive=False, label="Audio") + + with gr.Row(visible=False) as example_row_lj_speech: + examples = gr.Examples( # pylint: disable=unused-variable + examples=[ + [ + "We propose Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up O D E-based speech synthesis.", + 50, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 2, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 4, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 10, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 50, + 0.677, + 0.95, + ], + [ + "The narrative of these events is based largely on the recollections of the participants.", + 10, + 0.677, + 0.95, + ], + [ + "The jury did not believe him, and the verdict was for the defendants.", + 10, + 0.677, + 0.95, + ], + ], + fn=ljspeech_example_cacher, + inputs=[text, n_timesteps, mel_temp, length_scale], + outputs=[phonetised_text, audio, mel_spectrogram], + cache_examples=True, + ) + + with gr.Row() as example_row_multispeaker: + multi_speaker_examples = gr.Examples( # pylint: disable=unused-variable + examples=[ + [ + "Hello everyone! I am speaker 0 and I am here to tell you that Matcha-TTS is amazing!", + 10, + 0.677, + 0.85, + 0, + ], + [ + "Hello everyone! I am speaker 16 and I am here to tell you that Matcha-TTS is amazing!", + 10, + 0.677, + 0.85, + 16, + ], + [ + "Hello everyone! I am speaker 44 and I am here to tell you that Matcha-TTS is amazing!", + 50, + 0.677, + 0.85, + 44, + ], + [ + "Hello everyone! I am speaker 45 and I am here to tell you that Matcha-TTS is amazing!", + 50, + 0.677, + 0.85, + 45, + ], + [ + "Hello everyone! I am speaker 58 and I am here to tell you that Matcha-TTS is amazing!", + 4, + 0.677, + 0.85, + 58, + ], + ], + fn=multispeaker_example_cacher, + inputs=[text, n_timesteps, mel_temp, length_scale, spk_slider], + outputs=[phonetised_text, audio, mel_spectrogram], + cache_examples=True, + label="Multi Speaker Examples", + ) + + model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then( + load_model_ui, + inputs=[model_type, text], + outputs=[text, synth_btn, spk_slider, example_row_lj_speech, example_row_multispeaker, length_scale], + ) + + synth_btn.click( + fn=process_text_gradio, + inputs=[ + text, + ], + outputs=[phonetised_text, processed_text, processed_text_len], + api_name="matcha_tts", + queue=True, + ).then( + fn=synthesise_mel, + inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale, spk_slider], + outputs=[audio, mel_spectrogram], + ) + + demo.queue().launch(share=True) + + +if __name__ == "__main__": + main() diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/cli.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/cli.py new file mode 100644 index 0000000..aa15eb1 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/cli.py @@ -0,0 +1,419 @@ +import argparse +import datetime as dt +import os +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import soundfile as sf +import torch + +from matcha.hifigan.config import v1 +from matcha.hifigan.denoiser import Denoiser +from matcha.hifigan.env import AttrDict +from matcha.hifigan.models import Generator as HiFiGAN +from matcha.models.matcha_tts import MatchaTTS +from matcha.text import sequence_to_text, text_to_sequence +from matcha.utils.utils import assert_model_downloaded, get_user_data_dir, intersperse + +MATCHA_URLS = { + "matcha_ljspeech": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/matcha_ljspeech.ckpt", + "matcha_vctk": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/matcha_vctk.ckpt", +} + +VOCODER_URLS = { + "hifigan_T2_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/generator_v1", # Old url: https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link + "hifigan_univ_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/g_02500000", # Old url: https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link +} + +MULTISPEAKER_MODEL = { + "matcha_vctk": {"vocoder": "hifigan_univ_v1", "speaking_rate": 0.85, "spk": 0, "spk_range": (0, 107)} +} + +SINGLESPEAKER_MODEL = {"matcha_ljspeech": {"vocoder": "hifigan_T2_v1", "speaking_rate": 0.95, "spk": None}} + + +def plot_spectrogram_to_numpy(spectrogram, filename): + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.xlabel("Frames") + plt.ylabel("Channels") + plt.title("Synthesised Mel-Spectrogram") + fig.canvas.draw() + plt.savefig(filename) + + +def process_text(i: int, text: str, device: torch.device): + print(f"[{i}] - Input text: {text}") + x = torch.tensor( + intersperse(text_to_sequence(text, ["english_cleaners2"])[0], 0), + dtype=torch.long, + device=device, + )[None] + x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device) + x_phones = sequence_to_text(x.squeeze(0).tolist()) + print(f"[{i}] - Phonetised text: {x_phones[1::2]}") + + return {"x_orig": text, "x": x, "x_lengths": x_lengths, "x_phones": x_phones} + + +def get_texts(args): + if args.text: + texts = [args.text] + else: + with open(args.file, encoding="utf-8") as f: + texts = f.readlines() + return texts + + +def assert_required_models_available(args): + save_dir = get_user_data_dir() + if not hasattr(args, "checkpoint_path") and args.checkpoint_path is None: + model_path = args.checkpoint_path + else: + model_path = save_dir / f"{args.model}.ckpt" + assert_model_downloaded(model_path, MATCHA_URLS[args.model]) + + vocoder_path = save_dir / f"{args.vocoder}" + assert_model_downloaded(vocoder_path, VOCODER_URLS[args.vocoder]) + return {"matcha": model_path, "vocoder": vocoder_path} + + +def load_hifigan(checkpoint_path, device): + h = AttrDict(v1) + hifigan = HiFiGAN(h).to(device) + hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"]) + _ = hifigan.eval() + hifigan.remove_weight_norm() + return hifigan + + +def load_vocoder(vocoder_name, checkpoint_path, device): + print(f"[!] Loading {vocoder_name}!") + vocoder = None + if vocoder_name in ("hifigan_T2_v1", "hifigan_univ_v1"): + vocoder = load_hifigan(checkpoint_path, device) + else: + raise NotImplementedError( + f"Vocoder {vocoder_name} not implemented! define a load_<> method for it" + ) + + denoiser = Denoiser(vocoder, mode="zeros") + print(f"[+] {vocoder_name} loaded!") + return vocoder, denoiser + + +def load_matcha(model_name, checkpoint_path, device): + print(f"[!] Loading {model_name}!") + model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device) + _ = model.eval() + + print(f"[+] {model_name} loaded!") + return model + + +def to_waveform(mel, vocoder, denoiser=None, denoiser_strength=0.00025): + audio = vocoder(mel).clamp(-1, 1) + if denoiser is not None: + audio = denoiser(audio.squeeze(), strength=denoiser_strength).cpu().squeeze() + + return audio.cpu().squeeze() + + +def save_to_folder(filename: str, output: dict, folder: str): + folder = Path(folder) + folder.mkdir(exist_ok=True, parents=True) + plot_spectrogram_to_numpy(np.array(output["mel"].squeeze().float().cpu()), f"{filename}.png") + np.save(folder / f"{filename}", output["mel"].cpu().numpy()) + sf.write(folder / f"{filename}.wav", output["waveform"], 22050, "PCM_24") + return folder.resolve() / f"{filename}.wav" + + +def validate_args(args): + assert ( + args.text or args.file + ), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms." + assert args.temperature >= 0, "Sampling temperature cannot be negative" + assert args.steps > 0, "Number of ODE steps must be greater than 0" + + if args.checkpoint_path is None: + # When using pretrained models + if args.model in SINGLESPEAKER_MODEL: + args = validate_args_for_single_speaker_model(args) + + if args.model in MULTISPEAKER_MODEL: + args = validate_args_for_multispeaker_model(args) + else: + # When using a custom model + if args.vocoder != "hifigan_univ_v1": + warn_ = "[-] Using custom model checkpoint! I would suggest passing --vocoder hifigan_univ_v1, unless the custom model is trained on LJ Speech." + warnings.warn(warn_, UserWarning) + if args.speaking_rate is None: + args.speaking_rate = 1.0 + + if args.batched: + assert args.batch_size > 0, "Batch size must be greater than 0" + assert args.speaking_rate > 0, "Speaking rate must be greater than 0" + + return args + + +def validate_args_for_multispeaker_model(args): + if args.vocoder is not None: + if args.vocoder != MULTISPEAKER_MODEL[args.model]["vocoder"]: + warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {MULTISPEAKER_MODEL[args.model]['vocoder']}" + warnings.warn(warn_, UserWarning) + else: + args.vocoder = MULTISPEAKER_MODEL[args.model]["vocoder"] + + if args.speaking_rate is None: + args.speaking_rate = MULTISPEAKER_MODEL[args.model]["speaking_rate"] + + spk_range = MULTISPEAKER_MODEL[args.model]["spk_range"] + if args.spk is not None: + assert ( + args.spk >= spk_range[0] and args.spk <= spk_range[-1] + ), f"Speaker ID must be between {spk_range} for this model." + else: + available_spk_id = MULTISPEAKER_MODEL[args.model]["spk"] + warn_ = f"[!] Speaker ID not provided! Using speaker ID {available_spk_id}" + warnings.warn(warn_, UserWarning) + args.spk = available_spk_id + + return args + + +def validate_args_for_single_speaker_model(args): + if args.vocoder is not None: + if args.vocoder != SINGLESPEAKER_MODEL[args.model]["vocoder"]: + warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {SINGLESPEAKER_MODEL[args.model]['vocoder']}" + warnings.warn(warn_, UserWarning) + else: + args.vocoder = SINGLESPEAKER_MODEL[args.model]["vocoder"] + + if args.speaking_rate is None: + args.speaking_rate = SINGLESPEAKER_MODEL[args.model]["speaking_rate"] + + if args.spk != SINGLESPEAKER_MODEL[args.model]["spk"]: + warn_ = f"[-] Ignoring speaker id {args.spk} for {args.model}" + warnings.warn(warn_, UserWarning) + args.spk = SINGLESPEAKER_MODEL[args.model]["spk"] + + return args + + +@torch.inference_mode() +def cli(): + parser = argparse.ArgumentParser( + description=" 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching" + ) + parser.add_argument( + "--model", + type=str, + default="matcha_ljspeech", + help="Model to use", + choices=MATCHA_URLS.keys(), + ) + + parser.add_argument( + "--checkpoint_path", + type=str, + default=None, + help="Path to the custom model checkpoint", + ) + + parser.add_argument( + "--vocoder", + type=str, + default=None, + help="Vocoder to use (default: will use the one suggested with the pretrained model))", + choices=VOCODER_URLS.keys(), + ) + parser.add_argument("--text", type=str, default=None, help="Text to synthesize") + parser.add_argument("--file", type=str, default=None, help="Text file to synthesize") + parser.add_argument("--spk", type=int, default=None, help="Speaker ID") + parser.add_argument( + "--temperature", + type=float, + default=0.667, + help="Variance of the x0 noise (default: 0.667)", + ) + parser.add_argument( + "--speaking_rate", + type=float, + default=None, + help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)", + ) + parser.add_argument("--steps", type=int, default=10, help="Number of ODE steps (default: 10)") + parser.add_argument("--cpu", action="store_true", help="Use CPU for inference (default: use GPU if available)") + parser.add_argument( + "--denoiser_strength", + type=float, + default=0.00025, + help="Strength of the vocoder bias denoiser (default: 0.00025)", + ) + parser.add_argument( + "--output_folder", + type=str, + default=os.getcwd(), + help="Output folder to save results (default: current dir)", + ) + parser.add_argument("--batched", action="store_true", help="Batched inference (default: False)") + parser.add_argument( + "--batch_size", type=int, default=32, help="Batch size only useful when --batched (default: 32)" + ) + + args = parser.parse_args() + + args = validate_args(args) + device = get_device(args) + print_config(args) + paths = assert_required_models_available(args) + + if args.checkpoint_path is not None: + print(f"[🍵] Loading custom model from {args.checkpoint_path}") + paths["matcha"] = args.checkpoint_path + args.model = "custom_model" + + model = load_matcha(args.model, paths["matcha"], device) + vocoder, denoiser = load_vocoder(args.vocoder, paths["vocoder"], device) + + texts = get_texts(args) + + spk = torch.tensor([args.spk], device=device, dtype=torch.long) if args.spk is not None else None + if len(texts) == 1 or not args.batched: + unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk) + else: + batched_synthesis(args, device, model, vocoder, denoiser, texts, spk) + + +class BatchedSynthesisDataset(torch.utils.data.Dataset): + def __init__(self, processed_texts): + self.processed_texts = processed_texts + + def __len__(self): + return len(self.processed_texts) + + def __getitem__(self, idx): + return self.processed_texts[idx] + + +def batched_collate_fn(batch): + x = [] + x_lengths = [] + + for b in batch: + x.append(b["x"].squeeze(0)) + x_lengths.append(b["x_lengths"]) + + x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True) + x_lengths = torch.concat(x_lengths, dim=0) + return {"x": x, "x_lengths": x_lengths} + + +def batched_synthesis(args, device, model, vocoder, denoiser, texts, spk): + total_rtf = [] + total_rtf_w = [] + processed_text = [process_text(i, text, "cpu") for i, text in enumerate(texts)] + dataloader = torch.utils.data.DataLoader( + BatchedSynthesisDataset(processed_text), + batch_size=args.batch_size, + collate_fn=batched_collate_fn, + num_workers=8, + ) + for i, batch in enumerate(dataloader): + i = i + 1 + start_t = dt.datetime.now() + b = batch["x"].shape[0] + output = model.synthesise( + batch["x"].to(device), + batch["x_lengths"].to(device), + n_timesteps=args.steps, + temperature=args.temperature, + spks=spk.expand(b) if spk is not None else spk, + length_scale=args.speaking_rate, + ) + + output["waveform"] = to_waveform(output["mel"], vocoder, denoiser, args.denoiser_strength) + t = (dt.datetime.now() - start_t).total_seconds() + rtf_w = t * 22050 / (output["waveform"].shape[-1]) + print(f"[🍵-Batch: {i}] Matcha-TTS RTF: {output['rtf']:.4f}") + print(f"[🍵-Batch: {i}] Matcha-TTS + VOCODER RTF: {rtf_w:.4f}") + total_rtf.append(output["rtf"]) + total_rtf_w.append(rtf_w) + for j in range(output["mel"].shape[0]): + base_name = f"utterance_{j:03d}_speaker_{args.spk:03d}" if args.spk is not None else f"utterance_{j:03d}" + length = output["mel_lengths"][j] + new_dict = {"mel": output["mel"][j][:, :length], "waveform": output["waveform"][j][: length * 256]} + location = save_to_folder(base_name, new_dict, args.output_folder) + print(f"[🍵-{j}] Waveform saved: {location}") + + print("".join(["="] * 100)) + print(f"[🍵] Average Matcha-TTS RTF: {np.mean(total_rtf):.4f} ± {np.std(total_rtf)}") + print(f"[🍵] Average Matcha-TTS + VOCODER RTF: {np.mean(total_rtf_w):.4f} ± {np.std(total_rtf_w)}") + print("[🍵] Enjoy the freshly whisked 🍵 Matcha-TTS!") + + +def unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk): + total_rtf = [] + total_rtf_w = [] + for i, text in enumerate(texts): + i = i + 1 + base_name = f"utterance_{i:03d}_speaker_{args.spk:03d}" if args.spk is not None else f"utterance_{i:03d}" + + print("".join(["="] * 100)) + text = text.strip() + text_processed = process_text(i, text, device) + + print(f"[🍵] Whisking Matcha-T(ea)TS for: {i}") + start_t = dt.datetime.now() + output = model.synthesise( + text_processed["x"], + text_processed["x_lengths"], + n_timesteps=args.steps, + temperature=args.temperature, + spks=spk, + length_scale=args.speaking_rate, + ) + output["waveform"] = to_waveform(output["mel"], vocoder, denoiser, args.denoiser_strength) + # RTF with HiFiGAN + t = (dt.datetime.now() - start_t).total_seconds() + rtf_w = t * 22050 / (output["waveform"].shape[-1]) + print(f"[🍵-{i}] Matcha-TTS RTF: {output['rtf']:.4f}") + print(f"[🍵-{i}] Matcha-TTS + VOCODER RTF: {rtf_w:.4f}") + total_rtf.append(output["rtf"]) + total_rtf_w.append(rtf_w) + + location = save_to_folder(base_name, output, args.output_folder) + print(f"[+] Waveform saved: {location}") + + print("".join(["="] * 100)) + print(f"[🍵] Average Matcha-TTS RTF: {np.mean(total_rtf):.4f} ± {np.std(total_rtf)}") + print(f"[🍵] Average Matcha-TTS + VOCODER RTF: {np.mean(total_rtf_w):.4f} ± {np.std(total_rtf_w)}") + print("[🍵] Enjoy the freshly whisked 🍵 Matcha-TTS!") + + +def print_config(args): + print("[!] Configurations: ") + print(f"\t- Model: {args.model}") + print(f"\t- Vocoder: {args.vocoder}") + print(f"\t- Temperature: {args.temperature}") + print(f"\t- Speaking rate: {args.speaking_rate}") + print(f"\t- Number of ODE steps: {args.steps}") + print(f"\t- Speaker: {args.spk}") + + +def get_device(args): + if torch.cuda.is_available() and not args.cpu: + print("[+] GPU Available! Using GPU") + device = torch.device("cuda") + else: + print("[-] GPU not available or forced CPU run! Using CPU") + device = torch.device("cpu") + return device + + +if __name__ == "__main__": + cli() diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/LICENSE b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/LICENSE new file mode 100644 index 0000000..91751da --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Jungil Kong + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/README.md b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/README.md new file mode 100644 index 0000000..5db2585 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/README.md @@ -0,0 +1,101 @@ +# HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis + +### Jungil Kong, Jaehyeon Kim, Jaekyoung Bae + +In our [paper](https://arxiv.org/abs/2010.05646), +we proposed HiFi-GAN: a GAN-based model capable of generating high fidelity speech efficiently.
+We provide our implementation and pretrained models as open source in this repository. + +**Abstract :** +Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. +Although such methods improve the sampling efficiency and memory usage, +their sample quality has not yet reached that of autoregressive and flow-based generative models. +In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. +As speech audio consists of sinusoidal signals with various periods, +we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality. +A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method +demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9 times faster than +real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen +speakers and end-to-end speech synthesis. Finally, a small footprint version of HiFi-GAN generates samples 13.4 times +faster than real-time on CPU with comparable quality to an autoregressive counterpart. + +Visit our [demo website](https://jik876.github.io/hifi-gan-demo/) for audio samples. + +## Pre-requisites + +1. Python >= 3.6 +2. Clone this repository. +3. Install python requirements. Please refer [requirements.txt](requirements.txt) +4. Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/). + And move all wav files to `LJSpeech-1.1/wavs` + +## Training + +``` +python train.py --config config_v1.json +``` + +To train V2 or V3 Generator, replace `config_v1.json` with `config_v2.json` or `config_v3.json`.
+Checkpoints and copy of the configuration file are saved in `cp_hifigan` directory by default.
+You can change the path by adding `--checkpoint_path` option. + +Validation loss during training with V1 generator.
+![validation loss](./validation_loss.png) + +## Pretrained Model + +You can also use pretrained models we provide.
+[Download pretrained models](https://drive.google.com/drive/folders/1-eEYTB5Av9jNql0WGBlRoi-WH2J7bp5Y?usp=sharing)
+Details of each folder are as in follows: + +| Folder Name | Generator | Dataset | Fine-Tuned | +| ------------ | --------- | --------- | ------------------------------------------------------ | +| LJ_V1 | V1 | LJSpeech | No | +| LJ_V2 | V2 | LJSpeech | No | +| LJ_V3 | V3 | LJSpeech | No | +| LJ_FT_T2_V1 | V1 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| LJ_FT_T2_V2 | V2 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| LJ_FT_T2_V3 | V3 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| VCTK_V1 | V1 | VCTK | No | +| VCTK_V2 | V2 | VCTK | No | +| VCTK_V3 | V3 | VCTK | No | +| UNIVERSAL_V1 | V1 | Universal | No | + +We provide the universal model with discriminator weights that can be used as a base for transfer learning to other datasets. + +## Fine-Tuning + +1. Generate mel-spectrograms in numpy format using [Tacotron2](https://github.com/NVIDIA/tacotron2) with teacher-forcing.
+ The file name of the generated mel-spectrogram should match the audio file and the extension should be `.npy`.
+ Example: + ` Audio File : LJ001-0001.wav +Mel-Spectrogram File : LJ001-0001.npy` +2. Create `ft_dataset` folder and copy the generated mel-spectrogram files into it.
+3. Run the following command. + ``` + python train.py --fine_tuning True --config config_v1.json + ``` + For other command line options, please refer to the training section. + +## Inference from wav file + +1. Make `test_files` directory and copy wav files into the directory. +2. Run the following command. + ` python inference.py --checkpoint_file [generator checkpoint file path]` + Generated wav files are saved in `generated_files` by default.
+ You can change the path by adding `--output_dir` option. + +## Inference for end-to-end speech synthesis + +1. Make `test_mel_files` directory and copy generated mel-spectrogram files into the directory.
+ You can generate mel-spectrograms using [Tacotron2](https://github.com/NVIDIA/tacotron2), + [Glow-TTS](https://github.com/jaywalnut310/glow-tts) and so forth. +2. Run the following command. + ` python inference_e2e.py --checkpoint_file [generator checkpoint file path]` + Generated wav files are saved in `generated_files_from_mel` by default.
+ You can change the path by adding `--output_dir` option. + +## Acknowledgements + +We referred to [WaveGlow](https://github.com/NVIDIA/waveglow), [MelGAN](https://github.com/descriptinc/melgan-neurips) +and [Tacotron2](https://github.com/NVIDIA/tacotron2) to implement this. diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/config.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/config.py new file mode 100644 index 0000000..b3abea9 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/config.py @@ -0,0 +1,28 @@ +v1 = { + "resblock": "1", + "num_gpus": 0, + "batch_size": 16, + "learning_rate": 0.0004, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + "upsample_rates": [8, 8, 2, 2], + "upsample_kernel_sizes": [16, 16, 4, 4], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "resblock_initial_channel": 256, + "segment_size": 8192, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + "sampling_rate": 22050, + "fmin": 0, + "fmax": 8000, + "fmax_loss": None, + "num_workers": 4, + "dist_config": {"dist_backend": "nccl", "dist_url": "tcp://localhost:54321", "world_size": 1}, +} diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/denoiser.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/denoiser.py new file mode 100644 index 0000000..452be6a --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/denoiser.py @@ -0,0 +1,68 @@ +# Code modified from Rafael Valle's implementation https://github.com/NVIDIA/waveglow/blob/5bc2a53e20b3b533362f974cfa1ea0267ae1c2b1/denoiser.py + +"""Waveglow style denoiser can be used to remove the artifacts from the HiFiGAN generated audio.""" +import torch + + +class ModeException(Exception): + pass + + +class Denoiser(torch.nn.Module): + """Removes model bias from audio produced with waveglow""" + + def __init__(self, vocoder, filter_length=1024, n_overlap=4, win_length=1024, mode="zeros"): + super().__init__() + self.filter_length = filter_length + self.hop_length = int(filter_length / n_overlap) + self.win_length = win_length + + dtype, device = next(vocoder.parameters()).dtype, next(vocoder.parameters()).device + self.device = device + if mode == "zeros": + mel_input = torch.zeros((1, 80, 88), dtype=dtype, device=device) + elif mode == "normal": + mel_input = torch.randn((1, 80, 88), dtype=dtype, device=device) + else: + raise ModeException(f"Mode {mode} if not supported") + + def stft_fn(audio, n_fft, hop_length, win_length, window): + spec = torch.stft( + audio, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + return_complex=True, + ) + spec = torch.view_as_real(spec) + return torch.sqrt(spec.pow(2).sum(-1)), torch.atan2(spec[..., -1], spec[..., 0]) + + self.stft = lambda x: stft_fn( + audio=x, + n_fft=self.filter_length, + hop_length=self.hop_length, + win_length=self.win_length, + window=torch.hann_window(self.win_length, device=device), + ) + self.istft = lambda x, y: torch.istft( + torch.complex(x * torch.cos(y), x * torch.sin(y)), + n_fft=self.filter_length, + hop_length=self.hop_length, + win_length=self.win_length, + window=torch.hann_window(self.win_length, device=device), + ) + + with torch.no_grad(): + bias_audio = vocoder(mel_input).float().squeeze(0) + bias_spec, _ = self.stft(bias_audio) + + self.register_buffer("bias_spec", bias_spec[:, :, 0][:, :, None]) + + @torch.inference_mode() + def forward(self, audio, strength=0.0005): + audio_spec, audio_angles = self.stft(audio) + audio_spec_denoised = audio_spec - self.bias_spec.to(audio.device) * strength + audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) + audio_denoised = self.istft(audio_spec_denoised, audio_angles) + return audio_denoised diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/env.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/env.py new file mode 100644 index 0000000..9ea4f94 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/env.py @@ -0,0 +1,17 @@ +""" from https://github.com/jik876/hifi-gan """ + +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/meldataset.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/meldataset.py new file mode 100644 index 0000000..d1b3a90 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/meldataset.py @@ -0,0 +1,217 @@ +""" from https://github.com/jik876/hifi-gan """ + +import math +import os +import random + +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from librosa.util import normalize +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement,global-variable-not-assigned + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def get_dataset_filelist(a): + with open(a.input_training_file, encoding="utf-8") as fi: + training_files = [ + os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 + ] + + with open(a.input_validation_file, encoding="utf-8") as fi: + validation_files = [ + os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 + ] + return training_files, validation_files + + +class MelDataset(torch.utils.data.Dataset): + def __init__( + self, + training_files, + segment_size, + n_fft, + num_mels, + hop_size, + win_size, + sampling_rate, + fmin, + fmax, + split=True, + shuffle=True, + n_cache_reuse=1, + device=None, + fmax_loss=None, + fine_tuning=False, + base_mels_path=None, + ): + self.audio_files = training_files + random.seed(1234) + if shuffle: + random.shuffle(self.audio_files) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.fine_tuning = fine_tuning + self.base_mels_path = base_mels_path + + def __getitem__(self, index): + filename = self.audio_files[index] + if self._cache_ref_count == 0: + audio, sampling_rate = load_wav(filename) + audio = audio / MAX_WAV_VALUE + if not self.fine_tuning: + audio = normalize(audio) * 0.95 + self.cached_wav = audio + if sampling_rate != self.sampling_rate: + raise ValueError(f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR") + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + if not self.fine_tuning: + if self.split: + if audio.size(1) >= self.segment_size: + max_audio_start = audio.size(1) - self.segment_size + audio_start = random.randint(0, max_audio_start) + audio = audio[:, audio_start : audio_start + self.segment_size] + else: + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") + + mel = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax, + center=False, + ) + else: + mel = np.load(os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + ".npy")) + mel = torch.from_numpy(mel) + + if len(mel.shape) < 3: + mel = mel.unsqueeze(0) + + if self.split: + frames_per_seg = math.ceil(self.segment_size / self.hop_size) + + if audio.size(1) >= self.segment_size: + mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) + mel = mel[:, :, mel_start : mel_start + frames_per_seg] + audio = audio[:, mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size] + else: + mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant") + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") + + mel_loss = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax_loss, + center=False, + ) + + return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) + + def __len__(self): + return len(self.audio_files) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/models.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/models.py new file mode 100644 index 0000000..57305ef --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/models.py @@ -0,0 +1,368 @@ +""" from https://github.com/jik876/hifi-gan """ + +import torch +import torch.nn as nn # pylint: disable=consider-using-from-import +import torch.nn.functional as F +from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d +from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm + +from .xutils import get_padding, init_weights + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super().__init__() + self.h = h + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super().__init__() + self.h = h + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super().__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + h.upsample_initial_channel // (2**i), + h.upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for _, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super().__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super().__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for _, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super().__init__() + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super().__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + self.meanpools = nn.ModuleList([AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg**2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/xutils.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/xutils.py new file mode 100644 index 0000000..eefadcb --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/hifigan/xutils.py @@ -0,0 +1,60 @@ +""" from https://github.com/jik876/hifi-gan """ + +import glob +import os + +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print(f"Loading '{filepath}'") + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print(f"Saving checkpoint to {filepath}") + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/baselightningmodule.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/baselightningmodule.py new file mode 100644 index 0000000..f8abe7b --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/baselightningmodule.py @@ -0,0 +1,210 @@ +""" +This is a base lightning module that can be used to train a model. +The benefit of this abstraction is that all the logic outside of model definition can be reused for different models. +""" +import inspect +from abc import ABC +from typing import Any, Dict + +import torch +from lightning import LightningModule +from lightning.pytorch.utilities import grad_norm + +from matcha import utils +from matcha.utils.utils import plot_tensor + +log = utils.get_pylogger(__name__) + + +class BaseLightningClass(LightningModule, ABC): + def update_data_statistics(self, data_statistics): + if data_statistics is None: + data_statistics = { + "mel_mean": 0.0, + "mel_std": 1.0, + } + + self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"])) + self.register_buffer("mel_std", torch.tensor(data_statistics["mel_std"])) + + def configure_optimizers(self) -> Any: + optimizer = self.hparams.optimizer(params=self.parameters()) + if self.hparams.scheduler not in (None, {}): + scheduler_args = {} + # Manage last epoch for exponential schedulers + if "last_epoch" in inspect.signature(self.hparams.scheduler.scheduler).parameters: + if hasattr(self, "ckpt_loaded_epoch"): + current_epoch = self.ckpt_loaded_epoch - 1 + else: + current_epoch = -1 + + scheduler_args.update({"optimizer": optimizer}) + scheduler = self.hparams.scheduler.scheduler(**scheduler_args) + scheduler.last_epoch = current_epoch + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": scheduler, + "interval": self.hparams.scheduler.lightning_args.interval, + "frequency": self.hparams.scheduler.lightning_args.frequency, + "name": "learning_rate", + }, + } + + return {"optimizer": optimizer} + + def get_losses(self, batch): + x, x_lengths = batch["x"], batch["x_lengths"] + y, y_lengths = batch["y"], batch["y_lengths"] + spks = batch["spks"] + + dur_loss, prior_loss, diff_loss, *_ = self( + x=x, + x_lengths=x_lengths, + y=y, + y_lengths=y_lengths, + spks=spks, + out_size=self.out_size, + durations=batch["durations"], + ) + return { + "dur_loss": dur_loss, + "prior_loss": prior_loss, + "diff_loss": diff_loss, + } + + def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + self.ckpt_loaded_epoch = checkpoint["epoch"] # pylint: disable=attribute-defined-outside-init + + def training_step(self, batch: Any, batch_idx: int): + loss_dict = self.get_losses(batch) + self.log( + "step", + float(self.global_step), + on_step=True, + prog_bar=True, + logger=True, + sync_dist=True, + ) + + self.log( + "sub_loss/train_dur_loss", + loss_dict["dur_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/train_prior_loss", + loss_dict["prior_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/train_diff_loss", + loss_dict["diff_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + total_loss = sum(loss_dict.values()) + self.log( + "loss/train", + total_loss, + on_step=True, + on_epoch=True, + logger=True, + prog_bar=True, + sync_dist=True, + ) + + return {"loss": total_loss, "log": loss_dict} + + def validation_step(self, batch: Any, batch_idx: int): + loss_dict = self.get_losses(batch) + self.log( + "sub_loss/val_dur_loss", + loss_dict["dur_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/val_prior_loss", + loss_dict["prior_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/val_diff_loss", + loss_dict["diff_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + total_loss = sum(loss_dict.values()) + self.log( + "loss/val", + total_loss, + on_step=True, + on_epoch=True, + logger=True, + prog_bar=True, + sync_dist=True, + ) + + return total_loss + + def on_validation_end(self) -> None: + if self.trainer.is_global_zero: + one_batch = next(iter(self.trainer.val_dataloaders)) + if self.current_epoch == 0: + log.debug("Plotting original samples") + for i in range(2): + y = one_batch["y"][i].unsqueeze(0).to(self.device) + self.logger.experiment.add_image( + f"original/{i}", + plot_tensor(y.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + + log.debug("Synthesising...") + for i in range(2): + x = one_batch["x"][i].unsqueeze(0).to(self.device) + x_lengths = one_batch["x_lengths"][i].unsqueeze(0).to(self.device) + spks = one_batch["spks"][i].unsqueeze(0).to(self.device) if one_batch["spks"] is not None else None + output = self.synthesise(x[:, :x_lengths], x_lengths, n_timesteps=10, spks=spks) + y_enc, y_dec = output["encoder_outputs"], output["decoder_outputs"] + attn = output["attn"] + self.logger.experiment.add_image( + f"generated_enc/{i}", + plot_tensor(y_enc.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + self.logger.experiment.add_image( + f"generated_dec/{i}", + plot_tensor(y_dec.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + self.logger.experiment.add_image( + f"alignment/{i}", + plot_tensor(attn.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + + def on_before_optimizer_step(self, optimizer): + self.log_dict({f"grad_norm/{k}": v for k, v in grad_norm(self, norm_type=2).items()}) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/decoder.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/decoder.py new file mode 100644 index 0000000..504f88b --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/decoder.py @@ -0,0 +1,443 @@ +import math +from typing import Optional + +import torch +import torch.nn as nn # pylint: disable=consider-using-from-import +import torch.nn.functional as F +from conformer import ConformerBlock +from diffusers.models.activations import get_activation +from einops import pack, rearrange, repeat + +from matcha.models.components.transformer import BasicTransformerBlock + + +class SinusoidalPosEmb(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even" + + def forward(self, x, scale=1000): + if x.ndim < 1: + x = x.unsqueeze(0) + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class Block1D(torch.nn.Module): + def __init__(self, dim, dim_out, groups=8): + super().__init__() + self.block = torch.nn.Sequential( + torch.nn.Conv1d(dim, dim_out, 3, padding=1), + torch.nn.GroupNorm(groups, dim_out), + nn.Mish(), + ) + + def forward(self, x, mask): + output = self.block(x * mask) + return output * mask + + +class ResnetBlock1D(torch.nn.Module): + def __init__(self, dim, dim_out, time_emb_dim, groups=8): + super().__init__() + self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out)) + + self.block1 = Block1D(dim, dim_out, groups=groups) + self.block2 = Block1D(dim_out, dim_out, groups=groups) + + self.res_conv = torch.nn.Conv1d(dim, dim_out, 1) + + def forward(self, x, mask, time_emb): + h = self.block1(x, mask) + h += self.mlp(time_emb).unsqueeze(-1) + h = self.block2(h, mask) + output = h + self.res_conv(x * mask) + return output + + +class Downsample1D(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1) + + def forward(self, x): + return self.conv(x) + + +class TimestepEmbedding(nn.Module): + def __init__( + self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + ): + super().__init__() + + self.linear_1 = nn.Linear(in_channels, time_embed_dim) + + if cond_proj_dim is not None: + self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False) + else: + self.cond_proj = None + + self.act = get_activation(act_fn) + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out) + + if post_act_fn is None: + self.post_act = None + else: + self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +class Upsample1D(nn.Module): + """A 1D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels, use_conv=False, use_conv_transpose=True, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + self.conv = None + if use_conv_transpose: + self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1) + elif use_conv: + self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, inputs): + assert inputs.shape[1] == self.channels + if self.use_conv_transpose: + return self.conv(inputs) + + outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest") + + if self.use_conv: + outputs = self.conv(outputs) + + return outputs + + +class ConformerWrapper(ConformerBlock): + def __init__( # pylint: disable=useless-super-delegation + self, + *, + dim, + dim_head=64, + heads=8, + ff_mult=4, + conv_expansion_factor=2, + conv_kernel_size=31, + attn_dropout=0, + ff_dropout=0, + conv_dropout=0, + conv_causal=False, + ): + super().__init__( + dim=dim, + dim_head=dim_head, + heads=heads, + ff_mult=ff_mult, + conv_expansion_factor=conv_expansion_factor, + conv_kernel_size=conv_kernel_size, + attn_dropout=attn_dropout, + ff_dropout=ff_dropout, + conv_dropout=conv_dropout, + conv_causal=conv_causal, + ) + + def forward( + self, + hidden_states, + attention_mask, + encoder_hidden_states=None, + encoder_attention_mask=None, + timestep=None, + ): + return super().forward(x=hidden_states, mask=attention_mask.bool()) + + +class Decoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + down_block_type="transformer", + mid_block_type="transformer", + up_block_type="transformer", + ): + super().__init__() + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + self.get_block( + down_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for i in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + self.get_block( + mid_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + + resnet = ResnetBlock1D( + dim=2 * input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + self.get_block( + up_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + + self.final_block = Block1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + + self.initialize_weights() + # nn.init.normal_(self.final_proj.weight) + + @staticmethod + def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn): + if block_type == "conformer": + block = ConformerWrapper( + dim=dim, + dim_head=attention_head_dim, + heads=num_heads, + ff_mult=1, + conv_expansion_factor=2, + ff_dropout=dropout, + attn_dropout=dropout, + conv_dropout=dropout, + conv_kernel_size=31, + ) + elif block_type == "transformer": + block = BasicTransformerBlock( + dim=dim, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + else: + raise ValueError(f"Unknown block type {block_type}") + + return block + + def initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + elif isinstance(m, nn.GroupNorm): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, mask, mu, t, spks=None, cond=None): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + + t = self.time_embeddings(t) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c") + mask_down = rearrange(mask_down, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_down, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_down = rearrange(mask_down, "b t -> b 1 t") + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c") + mask_mid = rearrange(mask_mid, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_mid, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_mid = rearrange(mask_mid, "b t -> b 1 t") + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + x = resnet(pack([x, hiddens.pop()], "b * t")[0], mask_up, t) + x = rearrange(x, "b c t -> b t c") + mask_up = rearrange(mask_up, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_up, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_up = rearrange(mask_up, "b t -> b 1 t") + x = upsample(x * mask_up) + + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + + return output * mask diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/flow_matching.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/flow_matching.py new file mode 100644 index 0000000..5cad743 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/flow_matching.py @@ -0,0 +1,132 @@ +from abc import ABC + +import torch +import torch.nn.functional as F + +from matcha.models.components.decoder import Decoder +from matcha.utils.pylogger import get_pylogger + +log = get_pylogger(__name__) + + +class BASECFM(torch.nn.Module, ABC): + def __init__( + self, + n_feats, + cfm_params, + n_spks=1, + spk_emb_dim=128, + ): + super().__init__() + self.n_feats = n_feats + self.n_spks = n_spks + self.spk_emb_dim = spk_emb_dim + self.solver = cfm_params.solver + if hasattr(cfm_params, "sigma_min"): + self.sigma_min = cfm_params.sigma_min + else: + self.sigma_min = 1e-4 + + self.estimator = None + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + z = torch.randn_like(mu) * temperature + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond) + + def solve_euler(self, x, t_span, mu, mask, spks, cond): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + """ + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + + # I am storing this because I can later plot it by putting a debugger here and saving it to a file + # Or in future might add like a return_all_steps flag + sol = [] + + for step in range(1, len(t_span)): + dphi_dt = self.estimator(x, mask, mu, t, spks, cond) + + x = x + dt * dphi_dt + t = t + dt + sol.append(x) + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + return sol[-1] + + def compute_loss(self, x1, mask, mu, spks=None, cond=None): + """Computes diffusion loss + + Args: + x1 (torch.Tensor): Target + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): target mask + shape: (batch_size, 1, mel_timesteps) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + spks (torch.Tensor, optional): speaker embedding. Defaults to None. + shape: (batch_size, spk_emb_dim) + + Returns: + loss: conditional flow matching loss + y: conditional flow + shape: (batch_size, n_feats, mel_timesteps) + """ + b, _, t = mu.shape + + # random timestep + t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype) + # sample noise p(x_0) + z = torch.randn_like(x1) + + y = (1 - (1 - self.sigma_min) * t) * z + t * x1 + u = x1 - (1 - self.sigma_min) * z + + loss = F.mse_loss(self.estimator(y, mask, mu, t.squeeze(), spks), u, reduction="sum") / ( + torch.sum(mask) * u.shape[1] + ) + return loss, y + + +class CFM(BASECFM): + def __init__(self, in_channels, out_channel, cfm_params, decoder_params, n_spks=1, spk_emb_dim=64): + super().__init__( + n_feats=in_channels, + cfm_params=cfm_params, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + + in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0) + # Just change the architecture of the estimator here + self.estimator = Decoder(in_channels=in_channels, out_channels=out_channel, **decoder_params) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/text_encoder.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/text_encoder.py new file mode 100644 index 0000000..66e7164 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/text_encoder.py @@ -0,0 +1,410 @@ +""" from https://github.com/jaywalnut310/glow-tts """ + +import math + +import torch +import torch.nn as nn # pylint: disable=consider-using-from-import +from einops import rearrange + +import matcha.utils as utils # pylint: disable=consider-using-from-import +from matcha.utils.model import sequence_mask + +log = utils.get_pylogger(__name__) + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-4): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = torch.nn.Parameter(torch.ones(channels)) + self.beta = torch.nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + + self.conv_layers = torch.nn.ModuleList() + self.norm_layers = torch.nn.ModuleList() + self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout)) + for _ in range(n_layers - 1): + self.conv_layers.append( + torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2) + ) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + +class DurationPredictor(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout): + super().__init__() + self.in_channels = in_channels + self.filter_channels = filter_channels + self.p_dropout = p_dropout + + self.drop = torch.nn.Dropout(p_dropout) + self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_1 = LayerNorm(filter_channels) + self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_2 = LayerNorm(filter_channels) + self.proj = torch.nn.Conv1d(filter_channels, 1, 1) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask + + +class RotaryPositionalEmbeddings(nn.Module): + """ + ## RoPE module + + Rotary encoding transforms pairs of features by rotating in the 2D plane. + That is, it organizes the $d$ features as $\frac{d}{2}$ pairs. + Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it + by an angle depending on the position of the token. + """ + + def __init__(self, d: int, base: int = 10_000): + r""" + * `d` is the number of features $d$ + * `base` is the constant used for calculating $\Theta$ + """ + super().__init__() + + self.base = base + self.d = int(d) + self.cos_cached = None + self.sin_cached = None + + def _build_cache(self, x: torch.Tensor): + r""" + Cache $\cos$ and $\sin$ values + """ + # Return if cache is already built + if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]: + return + + # Get sequence length + seq_len = x.shape[0] + + # $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.einsum("n,d->nd", seq_idx, theta) + + # Concatenate so that for row $m$ we have + # $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$ + idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1) + + # Cache them + self.cos_cached = idx_theta2.cos()[:, None, None, :] + self.sin_cached = idx_theta2.sin()[:, None, None, :] + + def _neg_half(self, x: torch.Tensor): + # $\frac{d}{2}$ + d_2 = self.d // 2 + + # Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$ + return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1) + + def forward(self, x: torch.Tensor): + """ + * `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]` + """ + # Cache $\cos$ and $\sin$ values + x = rearrange(x, "b h t d -> t b h d") + + self._build_cache(x) + + # Split the features, we can choose to apply rotary embeddings only to a partial set of features. + x_rope, x_pass = x[..., : self.d], x[..., self.d :] + + # Calculate + # $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$ + neg_half_x = self._neg_half(x_rope) + + x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]]) + + return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d") + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + channels, + out_channels, + n_heads, + heads_share=True, + p_dropout=0.0, + proximal_bias=False, + proximal_init=False, + ): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.heads_share = heads_share + self.proximal_bias = proximal_bias + self.p_dropout = p_dropout + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = torch.nn.Conv1d(channels, channels, 1) + self.conv_k = torch.nn.Conv1d(channels, channels, 1) + self.conv_v = torch.nn.Conv1d(channels, channels, 1) + + # from https://nn.labml.ai/transformers/rope/index.html + self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) + self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) + + self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) + self.drop = torch.nn.Dropout(p_dropout) + + torch.nn.init.xavier_uniform_(self.conv_q.weight) + torch.nn.init.xavier_uniform_(self.conv_k.weight) + if proximal_init: + self.conv_k.weight.data.copy_(self.conv_q.weight.data) + self.conv_k.bias.data.copy_(self.conv_q.bias.data) + torch.nn.init.xavier_uniform_(self.conv_v.weight) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads) + key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads) + value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads) + + query = self.query_rotary_pe(query) + key = self.key_rotary_pe(key) + + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) + + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + p_attn = torch.nn.functional.softmax(scores, dim=-1) + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) + return output, p_attn + + @staticmethod + def _attention_bias_proximal(length): + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2) + self.drop = torch.nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + return x * x_mask + + +class Encoder(nn.Module): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size=1, + p_dropout=0.0, + **kwargs, + ): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.drop = torch.nn.Dropout(p_dropout) + self.attn_layers = torch.nn.ModuleList() + self.norm_layers_1 = torch.nn.ModuleList() + self.ffn_layers = torch.nn.ModuleList() + self.norm_layers_2 = torch.nn.ModuleList() + for _ in range(self.n_layers): + self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + for i in range(self.n_layers): + x = x * x_mask + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class TextEncoder(nn.Module): + def __init__( + self, + encoder_type, + encoder_params, + duration_predictor_params, + n_vocab, + n_spks=1, + spk_emb_dim=128, + ): + super().__init__() + self.encoder_type = encoder_type + self.n_vocab = n_vocab + self.n_feats = encoder_params.n_feats + self.n_channels = encoder_params.n_channels + self.spk_emb_dim = spk_emb_dim + self.n_spks = n_spks + + self.emb = torch.nn.Embedding(n_vocab, self.n_channels) + torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5) + + if encoder_params.prenet: + self.prenet = ConvReluNorm( + self.n_channels, + self.n_channels, + self.n_channels, + kernel_size=5, + n_layers=3, + p_dropout=0.5, + ) + else: + self.prenet = lambda x, x_mask: x + + self.encoder = Encoder( + encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0), + encoder_params.filter_channels, + encoder_params.n_heads, + encoder_params.n_layers, + encoder_params.kernel_size, + encoder_params.p_dropout, + ) + + self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1) + self.proj_w = DurationPredictor( + self.n_channels + (spk_emb_dim if n_spks > 1 else 0), + duration_predictor_params.filter_channels_dp, + duration_predictor_params.kernel_size, + duration_predictor_params.p_dropout, + ) + + def forward(self, x, x_lengths, spks=None): + """Run forward pass to the transformer based encoder and duration predictor + + Args: + x (torch.Tensor): text input + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): text input lengths + shape: (batch_size,) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size,) + + Returns: + mu (torch.Tensor): average output of the encoder + shape: (batch_size, n_feats, max_text_length) + logw (torch.Tensor): log duration predicted by the duration predictor + shape: (batch_size, 1, max_text_length) + x_mask (torch.Tensor): mask for the text input + shape: (batch_size, 1, max_text_length) + """ + x = self.emb(x) * math.sqrt(self.n_channels) + x = torch.transpose(x, 1, -1) + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + x = self.prenet(x, x_mask) + if self.n_spks > 1: + x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1) + x = self.encoder(x, x_mask) + mu = self.proj_m(x) * x_mask + + x_dp = torch.detach(x) + logw = self.proj_w(x_dp, x_mask) + + return mu, logw, x_mask diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/transformer.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/transformer.py new file mode 100644 index 0000000..4d604f5 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/components/transformer.py @@ -0,0 +1,316 @@ +from typing import Any, Dict, Optional + +import torch +import torch.nn as nn # pylint: disable=consider-using-from-import +from diffusers.models.attention import ( + GEGLU, + GELU, + AdaLayerNorm, + AdaLayerNormZero, + ApproximateGELU, +) +from diffusers.models.attention_processor import Attention +from diffusers.models.lora import LoRACompatibleLinear +from diffusers.utils.torch_utils import maybe_allow_in_graph + + +class SnakeBeta(nn.Module): + """ + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__(self, in_features, out_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): + """ + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + """ + super().__init__() + self.in_features = out_features if isinstance(out_features, list) else [out_features] + self.proj = LoRACompatibleLinear(in_features, out_features) + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = nn.Parameter(torch.zeros(self.in_features) * alpha) + self.beta = nn.Parameter(torch.zeros(self.in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = nn.Parameter(torch.ones(self.in_features) * alpha) + self.beta = nn.Parameter(torch.ones(self.in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + """ + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + x = self.proj(x) + if self.alpha_logscale: + alpha = torch.exp(self.alpha) + beta = torch.exp(self.beta) + else: + alpha = self.alpha + beta = self.beta + + x = x + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(torch.sin(x * alpha), 2) + + return x + + +class FeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + dim (`int`): The number of channels in the input. + dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. + mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. + """ + + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + activation_fn: str = "geglu", + final_dropout: bool = False, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + if activation_fn == "gelu": + act_fn = GELU(dim, inner_dim) + if activation_fn == "gelu-approximate": + act_fn = GELU(dim, inner_dim, approximate="tanh") + elif activation_fn == "geglu": + act_fn = GEGLU(dim, inner_dim) + elif activation_fn == "geglu-approximate": + act_fn = ApproximateGELU(dim, inner_dim) + elif activation_fn == "snakebeta": + act_fn = SnakeBeta(dim, inner_dim) + + self.net = nn.ModuleList([]) + # project in + self.net.append(act_fn) + # project dropout + self.net.append(nn.Dropout(dropout)) + # project out + self.net.append(LoRACompatibleLinear(inner_dim, dim_out)) + # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout + if final_dropout: + self.net.append(nn.Dropout(dropout)) + + def forward(self, hidden_states): + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +@maybe_allow_in_graph +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + # scale_qk=False, # uncomment this to not to use flash attention + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ): + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/matcha_tts.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/matcha_tts.py new file mode 100644 index 0000000..31d2557 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/models/matcha_tts.py @@ -0,0 +1,245 @@ +import datetime as dt +import math +import random + +import torch + +import matcha.utils.monotonic_align as monotonic_align # pylint: disable=consider-using-from-import +from matcha import utils +from matcha.models.baselightningmodule import BaseLightningClass +from matcha.models.components.flow_matching import CFM +from matcha.models.components.text_encoder import TextEncoder +from matcha.utils.model import ( + denormalize, + duration_loss, + fix_len_compatibility, + generate_path, + sequence_mask, +) + +log = utils.get_pylogger(__name__) + + +class MatchaTTS(BaseLightningClass): # 🍵 + def __init__( + self, + n_vocab, + n_spks, + spk_emb_dim, + n_feats, + encoder, + decoder, + cfm, + data_statistics, + out_size, + optimizer=None, + scheduler=None, + prior_loss=True, + use_precomputed_durations=False, + ): + super().__init__() + + self.save_hyperparameters(logger=False) + + self.n_vocab = n_vocab + self.n_spks = n_spks + self.spk_emb_dim = spk_emb_dim + self.n_feats = n_feats + self.out_size = out_size + self.prior_loss = prior_loss + self.use_precomputed_durations = use_precomputed_durations + + if n_spks > 1: + self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim) + + self.encoder = TextEncoder( + encoder.encoder_type, + encoder.encoder_params, + encoder.duration_predictor_params, + n_vocab, + n_spks, + spk_emb_dim, + ) + + self.decoder = CFM( + in_channels=2 * encoder.encoder_params.n_feats, + out_channel=encoder.encoder_params.n_feats, + cfm_params=cfm, + decoder_params=decoder, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + + self.update_data_statistics(data_statistics) + + @torch.inference_mode() + def synthesise(self, x, x_lengths, n_timesteps, temperature=1.0, spks=None, length_scale=1.0): + """ + Generates mel-spectrogram from text. Returns: + 1. encoder outputs + 2. decoder outputs + 3. generated alignment + + Args: + x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): lengths of texts in batch. + shape: (batch_size,) + n_timesteps (int): number of steps to use for reverse diffusion in decoder. + temperature (float, optional): controls variance of terminal distribution. + spks (bool, optional): speaker ids. + shape: (batch_size,) + length_scale (float, optional): controls speech pace. + Increase value to slow down generated speech and vice versa. + + Returns: + dict: { + "encoder_outputs": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Average mel spectrogram generated by the encoder + "decoder_outputs": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Refined mel spectrogram improved by the CFM + "attn": torch.Tensor, shape: (batch_size, max_text_length, max_mel_length), + # Alignment map between text and mel spectrogram + "mel": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Denormalized mel spectrogram + "mel_lengths": torch.Tensor, shape: (batch_size,), + # Lengths of mel spectrograms + "rtf": float, + # Real-time factor + } + """ + # For RTF computation + t = dt.datetime.now() + + if self.n_spks > 1: + # Get speaker embedding + spks = self.spk_emb(spks.long()) + + # Get encoder_outputs `mu_x` and log-scaled token durations `logw` + mu_x, logw, x_mask = self.encoder(x, x_lengths, spks) + + w = torch.exp(logw) * x_mask + w_ceil = torch.ceil(w) * length_scale + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_max_length = y_lengths.max() + y_max_length_ = fix_len_compatibility(y_max_length) + + # Using obtained durations `w` construct alignment map `attn` + y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) + attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) + attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) + + # Align encoded text and get mu_y + mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) + mu_y = mu_y.transpose(1, 2) + encoder_outputs = mu_y[:, :, :y_max_length] + + # Generate sample tracing the probability flow + decoder_outputs = self.decoder(mu_y, y_mask, n_timesteps, temperature, spks) + decoder_outputs = decoder_outputs[:, :, :y_max_length] + + t = (dt.datetime.now() - t).total_seconds() + rtf = t * 22050 / (decoder_outputs.shape[-1] * 256) + + return { + "encoder_outputs": encoder_outputs, + "decoder_outputs": decoder_outputs, + "attn": attn[:, :, :y_max_length], + "mel": denormalize(decoder_outputs, self.mel_mean, self.mel_std), + "mel_lengths": y_lengths, + "rtf": rtf, + } + + def forward(self, x, x_lengths, y, y_lengths, spks=None, out_size=None, cond=None, durations=None): + """ + Computes 3 losses: + 1. duration loss: loss between predicted token durations and those extracted by Monotonic Alignment Search (MAS). + 2. prior loss: loss between mel-spectrogram and encoder outputs. + 3. flow matching loss: loss between mel-spectrogram and decoder outputs. + + Args: + x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): lengths of texts in batch. + shape: (batch_size,) + y (torch.Tensor): batch of corresponding mel-spectrograms. + shape: (batch_size, n_feats, max_mel_length) + y_lengths (torch.Tensor): lengths of mel-spectrograms in batch. + shape: (batch_size,) + out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained. + Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size. + spks (torch.Tensor, optional): speaker ids. + shape: (batch_size,) + """ + if self.n_spks > 1: + # Get speaker embedding + spks = self.spk_emb(spks) + + # Get encoder_outputs `mu_x` and log-scaled token durations `logw` + mu_x, logw, x_mask = self.encoder(x, x_lengths, spks) + y_max_length = y.shape[-1] + + y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask) + attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) + + if self.use_precomputed_durations: + attn = generate_path(durations.squeeze(1), attn_mask.squeeze(1)) + else: + # Use MAS to find most likely alignment `attn` between text and mel-spectrogram + with torch.no_grad(): + const = -0.5 * math.log(2 * math.pi) * self.n_feats + factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device) + y_square = torch.matmul(factor.transpose(1, 2), y**2) + y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y) + mu_square = torch.sum(factor * (mu_x**2), 1).unsqueeze(-1) + log_prior = y_square - y_mu_double + mu_square + const + + attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1)) + attn = attn.detach() # b, t_text, T_mel + + # Compute loss between predicted log-scaled durations and those obtained from MAS + # refered to as prior loss in the paper + logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask + dur_loss = duration_loss(logw, logw_, x_lengths) + + # Cut a small segment of mel-spectrogram in order to increase batch size + # - "Hack" taken from Grad-TTS, in case of Grad-TTS, we cannot train batch size 32 on a 24GB GPU without it + # - Do not need this hack for Matcha-TTS, but it works with it as well + if not isinstance(out_size, type(None)): + max_offset = (y_lengths - out_size).clamp(0) + offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy())) + out_offset = torch.LongTensor( + [torch.tensor(random.choice(range(start, end)) if end > start else 0) for start, end in offset_ranges] + ).to(y_lengths) + attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device) + y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device) + + y_cut_lengths = [] + for i, (y_, out_offset_) in enumerate(zip(y, out_offset)): + y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0) + y_cut_lengths.append(y_cut_length) + cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length + y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper] + attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper] + + y_cut_lengths = torch.LongTensor(y_cut_lengths) + y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask) + + attn = attn_cut + y = y_cut + y_mask = y_cut_mask + + # Align encoded text with mel-spectrogram and get mu_y segment + mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) + mu_y = mu_y.transpose(1, 2) + + # Compute loss of the decoder + diff_loss, _ = self.decoder.compute_loss(x1=y, mask=y_mask, mu=mu_y, spks=spks, cond=cond) + + if self.prior_loss: + prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask) + prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats) + else: + prior_loss = 0 + + return dur_loss, prior_loss, diff_loss, attn diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/export.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/export.py new file mode 100644 index 0000000..9b79508 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/export.py @@ -0,0 +1,181 @@ +import argparse +import random +from pathlib import Path + +import numpy as np +import torch +from lightning import LightningModule + +from matcha.cli import VOCODER_URLS, load_matcha, load_vocoder + +DEFAULT_OPSET = 15 + +SEED = 1234 +random.seed(SEED) +np.random.seed(SEED) +torch.manual_seed(SEED) +torch.cuda.manual_seed(SEED) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + + +class MatchaWithVocoder(LightningModule): + def __init__(self, matcha, vocoder): + super().__init__() + self.matcha = matcha + self.vocoder = vocoder + + def forward(self, x, x_lengths, scales, spks=None): + mel, mel_lengths = self.matcha(x, x_lengths, scales, spks) + wavs = self.vocoder(mel).clamp(-1, 1) + lengths = mel_lengths * 256 + return wavs.squeeze(1), lengths + + +def get_exportable_module(matcha, vocoder, n_timesteps): + """ + Return an appropriate `LighteningModule` and output-node names + based on whether the vocoder is embedded in the final graph + """ + + def onnx_forward_func(x, x_lengths, scales, spks=None): + """ + Custom forward function for accepting + scaler parameters as tensors + """ + # Extract scaler parameters from tensors + temperature = scales[0] + length_scale = scales[1] + output = matcha.synthesise(x, x_lengths, n_timesteps, temperature, spks, length_scale) + return output["mel"], output["mel_lengths"] + + # Monkey-patch Matcha's forward function + matcha.forward = onnx_forward_func + + if vocoder is None: + model, output_names = matcha, ["mel", "mel_lengths"] + else: + model = MatchaWithVocoder(matcha, vocoder) + output_names = ["wav", "wav_lengths"] + return model, output_names + + +def get_inputs(is_multi_speaker): + """ + Create dummy inputs for tracing + """ + dummy_input_length = 50 + x = torch.randint(low=0, high=20, size=(1, dummy_input_length), dtype=torch.long) + x_lengths = torch.LongTensor([dummy_input_length]) + + # Scales + temperature = 0.667 + length_scale = 1.0 + scales = torch.Tensor([temperature, length_scale]) + + model_inputs = [x, x_lengths, scales] + input_names = [ + "x", + "x_lengths", + "scales", + ] + + if is_multi_speaker: + spks = torch.LongTensor([1]) + model_inputs.append(spks) + input_names.append("spks") + + return tuple(model_inputs), input_names + + +def main(): + parser = argparse.ArgumentParser(description="Export 🍵 Matcha-TTS to ONNX") + + parser.add_argument( + "checkpoint_path", + type=str, + help="Path to the model checkpoint", + ) + parser.add_argument("output", type=str, help="Path to output `.onnx` file") + parser.add_argument( + "--n-timesteps", type=int, default=5, help="Number of steps to use for reverse diffusion in decoder (default 5)" + ) + parser.add_argument( + "--vocoder-name", + type=str, + choices=list(VOCODER_URLS.keys()), + default=None, + help="Name of the vocoder to embed in the ONNX graph", + ) + parser.add_argument( + "--vocoder-checkpoint-path", + type=str, + default=None, + help="Vocoder checkpoint to embed in the ONNX graph for an `e2e` like experience", + ) + parser.add_argument("--opset", type=int, default=DEFAULT_OPSET, help="ONNX opset version to use (default 15") + + args = parser.parse_args() + + print(f"[🍵] Loading Matcha checkpoint from {args.checkpoint_path}") + print(f"Setting n_timesteps to {args.n_timesteps}") + + checkpoint_path = Path(args.checkpoint_path) + matcha = load_matcha(checkpoint_path.stem, checkpoint_path, "cpu") + + if args.vocoder_name or args.vocoder_checkpoint_path: + assert ( + args.vocoder_name and args.vocoder_checkpoint_path + ), "Both vocoder_name and vocoder-checkpoint are required when embedding the vocoder in the ONNX graph." + vocoder, _ = load_vocoder(args.vocoder_name, args.vocoder_checkpoint_path, "cpu") + else: + vocoder = None + + is_multi_speaker = matcha.n_spks > 1 + + dummy_input, input_names = get_inputs(is_multi_speaker) + model, output_names = get_exportable_module(matcha, vocoder, args.n_timesteps) + + # Set dynamic shape for inputs/outputs + dynamic_axes = { + "x": {0: "batch_size", 1: "time"}, + "x_lengths": {0: "batch_size"}, + } + + if vocoder is None: + dynamic_axes.update( + { + "mel": {0: "batch_size", 2: "time"}, + "mel_lengths": {0: "batch_size"}, + } + ) + else: + print("Embedding the vocoder in the ONNX graph") + dynamic_axes.update( + { + "wav": {0: "batch_size", 1: "time"}, + "wav_lengths": {0: "batch_size"}, + } + ) + + if is_multi_speaker: + dynamic_axes["spks"] = {0: "batch_size"} + + # Create the output directory (if not exists) + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + + model.to_onnx( + args.output, + dummy_input, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + opset_version=args.opset, + export_params=True, + do_constant_folding=True, + ) + print(f"[🍵] ONNX model exported to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/infer.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/infer.py new file mode 100644 index 0000000..89ca925 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/onnx/infer.py @@ -0,0 +1,168 @@ +import argparse +import os +import warnings +from pathlib import Path +from time import perf_counter + +import numpy as np +import onnxruntime as ort +import soundfile as sf +import torch + +from matcha.cli import plot_spectrogram_to_numpy, process_text + + +def validate_args(args): + assert ( + args.text or args.file + ), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms." + assert args.temperature >= 0, "Sampling temperature cannot be negative" + assert args.speaking_rate >= 0, "Speaking rate must be greater than 0" + return args + + +def write_wavs(model, inputs, output_dir, external_vocoder=None): + if external_vocoder is None: + print("The provided model has the vocoder embedded in the graph.\nGenerating waveform directly") + t0 = perf_counter() + wavs, wav_lengths = model.run(None, inputs) + infer_secs = perf_counter() - t0 + mel_infer_secs = vocoder_infer_secs = None + else: + print("[🍵] Generating mel using Matcha") + mel_t0 = perf_counter() + mels, mel_lengths = model.run(None, inputs) + mel_infer_secs = perf_counter() - mel_t0 + print("Generating waveform from mel using external vocoder") + vocoder_inputs = {external_vocoder.get_inputs()[0].name: mels} + vocoder_t0 = perf_counter() + wavs = external_vocoder.run(None, vocoder_inputs)[0] + vocoder_infer_secs = perf_counter() - vocoder_t0 + wavs = wavs.squeeze(1) + wav_lengths = mel_lengths * 256 + infer_secs = mel_infer_secs + vocoder_infer_secs + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for i, (wav, wav_length) in enumerate(zip(wavs, wav_lengths)): + output_filename = output_dir.joinpath(f"output_{i + 1}.wav") + audio = wav[:wav_length] + print(f"Writing audio to {output_filename}") + sf.write(output_filename, audio, 22050, "PCM_24") + + wav_secs = wav_lengths.sum() / 22050 + print(f"Inference seconds: {infer_secs}") + print(f"Generated wav seconds: {wav_secs}") + rtf = infer_secs / wav_secs + if mel_infer_secs is not None: + mel_rtf = mel_infer_secs / wav_secs + print(f"Matcha RTF: {mel_rtf}") + if vocoder_infer_secs is not None: + vocoder_rtf = vocoder_infer_secs / wav_secs + print(f"Vocoder RTF: {vocoder_rtf}") + print(f"Overall RTF: {rtf}") + + +def write_mels(model, inputs, output_dir): + t0 = perf_counter() + mels, mel_lengths = model.run(None, inputs) + infer_secs = perf_counter() - t0 + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for i, mel in enumerate(mels): + output_stem = output_dir.joinpath(f"output_{i + 1}") + plot_spectrogram_to_numpy(mel.squeeze(), output_stem.with_suffix(".png")) + np.save(output_stem.with_suffix(".numpy"), mel) + + wav_secs = (mel_lengths * 256).sum() / 22050 + print(f"Inference seconds: {infer_secs}") + print(f"Generated wav seconds: {wav_secs}") + rtf = infer_secs / wav_secs + print(f"RTF: {rtf}") + + +def main(): + parser = argparse.ArgumentParser( + description=" 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching" + ) + parser.add_argument( + "model", + type=str, + help="ONNX model to use", + ) + parser.add_argument("--vocoder", type=str, default=None, help="Vocoder to use (defaults to None)") + parser.add_argument("--text", type=str, default=None, help="Text to synthesize") + parser.add_argument("--file", type=str, default=None, help="Text file to synthesize") + parser.add_argument("--spk", type=int, default=None, help="Speaker ID") + parser.add_argument( + "--temperature", + type=float, + default=0.667, + help="Variance of the x0 noise (default: 0.667)", + ) + parser.add_argument( + "--speaking-rate", + type=float, + default=1.0, + help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)", + ) + parser.add_argument("--gpu", action="store_true", help="Use CPU for inference (default: use GPU if available)") + parser.add_argument( + "--output-dir", + type=str, + default=os.getcwd(), + help="Output folder to save results (default: current dir)", + ) + + args = parser.parse_args() + args = validate_args(args) + + if args.gpu: + providers = ["GPUExecutionProvider"] + else: + providers = ["CPUExecutionProvider"] + model = ort.InferenceSession(args.model, providers=providers) + + model_inputs = model.get_inputs() + model_outputs = list(model.get_outputs()) + + if args.text: + text_lines = args.text.splitlines() + else: + with open(args.file, encoding="utf-8") as file: + text_lines = file.read().splitlines() + + processed_lines = [process_text(0, line, "cpu") for line in text_lines] + x = [line["x"].squeeze() for line in processed_lines] + # Pad + x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True) + x = x.detach().cpu().numpy() + x_lengths = np.array([line["x_lengths"].item() for line in processed_lines], dtype=np.int64) + inputs = { + "x": x, + "x_lengths": x_lengths, + "scales": np.array([args.temperature, args.speaking_rate], dtype=np.float32), + } + is_multi_speaker = len(model_inputs) == 4 + if is_multi_speaker: + if args.spk is None: + args.spk = 0 + warn = "[!] Speaker ID not provided! Using speaker ID 0" + warnings.warn(warn, UserWarning) + inputs["spks"] = np.repeat(args.spk, x.shape[0]).astype(np.int64) + + has_vocoder_embedded = model_outputs[0].name == "wav" + if has_vocoder_embedded: + write_wavs(model, inputs, args.output_dir) + elif args.vocoder: + external_vocoder = ort.InferenceSession(args.vocoder, providers=providers) + write_wavs(model, inputs, args.output_dir, external_vocoder=external_vocoder) + else: + warn = "[!] A vocoder is not embedded in the graph nor an external vocoder is provided. The mel output will be written as numpy arrays to `*.npy` files in the output directory" + warnings.warn(warn, UserWarning) + write_mels(model, inputs, args.output_dir) + + +if __name__ == "__main__": + main() diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/__init__.py new file mode 100644 index 0000000..50d7b90 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/__init__.py @@ -0,0 +1,57 @@ +""" from https://github.com/keithito/tacotron """ +from matcha.text import cleaners +from matcha.text.symbols import symbols + +# Mappings from symbol to numeric ID and vice versa: +_symbol_to_id = {s: i for i, s in enumerate(symbols)} +_id_to_symbol = {i: s for i, s in enumerate(symbols)} # pylint: disable=unnecessary-comprehension + + +class UnknownCleanerException(Exception): + pass + + +def text_to_sequence(text, cleaner_names): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [] + + clean_text = _clean_text(text, cleaner_names) + for symbol in clean_text: + symbol_id = _symbol_to_id[symbol] + sequence += [symbol_id] + return sequence, clean_text + + +def cleaned_text_to_sequence(cleaned_text): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [_symbol_to_id[symbol] for symbol in cleaned_text] + return sequence + + +def sequence_to_text(sequence): + """Converts a sequence of IDs back to a string""" + result = "" + for symbol_id in sequence: + s = _id_to_symbol[symbol_id] + result += s + return result + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise UnknownCleanerException(f"Unknown cleaner: {name}") + text = cleaner(text) + return text diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/cleaners.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/cleaners.py new file mode 100644 index 0000000..a9d5587 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/cleaners.py @@ -0,0 +1,144 @@ +""" from https://github.com/keithito/tacotron + +Cleaners are transformations that run over the input text at both training and eval time. + +Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" +hyperparameter. Some cleaners are English-specific. You'll typically want to use: + 1. "english_cleaners" for English text + 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using + the Unidecode library (https://pypi.python.org/pypi/Unidecode) + 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update + the symbols in symbols.py to match your data). +""" + +import logging +import re + +import phonemizer +from unidecode import unidecode + +# To avoid excessive logging we set the log level of the phonemizer package to Critical +critical_logger = logging.getLogger("phonemizer") +critical_logger.setLevel(logging.CRITICAL) + +# Intializing the phonemizer globally significantly reduces the speed +# now the phonemizer is not initialising at every call +# Might be less flexible, but it is much-much faster +global_phonemizer = phonemizer.backend.EspeakBackend( + language="en-us", + preserve_punctuation=True, + with_stress=True, + language_switch="remove-flags", + logger=critical_logger, +) + + +# Regular expression matching whitespace: +_whitespace_re = re.compile(r"\s+") + +# Remove brackets +_brackets_re = re.compile(r"[\[\]\(\)\{\}]") + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [ + (re.compile(f"\\b{x[0]}\\.", re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] +] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def lowercase(text): + return text.lower() + + +def remove_brackets(text): + return re.sub(_brackets_re, "", text) + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + """Pipeline for non-English text that transliterates to ASCII.""" + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners2(text): + """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_abbreviations(text) + phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0] + # Added in some cases espeak is not removing brackets + phonemes = remove_brackets(phonemes) + phonemes = collapse_whitespace(phonemes) + return phonemes + + +def ipa_simplifier(text): + replacements = [ + ("ɐ", "ə"), + ("ˈə", "ə"), + ("ʤ", "dʒ"), + ("ʧ", "tʃ"), + ("ᵻ", "ɪ"), + ] + for replacement in replacements: + text = text.replace(replacement[0], replacement[1]) + phonemes = collapse_whitespace(text) + return phonemes + + +# I am removing this due to incompatibility with several version of python +# However, if you want to use it, you can uncomment it +# and install piper-phonemize with the following command: +# pip install piper-phonemize + +# import piper_phonemize +# def english_cleaners_piper(text): +# """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" +# text = convert_to_ascii(text) +# text = lowercase(text) +# text = expand_abbreviations(text) +# phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0]) +# phonemes = collapse_whitespace(phonemes) +# return phonemes diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/numbers.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/numbers.py new file mode 100644 index 0000000..f99a868 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/numbers.py @@ -0,0 +1,71 @@ +""" from https://github.com/keithito/tacotron """ + +import re + +import inflect + +_inflect = inflect.engine() +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") + + +def _remove_commas(m): + return m.group(1).replace(",", "") + + +def _expand_decimal_point(m): + return m.group(1).replace(".", " point ") + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return match + " dollars" + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return f"{dollars} {dollar_unit}, {cents} {cent_unit}" + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return f"{dollars} {dollar_unit}" + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return f"{cents} {cent_unit}" + else: + return "zero dollars" + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return "two thousand" + elif num > 2000 and num < 2010: + return "two thousand " + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + " hundred" + else: + return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ") + else: + return _inflect.number_to_words(num, andword="") + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/symbols.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/symbols.py new file mode 100644 index 0000000..7018df5 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/text/symbols.py @@ -0,0 +1,17 @@ +""" from https://github.com/keithito/tacotron + +Defines the set of symbols used in text input to the model. +""" +_pad = "_" +_punctuation = ';:,.!?¡¿—…"«»“” ' +_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_letters_ipa = ( + "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" +) + + +# Export all symbols: +symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa) + +# Special symbol ids +SPACE_ID = symbols.index(" ") diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/train.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/train.py new file mode 100644 index 0000000..d1d64c6 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/train.py @@ -0,0 +1,122 @@ +from typing import Any, Dict, List, Optional, Tuple + +import hydra +import lightning as L +import rootutils +from lightning import Callback, LightningDataModule, LightningModule, Trainer +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig + +from matcha import utils + +rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) +# ------------------------------------------------------------------------------------ # +# the setup_root above is equivalent to: +# - adding project root dir to PYTHONPATH +# (so you don't need to force user to install project as a package) +# (necessary before importing any local modules e.g. `from src import utils`) +# - setting up PROJECT_ROOT environment variable +# (which is used as a base for paths in "configs/paths/default.yaml") +# (this way all filepaths are the same no matter where you run the code) +# - loading environment variables from ".env" in root dir +# +# you can remove it if you: +# 1. either install project as a package or move entry files to project root dir +# 2. set `root_dir` to "." in "configs/paths/default.yaml" +# +# more info: https://github.com/ashleve/rootutils +# ------------------------------------------------------------------------------------ # + + +log = utils.get_pylogger(__name__) + + +@utils.task_wrapper +def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + """Trains the model. Can additionally evaluate on a testset, using best weights obtained during + training. + + This method is wrapped in optional @task_wrapper decorator, that controls the behavior during + failure. Useful for multiruns, saving info about the crash, etc. + + :param cfg: A DictConfig configuration composed by Hydra. + :return: A tuple with metrics and dict with all instantiated objects. + """ + # set seed for random number generators in pytorch, numpy and python.random + if cfg.get("seed"): + L.seed_everything(cfg.seed, workers=True) + + log.info(f"Instantiating datamodule <{cfg.data._target_}>") # pylint: disable=protected-access + datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data) + + log.info(f"Instantiating model <{cfg.model._target_}>") # pylint: disable=protected-access + model: LightningModule = hydra.utils.instantiate(cfg.model) + + log.info("Instantiating callbacks...") + callbacks: List[Callback] = utils.instantiate_callbacks(cfg.get("callbacks")) + + log.info("Instantiating loggers...") + logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger")) + + log.info(f"Instantiating trainer <{cfg.trainer._target_}>") # pylint: disable=protected-access + trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger) + + object_dict = { + "cfg": cfg, + "datamodule": datamodule, + "model": model, + "callbacks": callbacks, + "logger": logger, + "trainer": trainer, + } + + if logger: + log.info("Logging hyperparameters!") + utils.log_hyperparameters(object_dict) + + if cfg.get("train"): + log.info("Starting training!") + trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path")) + + train_metrics = trainer.callback_metrics + + if cfg.get("test"): + log.info("Starting testing!") + ckpt_path = trainer.checkpoint_callback.best_model_path + if ckpt_path == "": + log.warning("Best ckpt not found! Using current weights for testing...") + ckpt_path = None + trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path) + log.info(f"Best ckpt path: {ckpt_path}") + + test_metrics = trainer.callback_metrics + + # merge train and test metrics + metric_dict = {**train_metrics, **test_metrics} + + return metric_dict, object_dict + + +@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml") +def main(cfg: DictConfig) -> Optional[float]: + """Main entry point for training. + + :param cfg: DictConfig configuration composed by Hydra. + :return: Optional[float] with optimized metric value. + """ + # apply extra utilities + # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.) + utils.extras(cfg) + + # train the model + metric_dict, _ = train(cfg) + + # safely retrieve metric value for hydra-based hyperparameter optimization + metric_value = utils.get_metric_value(metric_dict=metric_dict, metric_name=cfg.get("optimized_metric")) + + # return optimized metric + return metric_value + + +if __name__ == "__main__": + main() # pylint: disable=no-value-for-parameter diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/__init__.py new file mode 100644 index 0000000..074db64 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/__init__.py @@ -0,0 +1,5 @@ +from matcha.utils.instantiators import instantiate_callbacks, instantiate_loggers +from matcha.utils.logging_utils import log_hyperparameters +from matcha.utils.pylogger import get_pylogger +from matcha.utils.rich_utils import enforce_tags, print_config_tree +from matcha.utils.utils import extras, get_metric_value, task_wrapper diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/audio.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/audio.py new file mode 100644 index 0000000..d257f0d --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/audio.py @@ -0,0 +1,82 @@ +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement,global-variable-not-assigned + if f"{str(fmax)}_{str(y.device)}" not in mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py new file mode 100644 index 0000000..305d806 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py @@ -0,0 +1,110 @@ +r""" +The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it +when needed. + +Parameters from hparam.py will be used +""" +import argparse +import json +import os +import sys +from pathlib import Path + +import rootutils +import torch +from hydra import compose, initialize +from omegaconf import open_dict +from tqdm.auto import tqdm + +from matcha.data.text_mel_datamodule import TextMelDataModule +from matcha.utils.logging_utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +def compute_data_statistics(data_loader: torch.utils.data.DataLoader, out_channels: int): + """Generate data mean and standard deviation helpful in data normalisation + + Args: + data_loader (torch.utils.data.Dataloader): _description_ + out_channels (int): mel spectrogram channels + """ + total_mel_sum = 0 + total_mel_sq_sum = 0 + total_mel_len = 0 + + for batch in tqdm(data_loader, leave=False): + mels = batch["y"] + mel_lengths = batch["y_lengths"] + + total_mel_len += torch.sum(mel_lengths) + total_mel_sum += torch.sum(mels) + total_mel_sq_sum += torch.sum(torch.pow(mels, 2)) + + data_mean = total_mel_sum / (total_mel_len * out_channels) + data_std = torch.sqrt((total_mel_sq_sum / (total_mel_len * out_channels)) - torch.pow(data_mean, 2)) + + return {"mel_mean": data_mean.item(), "mel_std": data_std.item()} + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "-i", + "--input-config", + type=str, + default="vctk.yaml", + help="The name of the yaml config file under configs/data", + ) + + parser.add_argument( + "-b", + "--batch-size", + type=int, + default="256", + help="Can have increased batch size for faster computation", + ) + + parser.add_argument( + "-f", + "--force", + action="store_true", + default=False, + required=False, + help="force overwrite the file", + ) + args = parser.parse_args() + output_file = Path(args.input_config).with_suffix(".json") + + if os.path.exists(output_file) and not args.force: + print("File already exists. Use -f to force overwrite") + sys.exit(1) + + with initialize(version_base="1.3", config_path="../../configs/data"): + cfg = compose(config_name=args.input_config, return_hydra_config=True, overrides=[]) + + root_path = rootutils.find_root(search_from=__file__, indicator=".project-root") + + with open_dict(cfg): + del cfg["hydra"] + del cfg["_target_"] + cfg["data_statistics"] = None + cfg["seed"] = 1234 + cfg["batch_size"] = args.batch_size + cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"])) + cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"])) + cfg["load_durations"] = False + + text_mel_datamodule = TextMelDataModule(**cfg) + text_mel_datamodule.setup() + data_loader = text_mel_datamodule.train_dataloader() + log.info("Dataloader loaded! Now computing stats...") + params = compute_data_statistics(data_loader, cfg["n_feats"]) + print(params) + with open(output_file, "w", encoding="utf-8") as dumpfile: + json.dump(params, dumpfile) + + +if __name__ == "__main__": + main() diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/get_durations_from_trained_model.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/get_durations_from_trained_model.py new file mode 100644 index 0000000..0fe2f35 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/get_durations_from_trained_model.py @@ -0,0 +1,195 @@ +r""" +The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it +when needed. + +Parameters from hparam.py will be used +""" +import argparse +import json +import os +import sys +from pathlib import Path + +import lightning +import numpy as np +import rootutils +import torch +from hydra import compose, initialize +from omegaconf import open_dict +from torch import nn +from tqdm.auto import tqdm + +from matcha.cli import get_device +from matcha.data.text_mel_datamodule import TextMelDataModule +from matcha.models.matcha_tts import MatchaTTS +from matcha.utils.logging_utils import pylogger +from matcha.utils.utils import get_phoneme_durations + +log = pylogger.get_pylogger(__name__) + + +def save_durations_to_folder( + attn: torch.Tensor, x_length: int, y_length: int, filepath: str, output_folder: Path, text: str +): + durations = attn.squeeze().sum(1)[:x_length].numpy() + durations_json = get_phoneme_durations(durations, text) + output = output_folder / Path(filepath).name.replace(".wav", ".npy") + with open(output.with_suffix(".json"), "w", encoding="utf-8") as f: + json.dump(durations_json, f, indent=4, ensure_ascii=False) + + np.save(output, durations) + + +@torch.inference_mode() +def compute_durations(data_loader: torch.utils.data.DataLoader, model: nn.Module, device: torch.device, output_folder): + """Generate durations from the model for each datapoint and save it in a folder + + Args: + data_loader (torch.utils.data.DataLoader): Dataloader + model (nn.Module): MatchaTTS model + device (torch.device): GPU or CPU + """ + + for batch in tqdm(data_loader, desc="🍵 Computing durations 🍵:"): + x, x_lengths = batch["x"], batch["x_lengths"] + y, y_lengths = batch["y"], batch["y_lengths"] + spks = batch["spks"] + x = x.to(device) + y = y.to(device) + x_lengths = x_lengths.to(device) + y_lengths = y_lengths.to(device) + spks = spks.to(device) if spks is not None else None + + _, _, _, attn = model( + x=x, + x_lengths=x_lengths, + y=y, + y_lengths=y_lengths, + spks=spks, + ) + attn = attn.cpu() + for i in range(attn.shape[0]): + save_durations_to_folder( + attn[i], + x_lengths[i].item(), + y_lengths[i].item(), + batch["filepaths"][i], + output_folder, + batch["x_texts"][i], + ) + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "-i", + "--input-config", + type=str, + default="ljspeech.yaml", + help="The name of the yaml config file under configs/data", + ) + + parser.add_argument( + "-b", + "--batch-size", + type=int, + default="32", + help="Can have increased batch size for faster computation", + ) + + parser.add_argument( + "-f", + "--force", + action="store_true", + default=False, + required=False, + help="force overwrite the file", + ) + parser.add_argument( + "-c", + "--checkpoint_path", + type=str, + required=True, + help="Path to the checkpoint file to load the model from", + ) + + parser.add_argument( + "-o", + "--output-folder", + type=str, + default=None, + help="Output folder to save the data statistics", + ) + + parser.add_argument( + "--cpu", action="store_true", help="Use CPU for inference, not recommended (default: use GPU if available)" + ) + + args = parser.parse_args() + + with initialize(version_base="1.3", config_path="../../configs/data"): + cfg = compose(config_name=args.input_config, return_hydra_config=True, overrides=[]) + + root_path = rootutils.find_root(search_from=__file__, indicator=".project-root") + + with open_dict(cfg): + del cfg["hydra"] + del cfg["_target_"] + cfg["seed"] = 1234 + cfg["batch_size"] = args.batch_size + cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"])) + cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"])) + cfg["load_durations"] = False + + if args.output_folder is not None: + output_folder = Path(args.output_folder) + else: + output_folder = Path(cfg["train_filelist_path"]).parent / "durations" + + print(f"Output folder set to: {output_folder}") + + if os.path.exists(output_folder) and not args.force: + print("Folder already exists. Use -f to force overwrite") + sys.exit(1) + + output_folder.mkdir(parents=True, exist_ok=True) + + print(f"Preprocessing: {cfg['name']} from training filelist: {cfg['train_filelist_path']}") + print("Loading model...") + device = get_device(args) + model = MatchaTTS.load_from_checkpoint(args.checkpoint_path, map_location=device) + + text_mel_datamodule = TextMelDataModule(**cfg) + text_mel_datamodule.setup() + try: + print("Computing stats for training set if exists...") + train_dataloader = text_mel_datamodule.train_dataloader() + compute_durations(train_dataloader, model, device, output_folder) + except lightning.fabric.utilities.exceptions.MisconfigurationException: + print("No training set found") + + try: + print("Computing stats for validation set if exists...") + val_dataloader = text_mel_datamodule.val_dataloader() + compute_durations(val_dataloader, model, device, output_folder) + except lightning.fabric.utilities.exceptions.MisconfigurationException: + print("No validation set found") + + try: + print("Computing stats for test set if exists...") + test_dataloader = text_mel_datamodule.test_dataloader() + compute_durations(test_dataloader, model, device, output_folder) + except lightning.fabric.utilities.exceptions.MisconfigurationException: + print("No test set found") + + print(f"[+] Done! Data statistics saved to: {output_folder}") + + +if __name__ == "__main__": + # Helps with generating durations for the dataset to train other architectures + # that cannot learn to align due to limited size of dataset + # Example usage: + # python python matcha/utils/get_durations_from_trained_model.py -i ljspeech.yaml -c pretrained_model + # This will create a folder in data/processed_data/durations/ljspeech with the durations + main() diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/instantiators.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/instantiators.py new file mode 100644 index 0000000..5547b4e --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/instantiators.py @@ -0,0 +1,56 @@ +from typing import List + +import hydra +from lightning import Callback +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]: + """Instantiates callbacks from config. + + :param callbacks_cfg: A DictConfig object containing callback configurations. + :return: A list of instantiated callbacks. + """ + callbacks: List[Callback] = [] + + if not callbacks_cfg: + log.warning("No callback configs found! Skipping..") + return callbacks + + if not isinstance(callbacks_cfg, DictConfig): + raise TypeError("Callbacks config must be a DictConfig!") + + for _, cb_conf in callbacks_cfg.items(): + if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf: + log.info(f"Instantiating callback <{cb_conf._target_}>") # pylint: disable=protected-access + callbacks.append(hydra.utils.instantiate(cb_conf)) + + return callbacks + + +def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]: + """Instantiates loggers from config. + + :param logger_cfg: A DictConfig object containing logger configurations. + :return: A list of instantiated loggers. + """ + logger: List[Logger] = [] + + if not logger_cfg: + log.warning("No logger configs found! Skipping...") + return logger + + if not isinstance(logger_cfg, DictConfig): + raise TypeError("Logger config must be a DictConfig!") + + for _, lg_conf in logger_cfg.items(): + if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf: + log.info(f"Instantiating logger <{lg_conf._target_}>") # pylint: disable=protected-access + logger.append(hydra.utils.instantiate(lg_conf)) + + return logger diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/logging_utils.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/logging_utils.py new file mode 100644 index 0000000..1a12d1d --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/logging_utils.py @@ -0,0 +1,53 @@ +from typing import Any, Dict + +from lightning.pytorch.utilities import rank_zero_only +from omegaconf import OmegaConf + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def log_hyperparameters(object_dict: Dict[str, Any]) -> None: + """Controls which config parts are saved by Lightning loggers. + + Additionally saves: + - Number of model parameters + + :param object_dict: A dictionary containing the following objects: + - `"cfg"`: A DictConfig object containing the main config. + - `"model"`: The Lightning model. + - `"trainer"`: The Lightning trainer. + """ + hparams = {} + + cfg = OmegaConf.to_container(object_dict["cfg"]) + model = object_dict["model"] + trainer = object_dict["trainer"] + + if not trainer.logger: + log.warning("Logger not found! Skipping hyperparameter logging...") + return + + hparams["model"] = cfg["model"] + + # save number of model parameters + hparams["model/params/total"] = sum(p.numel() for p in model.parameters()) + hparams["model/params/trainable"] = sum(p.numel() for p in model.parameters() if p.requires_grad) + hparams["model/params/non_trainable"] = sum(p.numel() for p in model.parameters() if not p.requires_grad) + + hparams["data"] = cfg["data"] + hparams["trainer"] = cfg["trainer"] + + hparams["callbacks"] = cfg.get("callbacks") + hparams["extras"] = cfg.get("extras") + + hparams["task_name"] = cfg.get("task_name") + hparams["tags"] = cfg.get("tags") + hparams["ckpt_path"] = cfg.get("ckpt_path") + hparams["seed"] = cfg.get("seed") + + # send hparams to all loggers + for logger in trainer.loggers: + logger.log_hyperparams(hparams) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/model.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/model.py new file mode 100644 index 0000000..869cc60 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/model.py @@ -0,0 +1,90 @@ +""" from https://github.com/jaywalnut310/glow-tts """ + +import numpy as np +import torch + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def fix_len_compatibility(length, num_downsamplings_in_unet=2): + factor = torch.scalar_tensor(2).pow(num_downsamplings_in_unet) + length = (length / factor).ceil() * factor + if not torch.onnx.is_in_onnx_export(): + return length.int().item() + else: + return length + + +def convert_pad_shape(pad_shape): + inverted_shape = pad_shape[::-1] + pad_shape = [item for sublist in inverted_shape for item in sublist] + return pad_shape + + +def generate_path(duration, mask): + device = duration.device + + b, t_x, t_y = mask.shape + cum_duration = torch.cumsum(duration, 1) + path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - torch.nn.functional.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path * mask + return path + + +def duration_loss(logw, logw_, lengths): + loss = torch.sum((logw - logw_) ** 2) / torch.sum(lengths) + return loss + + +def normalize(data, mu, std): + if not isinstance(mu, (float, int)): + if isinstance(mu, list): + mu = torch.tensor(mu, dtype=data.dtype, device=data.device) + elif isinstance(mu, torch.Tensor): + mu = mu.to(data.device) + elif isinstance(mu, np.ndarray): + mu = torch.from_numpy(mu).to(data.device) + mu = mu.unsqueeze(-1) + + if not isinstance(std, (float, int)): + if isinstance(std, list): + std = torch.tensor(std, dtype=data.dtype, device=data.device) + elif isinstance(std, torch.Tensor): + std = std.to(data.device) + elif isinstance(std, np.ndarray): + std = torch.from_numpy(std).to(data.device) + std = std.unsqueeze(-1) + + return (data - mu) / std + + +def denormalize(data, mu, std): + if not isinstance(mu, float): + if isinstance(mu, list): + mu = torch.tensor(mu, dtype=data.dtype, device=data.device) + elif isinstance(mu, torch.Tensor): + mu = mu.to(data.device) + elif isinstance(mu, np.ndarray): + mu = torch.from_numpy(mu).to(data.device) + mu = mu.unsqueeze(-1) + + if not isinstance(std, float): + if isinstance(std, list): + std = torch.tensor(std, dtype=data.dtype, device=data.device) + elif isinstance(std, torch.Tensor): + std = std.to(data.device) + elif isinstance(std, np.ndarray): + std = torch.from_numpy(std).to(data.device) + std = std.unsqueeze(-1) + + return data * std + mu diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py new file mode 100644 index 0000000..eee6e0d --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py @@ -0,0 +1,22 @@ +import numpy as np +import torch + +from matcha.utils.monotonic_align.core import maximum_path_c + + +def maximum_path(value, mask): + """Cython optimised version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + value = value * mask + device = value.device + dtype = value.dtype + value = value.data.cpu().numpy().astype(np.float32) + path = np.zeros_like(value).astype(np.int32) + mask = mask.data.cpu().numpy() + + t_x_max = mask.sum(1)[:, 0].astype(np.int32) + t_y_max = mask.sum(2)[:, 0].astype(np.int32) + maximum_path_c(path, value, t_x_max, t_y_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx new file mode 100644 index 0000000..091fcc3 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx @@ -0,0 +1,47 @@ +import numpy as np + +cimport cython +cimport numpy as np + +from cython.parallel import prange + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil: + cdef int x + cdef int y + cdef float v_prev + cdef float v_cur + cdef float tmp + cdef int index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[x, y-1] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[x-1, y-1] + value[x, y] = max(v_cur, v_prev) + value[x, y] + + for y in range(t_y - 1, -1, -1): + path[index, y] = 1 + if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]): + index = index - 1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil: + cdef int b = values.shape[0] + + cdef int i + for i in prange(b, nogil=True): + maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py new file mode 100644 index 0000000..f22bc6a --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py @@ -0,0 +1,7 @@ +# from distutils.core import setup +# from Cython.Build import cythonize +# import numpy + +# setup(name='monotonic_align', +# ext_modules=cythonize("core.pyx"), +# include_dirs=[numpy.get_include()]) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/pylogger.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/pylogger.py new file mode 100644 index 0000000..6160067 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/pylogger.py @@ -0,0 +1,21 @@ +import logging + +from lightning.pytorch.utilities import rank_zero_only + + +def get_pylogger(name: str = __name__) -> logging.Logger: + """Initializes a multi-GPU-friendly python command line logger. + + :param name: The name of the logger, defaults to ``__name__``. + + :return: A logger object. + """ + logger = logging.getLogger(name) + + # this ensures all logging levels get marked with the rank zero decorator + # otherwise logs would get multiplied for each GPU process in multi-GPU setup + logging_levels = ("debug", "info", "warning", "error", "exception", "fatal", "critical") + for level in logging_levels: + setattr(logger, level, rank_zero_only(getattr(logger, level))) + + return logger diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/rich_utils.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/rich_utils.py new file mode 100644 index 0000000..1f1d6fe --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/rich_utils.py @@ -0,0 +1,101 @@ +from pathlib import Path +from typing import Sequence + +import rich +import rich.syntax +import rich.tree +from hydra.core.hydra_config import HydraConfig +from lightning.pytorch.utilities import rank_zero_only +from omegaconf import DictConfig, OmegaConf, open_dict +from rich.prompt import Prompt + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def print_config_tree( + cfg: DictConfig, + print_order: Sequence[str] = ( + "data", + "model", + "callbacks", + "logger", + "trainer", + "paths", + "extras", + ), + resolve: bool = False, + save_to_file: bool = False, +) -> None: + """Prints the contents of a DictConfig as a tree structure using the Rich library. + + :param cfg: A DictConfig composed by Hydra. + :param print_order: Determines in what order config components are printed. Default is ``("data", "model", + "callbacks", "logger", "trainer", "paths", "extras")``. + :param resolve: Whether to resolve reference fields of DictConfig. Default is ``False``. + :param save_to_file: Whether to export config to the hydra output folder. Default is ``False``. + """ + style = "dim" + tree = rich.tree.Tree("CONFIG", style=style, guide_style=style) + + queue = [] + + # add fields from `print_order` to queue + for field in print_order: + _ = ( + queue.append(field) + if field in cfg + else log.warning(f"Field '{field}' not found in config. Skipping '{field}' config printing...") + ) + + # add all the other fields to queue (not specified in `print_order`) + for field in cfg: + if field not in queue: + queue.append(field) + + # generate config tree from queue + for field in queue: + branch = tree.add(field, style=style, guide_style=style) + + config_group = cfg[field] + if isinstance(config_group, DictConfig): + branch_content = OmegaConf.to_yaml(config_group, resolve=resolve) + else: + branch_content = str(config_group) + + branch.add(rich.syntax.Syntax(branch_content, "yaml")) + + # print config tree + rich.print(tree) + + # save config tree to file + if save_to_file: + with open(Path(cfg.paths.output_dir, "config_tree.log"), "w", encoding="utf-8") as file: + rich.print(tree, file=file) + + +@rank_zero_only +def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None: + """Prompts user to input tags from command line if no tags are provided in config. + + :param cfg: A DictConfig composed by Hydra. + :param save_to_file: Whether to export tags to the hydra output folder. Default is ``False``. + """ + if not cfg.get("tags"): + if "id" in HydraConfig().cfg.hydra.job: + raise ValueError("Specify tags before launching a multirun!") + + log.warning("No tags provided in config. Prompting user to input tags...") + tags = Prompt.ask("Enter a list of comma separated tags", default="dev") + tags = [t.strip() for t in tags.split(",") if t != ""] + + with open_dict(cfg): + cfg.tags = tags + + log.info(f"Tags: {cfg.tags}") + + if save_to_file: + with open(Path(cfg.paths.output_dir, "tags.log"), "w", encoding="utf-8") as file: + rich.print(cfg.tags, file=file) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/utils.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/utils.py new file mode 100644 index 0000000..fc3a48e --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/matcha/utils/utils.py @@ -0,0 +1,259 @@ +import os +import sys +import warnings +from importlib.util import find_spec +from math import ceil +from pathlib import Path +from typing import Any, Callable, Dict, Tuple + +import gdown +import matplotlib.pyplot as plt +import numpy as np +import torch +import wget +from omegaconf import DictConfig + +from matcha.utils import pylogger, rich_utils + +log = pylogger.get_pylogger(__name__) + + +def extras(cfg: DictConfig) -> None: + """Applies optional utilities before the task is started. + + Utilities: + - Ignoring python warnings + - Setting tags from command line + - Rich config printing + + :param cfg: A DictConfig object containing the config tree. + """ + # return if no `extras` config + if not cfg.get("extras"): + log.warning("Extras config not found! ") + return + + # disable python warnings + if cfg.extras.get("ignore_warnings"): + log.info("Disabling python warnings! ") + warnings.filterwarnings("ignore") + + # prompt user to input tags from command line if none are provided in the config + if cfg.extras.get("enforce_tags"): + log.info("Enforcing tags! ") + rich_utils.enforce_tags(cfg, save_to_file=True) + + # pretty print config tree using Rich library + if cfg.extras.get("print_config"): + log.info("Printing config tree with Rich! ") + rich_utils.print_config_tree(cfg, resolve=True, save_to_file=True) + + +def task_wrapper(task_func: Callable) -> Callable: + """Optional decorator that controls the failure behavior when executing the task function. + + This wrapper can be used to: + - make sure loggers are closed even if the task function raises an exception (prevents multirun failure) + - save the exception to a `.log` file + - mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later) + - etc. (adjust depending on your needs) + + Example: + ``` + @utils.task_wrapper + def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + ... + return metric_dict, object_dict + ``` + + :param task_func: The task function to be wrapped. + + :return: The wrapped task function. + """ + + def wrap(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + # execute the task + try: + metric_dict, object_dict = task_func(cfg=cfg) + + # things to do if exception occurs + except Exception as ex: + # save exception to `.log` file + log.exception("") + + # some hyperparameter combinations might be invalid or cause out-of-memory errors + # so when using hparam search plugins like Optuna, you might want to disable + # raising the below exception to avoid multirun failure + raise ex + + # things to always do after either success or exception + finally: + # display output dir path in terminal + log.info(f"Output dir: {cfg.paths.output_dir}") + + # always close wandb run (even if exception occurs so multirun won't fail) + if find_spec("wandb"): # check if wandb is installed + import wandb + + if wandb.run: + log.info("Closing wandb!") + wandb.finish() + + return metric_dict, object_dict + + return wrap + + +def get_metric_value(metric_dict: Dict[str, Any], metric_name: str) -> float: + """Safely retrieves value of the metric logged in LightningModule. + + :param metric_dict: A dict containing metric values. + :param metric_name: The name of the metric to retrieve. + :return: The value of the metric. + """ + if not metric_name: + log.info("Metric name is None! Skipping metric value retrieval...") + return None + + if metric_name not in metric_dict: + raise ValueError( + f"Metric value not found! \n" + "Make sure metric name logged in LightningModule is correct!\n" + "Make sure `optimized_metric` name in `hparams_search` config is correct!" + ) + + metric_value = metric_dict[metric_name].item() + log.info(f"Retrieved metric value! <{metric_name}={metric_value}>") + + return metric_value + + +def intersperse(lst, item): + # Adds blank symbol + result = [item] * (len(lst) * 2 + 1) + result[1::2] = lst + return result + + +def save_figure_to_numpy(fig): + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + return data + + +def plot_tensor(tensor): + plt.style.use("default") + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.tight_layout() + fig.canvas.draw() + data = save_figure_to_numpy(fig) + plt.close() + return data + + +def save_plot(tensor, savepath): + plt.style.use("default") + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.tight_layout() + fig.canvas.draw() + plt.savefig(savepath) + plt.close() + + +def to_numpy(tensor): + if isinstance(tensor, np.ndarray): + return tensor + elif isinstance(tensor, torch.Tensor): + return tensor.detach().cpu().numpy() + elif isinstance(tensor, list): + return np.array(tensor) + else: + raise TypeError("Unsupported type for conversion to numpy array") + + +def get_user_data_dir(appname="matcha_tts"): + """ + Args: + appname (str): Name of application + + Returns: + Path: path to user data directory + """ + + MATCHA_HOME = os.environ.get("MATCHA_HOME") + if MATCHA_HOME is not None: + ans = Path(MATCHA_HOME).expanduser().resolve(strict=False) + elif sys.platform == "win32": + import winreg # pylint: disable=import-outside-toplevel + + key = winreg.OpenKey( + winreg.HKEY_CURRENT_USER, + r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders", + ) + dir_, _ = winreg.QueryValueEx(key, "Local AppData") + ans = Path(dir_).resolve(strict=False) + elif sys.platform == "darwin": + ans = Path("~/Library/Application Support/").expanduser() + else: + ans = Path.home().joinpath(".local/share") + + final_path = ans.joinpath(appname) + final_path.mkdir(parents=True, exist_ok=True) + return final_path + + +def assert_model_downloaded(checkpoint_path, url, use_wget=True): + if Path(checkpoint_path).exists(): + log.debug(f"[+] Model already present at {checkpoint_path}!") + print(f"[+] Model already present at {checkpoint_path}!") + return + log.info(f"[-] Model not found at {checkpoint_path}! Will download it") + print(f"[-] Model not found at {checkpoint_path}! Will download it") + checkpoint_path = str(checkpoint_path) + if not use_wget: + gdown.download(url=url, output=checkpoint_path, quiet=False, fuzzy=True) + else: + wget.download(url=url, out=checkpoint_path) + + +def get_phoneme_durations(durations, phones): + prev = durations[0] + merged_durations = [] + # Convolve with stride 2 + for i in range(1, len(durations), 2): + if i == len(durations) - 2: + # if it is last take full value + next_half = durations[i + 1] + else: + next_half = ceil(durations[i + 1] / 2) + + curr = prev + durations[i] + next_half + prev = durations[i + 1] - next_half + merged_durations.append(curr) + + assert len(phones) == len(merged_durations) + assert len(merged_durations) == (len(durations) - 1) // 2 + + merged_durations = torch.cumsum(torch.tensor(merged_durations), 0, dtype=torch.long) + start = torch.tensor(0) + duration_json = [] + for i, duration in enumerate(merged_durations): + duration_json.append( + { + phones[i]: { + "starttime": start.item(), + "endtime": duration.item(), + "duration": duration.item() - start.item(), + } + } + ) + start = duration + + assert list(duration_json[-1].values())[0]["endtime"] == sum( + durations + ), f"{list(duration_json[-1].values())[0]['endtime'], sum(durations)}" + return duration_json diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/notebooks/.gitkeep b/src/voice/cosyvoice-src/third_party/Matcha-TTS/notebooks/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/pyproject.toml b/src/voice/cosyvoice-src/third_party/Matcha-TTS/pyproject.toml new file mode 100644 index 0000000..74aa393 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/pyproject.toml @@ -0,0 +1,51 @@ +[build-system] +requires = ["setuptools", "wheel", "cython==0.29.35", "numpy==1.24.3", "packaging"] + +[tool.black] +line-length = 120 +target-version = ['py310'] +exclude = ''' + +( + /( + \.eggs # exclude a few common directories in the + | \.git # root of the project + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist + )/ + | foo.py # also separately exclude a file named foo.py in + # the root of the project +) +''' + +[tool.pytest.ini_options] +addopts = [ + "--color=yes", + "--durations=0", + "--strict-markers", + "--doctest-modules", +] +filterwarnings = [ + "ignore::DeprecationWarning", + "ignore::UserWarning", +] +log_cli = "True" +markers = [ + "slow: slow tests", +] +minversion = "6.0" +testpaths = "tests/" + +[tool.coverage.report] +exclude_lines = [ + "pragma: nocover", + "raise NotImplementedError", + "raise NotImplementedError()", + "if __name__ == .__main__.:", +] diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/requirements.txt b/src/voice/cosyvoice-src/third_party/Matcha-TTS/requirements.txt new file mode 100644 index 0000000..6b0eabf --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/requirements.txt @@ -0,0 +1,44 @@ +# --------- pytorch --------- # +torch>=2.0.0 +torchvision>=0.15.0 +lightning>=2.0.0 +torchmetrics>=0.11.4 + +# --------- hydra --------- # +hydra-core==1.3.2 +hydra-colorlog==1.2.0 +hydra-optuna-sweeper==1.2.0 + +# --------- loggers --------- # +# wandb +# neptune-client +# mlflow +# comet-ml +# aim>=3.16.2 # no lower than 3.16.2, see https://github.com/aimhubio/aim/issues/2550 + +# --------- others --------- # +rootutils # standardizing the project root setup +pre-commit # hooks for applying linters on commit +rich # beautiful text formatting in terminal +pytest # tests +# sh # for running bash commands in some tests (linux/macos only) +phonemizer # phonemization of text +tensorboard +librosa +Cython +numpy +einops +inflect +Unidecode +scipy +torchaudio +matplotlib +pandas +conformer==0.3.2 +diffusers # developed using version ==0.25.0 +notebook +ipywidgets +gradio==3.43.2 +gdown +wget +seaborn diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/scripts/schedule.sh b/src/voice/cosyvoice-src/third_party/Matcha-TTS/scripts/schedule.sh new file mode 100644 index 0000000..44b3da1 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/scripts/schedule.sh @@ -0,0 +1,7 @@ +#!/bin/bash +# Schedule execution of many runs +# Run from root folder with: bash scripts/schedule.sh + +python src/train.py trainer.max_epochs=5 logger=csv + +python src/train.py trainer.max_epochs=10 logger=csv diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/setup.py b/src/voice/cosyvoice-src/third_party/Matcha-TTS/setup.py new file mode 100644 index 0000000..5375723 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/setup.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python +import os + +import numpy +from Cython.Build import cythonize +from setuptools import Extension, find_packages, setup + +exts = [ + Extension( + name="matcha.utils.monotonic_align.core", + sources=["matcha/utils/monotonic_align/core.pyx"], + ) +] + +with open("README.md", encoding="utf-8") as readme_file: + README = readme_file.read() + +cwd = os.path.dirname(os.path.abspath(__file__)) +with open(os.path.join(cwd, "matcha", "VERSION"), encoding="utf-8") as fin: + version = fin.read().strip() + + +def get_requires(): + requirements = os.path.join(os.path.dirname(__file__), "requirements.txt") + with open(requirements, encoding="utf-8") as reqfile: + return [str(r).strip() for r in reqfile] + + +setup( + name="matcha-tts", + version=version, + description="🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching", + long_description=README, + long_description_content_type="text/markdown", + author="Shivam Mehta", + author_email="shivam.mehta25@gmail.com", + url="https://shivammehta25.github.io/Matcha-TTS", + install_requires=get_requires(), + include_dirs=[numpy.get_include()], + include_package_data=True, + packages=find_packages(exclude=["tests", "tests/*", "examples", "examples/*"]), + # use this to customize global commands available in the terminal after installing the package + entry_points={ + "console_scripts": [ + "matcha-data-stats=matcha.utils.generate_data_statistics:main", + "matcha-tts=matcha.cli:cli", + "matcha-tts-app=matcha.app:main", + "matcha-tts-get-durations=matcha.utils.get_durations_from_trained_model:main", + ] + }, + ext_modules=cythonize(exts, language_level=3), + python_requires=">=3.9.0", +) diff --git a/src/voice/cosyvoice-src/third_party/Matcha-TTS/synthesis.ipynb b/src/voice/cosyvoice-src/third_party/Matcha-TTS/synthesis.ipynb new file mode 100644 index 0000000..1e47c53 --- /dev/null +++ b/src/voice/cosyvoice-src/third_party/Matcha-TTS/synthesis.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f37f4e3b-f764-4502-a6a2-6417bd9bfab9", + "metadata": {}, + "source": [ + "# Matcha-TTS: A fast TTS architecture with conditional flow matching\n", + "---\n", + "[Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/)\n", + "\n", + "We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM). This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching. Careful design choices additionally ensure each synthesis step is fast to run. The method is probabilistic, non-autoregressive, and learns to speak from scratch without external alignments. Compared to strong pre-trained baseline models, the Matcha-TTS system has the smallest memory footprint, rivals the speed of the fastest models on long utterances, and attains the highest mean opinion score in a listening test.\n", + "\n", + "Demo Page: https://shivammehta25.github.io/Matcha-TTS \\\n", + "Code: https://github.com/shivammehta25/Matcha-TTS\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "148f4bc0-c28e-4670-9a5e-4c7928ab8992", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CUDA_VISIBLE_DEVICES=0\n" + ] + } + ], + "source": [ + "%env CUDA_VISIBLE_DEVICES=0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8d5876c0-b47e-4c80-9e9c-62550f81b64e", + "metadata": {}, + "outputs": [], + "source": [ + "import datetime as dt\n", + "from pathlib import Path\n", + "\n", + "import IPython.display as ipd\n", + "import numpy as np\n", + "import soundfile as sf\n", + "import torch\n", + "from tqdm.auto import tqdm\n", + "\n", + "# Hifigan imports\n", + "from matcha.hifigan.config import v1\n", + "from matcha.hifigan.denoiser import Denoiser\n", + "from matcha.hifigan.env import AttrDict\n", + "from matcha.hifigan.models import Generator as HiFiGAN\n", + "# Matcha imports\n", + "from matcha.models.matcha_tts import MatchaTTS\n", + "from matcha.text import sequence_to_text, text_to_sequence\n", + "from matcha.utils.model import denormalize\n", + "from matcha.utils.utils import get_user_data_dir, intersperse" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1a30306-588c-4f22-8d9b-e2676880b0e5", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "# This allows for real time code changes being reflected in the notebook, no need to restart the kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a312856b-01a9-4d75-a4c8-4666dffa0692", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "markdown", + "id": "88f3b3c3-d014-443b-84eb-e143cdec3e21", + "metadata": {}, + "source": [ + "## Filepaths" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7640a4c1-44ce-447c-a8ff-45012fb7bddd", + "metadata": {}, + "outputs": [], + "source": [ + "MATCHA_CHECKPOINT = get_user_data_dir()/\"matcha_ljspeech.ckpt\"\n", + "HIFIGAN_CHECKPOINT = get_user_data_dir() / \"hifigan_T2_v1\"\n", + "OUTPUT_FOLDER = \"synth_output\"" + ] + }, + { + "cell_type": "markdown", + "id": "6477a3a9-71f2-4d2f-bb86-bdf3e31c2461", + "metadata": {}, + "source": [ + "## Load Matcha-TTS" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "26a16230-04ba-4825-a844-2fb5ab945e24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model loaded! Parameter count: 18,204,193\n" + ] + } + ], + "source": [ + "def load_model(checkpoint_path):\n", + " model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device)\n", + " model.eval()\n", + " return model\n", + "count_params = lambda x: f\"{sum(p.numel() for p in x.parameters()):,}\"\n", + "\n", + "\n", + "model = load_model(MATCHA_CHECKPOINT)\n", + "print(f\"Model loaded! Parameter count: {count_params(model)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3077b84b-e3b6-42e1-a84b-2f7084b13f92", + "metadata": {}, + "source": [ + "## Load HiFi-GAN (Vocoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f6b68184-968d-4868-9029-f0c40e9e68af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing weight norm...\n" + ] + } + ], + "source": [ + "def load_vocoder(checkpoint_path):\n", + " h = AttrDict(v1)\n", + " hifigan = HiFiGAN(h).to(device)\n", + " hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)['generator'])\n", + " _ = hifigan.eval()\n", + " hifigan.remove_weight_norm()\n", + " return hifigan\n", + "\n", + "vocoder = load_vocoder(HIFIGAN_CHECKPOINT)\n", + "denoiser = Denoiser(vocoder, mode='zeros')" + ] + }, + { + "cell_type": "markdown", + "id": "4cbc2ba0-09ff-40e2-9e60-6b77b534f9fb", + "metadata": {}, + "source": [ + "### Helper functions to synthesise" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "880a1879-24fd-4757-849c-850339120796", + "metadata": {}, + "outputs": [], + "source": [ + "@torch.inference_mode()\n", + "def process_text(text: str):\n", + " x = torch.tensor(intersperse(text_to_sequence(text, ['english_cleaners2'])[0], 0),dtype=torch.long, device=device)[None]\n", + " x_lengths = torch.tensor([x.shape[-1]],dtype=torch.long, device=device)\n", + " x_phones = sequence_to_text(x.squeeze(0).tolist())\n", + " return {\n", + " 'x_orig': text,\n", + " 'x': x,\n", + " 'x_lengths': x_lengths,\n", + " 'x_phones': x_phones\n", + " }\n", + "\n", + "\n", + "@torch.inference_mode()\n", + "def synthesise(text, spks=None):\n", + " text_processed = process_text(text)\n", + " start_t = dt.datetime.now()\n", + " output = model.synthesise(\n", + " text_processed['x'], \n", + " text_processed['x_lengths'],\n", + " n_timesteps=n_timesteps,\n", + " temperature=temperature,\n", + " spks=spks,\n", + " length_scale=length_scale\n", + " )\n", + " # merge everything to one dict \n", + " output.update({'start_t': start_t, **text_processed})\n", + " return output\n", + "\n", + "@torch.inference_mode()\n", + "def to_waveform(mel, vocoder):\n", + " audio = vocoder(mel).clamp(-1, 1)\n", + " audio = denoiser(audio.squeeze(0), strength=0.00025).cpu().squeeze()\n", + " return audio.cpu().squeeze()\n", + " \n", + "def save_to_folder(filename: str, output: dict, folder: str):\n", + " folder = Path(folder)\n", + " folder.mkdir(exist_ok=True, parents=True)\n", + " np.save(folder / f'{filename}', output['mel'].cpu().numpy())\n", + " sf.write(folder / f'{filename}.wav', output['waveform'], 22050, 'PCM_24')" + ] + }, + { + "cell_type": "markdown", + "id": "78f857e3-2ef7-4c86-b776-596c4d3cf875", + "metadata": {}, + "source": [ + "## Setup text to synthesise" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2e0a9acd-0845-4192-ba09-b9683e28a3ac", + "metadata": {}, + "outputs": [], + "source": [ + "texts = [\n", + " \"The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.\"\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "a9da9e2d-99b9-4c6f-8a08-c828e2cba121", + "metadata": {}, + "source": [ + "### Hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f0d216e5-4895-4da8-9d24-9e61021d2556", + "metadata": {}, + "outputs": [], + "source": [ + "## Number of ODE Solver steps\n", + "n_timesteps = 10\n", + "\n", + "## Changes to the speaking rate\n", + "length_scale=1.0\n", + "\n", + "## Sampling temperature\n", + "temperature = 0.667" + ] + }, + { + "cell_type": "markdown", + "id": "b93aac89-c7f8-4975-8510-4e763c9689f4", + "metadata": {}, + "source": [ + "## Synthesis" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5a227963-aa12-43b9-a706-1168b6fc0ba5", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8342d12401c54017b0e19b8d293a06bf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00\n", + " \n", + " Your browser does not support the audio element.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of ODE steps: 10\n", + "Mean RTF:\t\t\t\t0.017228 ± 0.000000\n", + "Mean RTF Waveform (incl. vocoder):\t0.021445 ± 0.000000\n" + ] + } + ], + "source": [ + "outputs, rtfs = [], []\n", + "rtfs_w = []\n", + "for i, text in enumerate(tqdm(texts)):\n", + " output = synthesise(text) #, torch.tensor([15], device=device, dtype=torch.long).unsqueeze(0))\n", + " output['waveform'] = to_waveform(output['mel'], vocoder)\n", + "\n", + " # Compute Real Time Factor (RTF) with HiFi-GAN\n", + " t = (dt.datetime.now() - output['start_t']).total_seconds()\n", + " rtf_w = t * 22050 / (output['waveform'].shape[-1])\n", + "\n", + " ## Pretty print\n", + " print(f\"{'*' * 53}\")\n", + " print(f\"Input text - {i}\")\n", + " print(f\"{'-' * 53}\")\n", + " print(output['x_orig'])\n", + " print(f\"{'*' * 53}\")\n", + " print(f\"Phonetised text - {i}\")\n", + " print(f\"{'-' * 53}\")\n", + " print(output['x_phones'])\n", + " print(f\"{'*' * 53}\")\n", + " print(f\"RTF:\\t\\t{output['rtf']:.6f}\")\n", + " print(f\"RTF Waveform:\\t{rtf_w:.6f}\")\n", + " rtfs.append(output['rtf'])\n", + " rtfs_w.append(rtf_w)\n", + "\n", + " ## Display the synthesised waveform\n", + " ipd.display(ipd.Audio(output['waveform'], rate=22050))\n", + "\n", + " ## Save the generated waveform\n", + " save_to_folder(i, output, OUTPUT_FOLDER)\n", + "\n", + "print(f\"Number of ODE steps: {n_timesteps}\")\n", + "print(f\"Mean RTF:\\t\\t\\t\\t{np.mean(rtfs):.6f} ± {np.std(rtfs):.6f}\")\n", + "print(f\"Mean RTF Waveform (incl. vocoder):\\t{np.mean(rtfs_w):.6f} ± {np.std(rtfs_w):.6f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e85c3f-1623-4647-b40c-fa96907656fc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/voice/cosyvoice-test.py b/src/voice/cosyvoice-test.py new file mode 100644 index 0000000..f428f0c --- /dev/null +++ b/src/voice/cosyvoice-test.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +"""CosyVoice2-0.5B 可行性验证脚本：8GB 显存跑中文合成 + 女声克隆 + +用法: py -3 src/voice/cosyvoice-test.py [模型目录] [参考wav] [参考文本] +""" +import os +import sys +import time +import subprocess + +BASE = os.path.dirname(os.path.abspath(__file__)) +MODEL_DIR = sys.argv[1] if len(sys.argv) > 1 else os.path.join(BASE, "models", "CosyVoice2-0.5B") +REF_WAV = sys.argv[2] if len(sys.argv) > 2 else os.path.join(BASE, "models", "melo-tts", "ref_female.wav") +REF_TEXT = sys.argv[3] if len(sys.argv) > 3 else "你好，我是小雅，今天想和你聊聊天。" + +sys.path.insert(0, os.path.join(BASE, "cosyvoice-src")) +sys.path.insert(0, os.path.join(BASE, "cosyvoice-src", "third_party", "Matcha-TTS")) + +def gpu_mem(): + try: + out = subprocess.run( + ["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=5, + ).stdout.strip() + return out + except Exception: + return "N/A" + +def save_wav(tensor, sr, path): + import soundfile as sf + sf.write(path, tensor.squeeze(0).cpu().numpy(), sr) + +def main(): + print(f"[cosyvoice] 模型目录: {MODEL_DIR}", flush=True) + print(f"[cosyvoice] GPU: {gpu_mem()} MiB", flush=True) + t0 = time.time() + + from cosyvoice.cli.cosyvoice import AutoModel + print(f"[cosyvoice] 加载模型（fp16）…", flush=True) + cosyvoice = AutoModel(model_dir=MODEL_DIR, fp16=True) + print(f"[cosyvoice] 模型加载完成: {time.time()-t0:.1f}s | GPU: {gpu_mem()} MiB | 采样率: {cosyvoice.sample_rate}", flush=True) + + text = "你好，我是小雅，很高兴认识你。今天我们聊聊白龙马的奇妙旅程吧。" + + # --- 测试 1：女声克隆（zero_shot） --- + print(f"[cosyvoice] zero_shot 女声克隆（参考: {os.path.basename(REF_WAV)}）…", flush=True) + t1 = time.time() + chunks = list(cosyvoice.inference_zero_shot(text, REF_TEXT, REF_WAV, stream=False)) + elapsed = time.time() - t1 + import torch + wav = torch.cat([c['tts_speech'] for c in chunks], dim=1) if len(chunks) > 1 else chunks[0]['tts_speech'] + duration = wav.shape[1] / cosyvoice.sample_rate + print(f"[cosyvoice] 合成完成: {elapsed:.1f}s | 音频 {duration:.1f}s | RTF {elapsed/duration:.2f} | GPU: {gpu_mem()} MiB", flush=True) + save_wav(wav, cosyvoice.sample_rate, os.path.join(os.path.dirname(BASE), "cosyvoice_clone_test.wav")) + print("[cosyvoice] 已保存: cosyvoice_clone_test.wav", flush=True) + +if __name__ == "__main__": + main() diff --git a/src/voice/test-cosyvoice-provider.mjs b/src/voice/test-cosyvoice-provider.mjs new file mode 100644 index 0000000..a43579d --- /dev/null +++ b/src/voice/test-cosyvoice-provider.mjs @@ -0,0 +1,19 @@ +// CosyVoice2 provider 端到端测试 +import fs from 'fs' +import path from 'path' +import { fileURLToPath } from 'url' +import { streamTTS } from './tts-providers.js' + +const here = path.dirname(fileURLToPath(import.meta.url)) + +async function main() { + const t0 = Date.now() + const stream = await streamTTS({ text: '白龙马你好，我是小雅。今天验证一下集成的效果。', provider: 'cosyvoice' }) + const chunks = [] + for await (const c of stream) chunks.push(c) + const out = path.join(here, '..', 'cosyvoice_provider_test.wav') + fs.writeFileSync(out, Buffer.concat(chunks)) + console.log(`OK: ${Buffer.concat(chunks).length} 字节 | 总耗时 ${((Date.now() - t0) / 1000).toFixed(1)}s（含模型加载）`) +} + +main().catch((e) => { console.error('FAIL:', e.message); process.exit(1) }) diff --git a/src/voice/tts-providers.js b/src/voice/tts-providers.js index 359b133..1eae1c1 100644 --- a/src/voice/tts-providers.js +++ b/src/voice/tts-providers.js @@ -10,6 +10,7 @@ export const TTS_PROVIDERS = [ { id: 'elevenlabs', label: 'ElevenLabs', streaming: true }, { id: 'volcano', label: '火山引擎', streaming: false }, { id: 'melo', label: 'MeloTTS 本地中文（免费离线）', streaming: false }, + { id: 'cosyvoice', label: 'CosyVoice2 本地中文（女声克隆）', streaming: true }, ] export const TTS_VOICES = { @@ -310,6 +311,16 @@ import { resolvePython, resolveLocalModelDir } from './manager.js' const __voiceDir = path.dirname(fileURLToPath(import.meta.url)) +// 打包后本文件在 app.asar 内，__voiceDir 是 asar 虚拟路径 —— 外部 python +// 无法打开 asar 内的 .py，必须转换为 app.asar.unpacked 实体路径 +// （对齐 manager.js resolveServer 的同一模式） +function resolveScriptPath(p) { + const marker = path.sep + 'app.asar' + path.sep + const idx = p.indexOf(marker) + if (idx === -1) return p + return p.slice(0, idx) + path.sep + 'app.asar.unpacked' + p.slice(idx + marker.length - 1) +} + function streamMelo({ text }) { const modelDir = resolveLocalModelDir('melo-tts') const modelPath = path.join(modelDir, 'model.onnx') @@ -317,7 +328,7 @@ function streamMelo({ text }) { throw new Error(`MeloTTS 本地模型不存在: ${modelPath}\n请将模型放到 ${modelDir} 目录（安装包自带）`) } const { cmd, prefixArgs } = resolvePython() - const scriptPath = path.join(__voiceDir, 'tts_melo.py') + const scriptPath = resolveScriptPath(path.join(__voiceDir, 'tts_melo.py')) const args = [...prefixArgs, scriptPath, modelDir] const child = spawn(cmd, args, { @@ -345,6 +356,100 @@ function streamMelo({ text }) { return stream } +// ── CosyVoice2 本地中文 TTS（常驻进程，女声克隆）───────────────────────────── +// 模型加载约 30s，不能每次 spawn —— 单例常驻进程 + 请求串行队列： +// stdin JSON 请求行 → stdout 先 READY，随后每个请求 [4B大端长度][WAV] +// 进程崩溃自动重建（下个请求时） +let cosyVoiceChild = null +let cosyVoiceChain = Promise.resolve() + +function getCosyVoiceChild() { + if (cosyVoiceChild && !cosyVoiceChild.killed) return cosyVoiceChild + const modelDir = resolveLocalModelDir('CosyVoice2-0.5B') + const modelPath = path.join(modelDir, 'llm.pt') + if (!fs.existsSync(modelPath)) { + throw new Error(`CosyVoice2 本地模型不存在: ${modelPath}\n请将模型放到 ${modelDir} 目录`) + } + const refWav = path.join(resolveLocalModelDir('melo-tts'), 'ref_female.wav') + if (!fs.existsSync(refWav)) { + throw new Error(`CosyVoice2 女声参考音频不存在: ${refWav}\n请用 MeloTTS 合成一段女声存为 ref_female.wav`) + } + const { cmd, prefixArgs } = resolvePython() + const scriptPath = resolveScriptPath(path.join(__voiceDir, 'tts_cosyvoice.py')) + const child = spawn(cmd, [...prefixArgs, scriptPath, modelDir, refWav, '你好，我是小雅，今天想和你聊聊天。'], { + stdio: ['pipe', 'pipe', 'pipe'], + windowsHide: true, + env: { ...process.env, PYTHONIOENCODING: 'utf-8', PYTHONUTF8: '1' }, + }) + child.stderr.on('data', (d) => process.stderr.write(d)) // 透传日志 + child.on('close', () => { cosyVoiceChild = null }) + child.on('error', () => { cosyVoiceChild = null }) + cosyVoiceChild = child + return child +} + +function streamCosyVoice({ text }) { + const stream = new Readable({ read() {} }) + // 严格串行：同一进程同时只处理一个请求，避免响应错配 + cosyVoiceChain = cosyVoiceChain.then(() => new Promise((resolve) => { + let child + try { + child = getCosyVoiceChild() + } catch (err) { + stream.destroy(err) + return resolve() + } + let buf = Buffer.alloc(0) + let state = 'ready' // ready → len → wav + let needLen = null + let done = false + + const cleanup = () => { + child.stdout.off('data', onData) + child.off('error', onError) + child.off('close', onClose) + } + const finish = (err, wav) => { + if (done) return + done = true + cleanup() + if (err) stream.destroy(err) + else { stream.push(wav); stream.push(null) } + resolve() + } + const onError = (err) => finish(new Error(`CosyVoice2 进程错误: ${err.message}`)) + const onClose = (code) => finish(new Error(`CosyVoice2 进程退出 (code ${code})`)) + const onData = (chunk) => { + if (done) return + buf = Buffer.concat([buf, chunk]) + if (state === 'ready') { + const nl = buf.indexOf('\n') + if (nl === -1) return + buf = buf.slice(nl + 1) + state = 'len' + } + if (state === 'len' && buf.length >= 4) { + const n = buf.readUInt32BE(0) + buf = buf.slice(4) + if (n === 0) return finish(new Error('CosyVoice2 合成失败（详见服务日志）')) + needLen = n + state = 'wav' + } + if (state === 'wav' && needLen !== null && buf.length >= needLen) { + const wav = buf.slice(0, needLen) + finish(null, wav) + } + } + child.stdout.on('data', onData) + child.on('error', onError) + child.on('close', onClose) + child.stdin.write(JSON.stringify({ text }) + '\n') + // 超时保险（模型加载 30s + 合成） + setTimeout(() => finish(new Error('CosyVoice2 合成超时')), 180_000).unref?.() + })) + return stream +} + // ── 通用入口 ──────────────────────────────────────────────────────────────── export async function streamTTS({ text, provider, voiceId, keys = {} }) { if (!text?.trim()) throw new Error('TTS: 文本为空') @@ -368,6 +473,8 @@ export async function streamTTS({ text, provider, voiceId, keys = {} }) { return streamVolcano({ text, voiceId, appId: keys.volcanoAppId, token: keys.volcanoToken }) case 'melo': return streamMelo({ text }) + case 'cosyvoice': + return streamCosyVoice({ text }) default: throw new Error(`未知 TTS 服务商: ${provider}，请在设置中选择一个 TTS 服务商`) } diff --git a/src/voice/tts_cosyvoice.py b/src/voice/tts_cosyvoice.py new file mode 100644 index 0000000..7fcf3a2 --- /dev/null +++ b/src/voice/tts_cosyvoice.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +"""CosyVoice2 常驻 TTS 服务（Windows 本地，fp16） + +协议： + stdin 每行一个 JSON 请求 {"text": "..."}（UTF-8） + stdout READY 一行表示模型加载完成；之后每个请求响应： + [4字节大端长度 N][N 字节 WAV] + N=0 表示合成失败（错误信息在 stderr） + stderr 日志/错误 + +用法: py -3 tts_cosyvoice.py +""" +import os +import sys +import json +import io +import struct +import time + +BASE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, os.path.join(BASE, "cosyvoice-src")) +sys.path.insert(0, os.path.join(BASE, "cosyvoice-src", "third_party", "Matcha-TTS")) + +def main(): + if len(sys.argv) < 4: + print("[cosyvoice] 用法: tts_cosyvoice.py ", file=sys.stderr) + sys.exit(1) + model_dir, ref_wav, ref_text = sys.argv[1], sys.argv[2], sys.argv[3] + + from cosyvoice.cli.cosyvoice import AutoModel + t0 = time.time() + print("[cosyvoice] 加载模型…", file=sys.stderr, flush=True) + cosyvoice = AutoModel(model_dir=model_dir, fp16=True) + print(f"[cosyvoice] 加载完成 {time.time()-t0:.1f}s，等待请求", file=sys.stderr, flush=True) + + out = sys.stdout.buffer + sys.stdin.reconfigure(encoding='utf-8', errors='replace') + sys.stdout.reconfigure(encoding='utf-8') + out.write(b"READY\n") + out.flush() + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + req = json.loads(line) + text = req.get("text", "") + if not text.strip(): + out.write(struct.pack(">I", 0)) + out.flush() + continue + t1 = time.time() + chunks = [] + for j in cosyvoice.inference_zero_shot(text, ref_text, ref_wav, stream=True): + chunks.append(j["tts_speech"]) + import torch + wav = torch.cat(chunks, dim=1).squeeze(0).cpu().numpy() + import soundfile as sf + buf = io.BytesIO() + sf.write(buf, wav, cosyvoice.sample_rate, format="WAV") + data = buf.getvalue() + out.write(struct.pack(">I", len(data))) + out.write(data) + out.flush() + dur = len(wav) / cosyvoice.sample_rate + print(f"[cosyvoice] OK {time.time()-t1:.1f}s | {dur:.1f}s 音频", file=sys.stderr, flush=True) + except Exception as e: + import traceback + traceback.print_exc(file=sys.stderr) + try: + out.write(struct.pack(">I", 0)) + out.flush() + except Exception: + pass + +if __name__ == "__main__": + main() diff --git a/src/voice/voxcpm-clone-test.py b/src/voice/voxcpm-clone-test.py new file mode 100644 index 0000000..2876c9e --- /dev/null +++ b/src/voice/voxcpm-clone-test.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""VoxCPM2 音色克隆验证：用参考音频克隆音色合成中文 + +用法: py -3 src/voice/voxcpm-clone-test.py <参考wav> <参考文本> [模型目录] +""" +import os +import sys +import time +import subprocess + +MODEL_DIR = sys.argv[3] if len(sys.argv) > 3 else "src/voice/models/voxcpm2" +REF_WAV = sys.argv[1] if len(sys.argv) > 1 else None +REF_TEXT = sys.argv[2] if len(sys.argv) > 2 else None + +def gpu_mem(): + try: + out = subprocess.run( + ["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=5, + ).stdout.strip() + return out + except Exception: + return "N/A" + +def main(): + print(f"[voxcpm] 模型目录: {MODEL_DIR} | 参考音频: {REF_WAV}", flush=True) + print(f"[voxcpm] GPU: {gpu_mem()} MiB", flush=True) + t0 = time.time() + + from voxcpm import VoxCPM + print(f"[voxcpm] 加载模型（load_denoiser=False）…", flush=True) + model = VoxCPM.from_pretrained(MODEL_DIR, load_denoiser=False) + print(f"[voxcpm] 模型加载完成: {time.time()-t0:.1f}s | GPU: {gpu_mem()} MiB", flush=True) + + text = "你好，我是小雅，很高兴认识你。今天我们聊聊白龙马的奇妙旅程吧。" + + kwargs = {} + if REF_WAV: + kwargs["prompt_wav_path"] = REF_WAV + kwargs["prompt_text"] = REF_TEXT or "" + print(f"[voxcpm] 使用参考音频克隆音色…", flush=True) + + t1 = time.time() + wav = model.generate(text, cfg_value=2.0, inference_timesteps=10, **kwargs) + elapsed = time.time() - t1 + sr = model.tts_model.sample_rate + duration = len(wav) / sr + print(f"[voxcpm] 合成完成: {elapsed:.1f}s | 音频 {duration:.1f}s | RTF {elapsed/duration:.2f} | GPU: {gpu_mem()} MiB", flush=True) + + import soundfile as sf + out = "voxcpm_clone_test.wav" + sf.write(out, wav, sr) + print(f"[voxcpm] 已保存: {out}", flush=True) + +if __name__ == "__main__": + main() diff --git a/src/voice/voxcpm-test.py b/src/voice/voxcpm-test.py new file mode 100644 index 0000000..a772756 --- /dev/null +++ b/src/voice/voxcpm-test.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +"""VoxCPM2 可行性验证脚本：8GB 显存跑一句话中文合成 + +用法: py -3 src/voice/voxcpm-test.py [模型目录] [--stream] +""" +import os +import sys +import time +import subprocess + +MODEL_DIR = sys.argv[1] if len(sys.argv) > 1 else "src/voice/models/voxcpm2" +DO_STREAM = "--stream" in sys.argv + +def gpu_mem(): + try: + out = subprocess.run( + ["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=5, + ).stdout.strip() + return out + except Exception: + return "N/A" + +def main(): + print(f"[voxcpm] 模型目录: {MODEL_DIR}") + print(f"[voxcpm] GPU: {gpu_mem()} MiB") + t0 = time.time() + + from voxcpm import VoxCPM + print(f"[voxcpm] 加载模型（load_denoiser=False）…") + model = VoxCPM.from_pretrained(MODEL_DIR, load_denoiser=False) + print(f"[voxcpm] 模型加载完成: {time.time()-t0:.1f}s | GPU: {gpu_mem()} MiB") + + text = "你好，我是白龙马。今天我们来测试本地语音合成的效果，看看声音是否自然流畅。" + + if DO_STREAM: + # 流式：逐 chunk 输出（如 API 支持） + print(f"[voxcpm] 流式合成中…") + t1 = time.time() + chunks = [] + stream = model.generate_stream(text, cfg_value=2.0, inference_timesteps=10) + for i, chunk in enumerate(stream): + chunks.append(chunk) + print(f" chunk {i}: {len(chunk)} samples | {time.time()-t1:.1f}s") + import numpy as np + wav = np.concatenate(chunks) if chunks else None + else: + print(f"[voxcpm] 合成中…") + t1 = time.time() + wav = model.generate(text, cfg_value=2.0, inference_timesteps=10) + + elapsed = time.time() - t1 + sr = model.tts_model.sample_rate + duration = len(wav) / sr + print(f"[voxcpm] 合成完成: {elapsed:.1f}s | 音频 {duration:.1f}s | RTF {elapsed/duration:.2f} | GPU: {gpu_mem()} MiB") + + import soundfile as sf + out = "voxcpm_test.wav" + sf.write(out, wav, sr) + print(f"[voxcpm] 已保存: {out}") + +if __name__ == "__main__": + main() diff --git a/待办.txt b/待办.txt new file mode 100644 index 0000000..4c7e671 --- /dev/null +++ b/待办.txt @@ -0,0 +1,59 @@ +待办事项（2026-08-09 更新） +============================ + +【1】✅ CosyVoice2-0.5B 本地语音合成（已完成，替代 VoxCPM2） +------------------------------------------------------------ +背景：VoxCPM2 验证后 RTF≈20（3060 8G 上一句话 2 分钟），性能不达标已弃用。 + CosyVoice2-0.5B：fp16 下显存 3.9G/8G，RTF≈2.7，流式首包 ~7s，女声克隆（MeloTTS 参考音频）。 + +已完成： + ✅ 模型：src/voice/models/CosyVoice2-0.5B/（3.8G：llm.pt 2G + flow.pt + hift.pt + speech_tokenizer_v2.onnx + campplus.onnx + CosyVoice-BlankEN/） + ✅ 源码：src/voice/cosyvoice-src/（cosyvoice 包 + third_party/Matcha-TTS + asset/） + ✅ 依赖：hyperpyyaml、onnxruntime、x-transformers、transformers 4.51.3、wetext、pypinyin、conformer、lightning、matcha(源码)、pyworld 0.3.5、openai-whisper、modelscope、torchcodec 0.9.1 等 + ✅ torch cu126 2.9.1 已装（注意：别装 torchvision，会把 torch 覆盖回 CPU 版！） + ✅ 常驻服务：src/voice/tts_cosyvoice.py（stdin JSON 行 → stdout [4B长度+WAV]，READY 协议） + ✅ provider：tts-providers.js 新增 cosyvoice（单例进程 + 串行队列，崩溃自动重建） + ✅ 验证：cosyvoice-test.py / cosyvoice-opt-test.py / test-cosyvoice-provider.mjs（端到端 47s 含加载） + ✅ 女声参考固化：src/voice/models/melo-tts/ref_female.wav（MeloTTS 合成） + +待办/注意： + ⏳ 打包策略未定：CosyVoice2 模型 3.8G，安装包会从 391MB 涨到 4G+。 + 建议：打包排除模型（files 里加 !src/voice/models/CosyVoice2-0.5B/**）， + 首次使用 TTS 时提示手动放置/下载。 + ⏳ 应用内验证：设置 → TTS 服务商选 CosyVoice2 → 对话测试出声 + ⏳ jit 实测更慢（RTF 5.9 vs 2.7），已弃用；flow.decoder.estimator onnx（onnxruntime-gpu）未试，是后续提速方向 + + +【2】⏳ 安装新版安装包，验证应用内 TTS +------------------------------------ +背景：安装包 TTS 失败根因 = streamMelo 脚本路径没做 asar→unpacked 转换（已修复）。 + dist/Bailongma-Setup-2.1.300.exe（03:19，391MB）已修复该 bug，但用户尚未重装验证。 +待办：退出旧版 → 覆盖安装 03:19 的包 → 设置里 TTS 选 MeloTTS 本地 → 测试出声。 +安装版配置已改好（%APPDATA%\Bailongma\config.json：ttsProvider=melo）。 + + +【3】⏳ 代码提交（语音集成修复 + CosyVoice2 集成） +-------------------------------------------------- +未提交的改动： + - src/voice/tts-providers.js（streamMelo asar 修复 + cosyvoice provider） + - src/voice/tts_cosyvoice.py（CosyVoice2 常驻服务） + - src/voice/cosyvoice-src/（CosyVoice2 源码，~10MB，含 Matcha-TTS） + - src/voice/cosyvoice-test.py / cosyvoice-opt-test.py / test-cosyvoice-provider.mjs + - src/voice/voxcpm-test.py / voxcpm-clone-test.py（可保留或删除） + - 待办.txt 本身 +提交后 push gitea（远端：gitea，token 在 .git/config）。 +注意：CosyVoice2 模型 3.8G 不入库（src/voice/models/CosyVoice2-0.5B 需加入 .gitignore） + + +【4】⚠️ 安全事项：config.json 含明文 API 密钥 +--------------------------------------------- +config.json（豆包/火山密钥）被 git 跟踪过且 Gitea 仓库是 public —— 密钥已公开。 +建议：1) 到控制台轮换密钥；2) git rm --cached config.json + gitignore。 +历史提交里的密钥无法删除，轮换是根本解。 + + +【5】💡 可选优化（做完 1-3 后再考虑） +------------------------------------ + - 语音唤醒词（"小克小克"，备份 F:\optimized-backup\voice-system 里有现成脚本） + - CosyVoice2 提速：onnxruntime-gpu + flow.decoder.estimator.onnx（未试） + - 安装包默认引擎已设为本地（voice-panel.js 默认 'local'）
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