From a293e9bbd07220e6f5571a7bdae6f96d28166668 Mon Sep 17 00:00:00 2001 From: jishengpeng <142373325+novateurjsp@users.noreply.github.com> Date: Tue, 20 Feb 2024 22:51:29 +0800 Subject: [PATCH] undate --- README.md | 135 +- baselines/infer_encodec.py | 77 + code/datasplit.py | 45 + code/datasplit2.py | 45 + code/getdata.py | 12 + code/predata.py | 144 + code/random_demo.py | 23 + code/test.py | 36 + configs/languagecodec.yaml | 89 + data/libritts_testother.txt | 5120 +++++++++++++++++ encodec/__init__.py | 12 + encodec/__pycache__/__init__.cpython-310.pyc | Bin 0 -> 280 bytes encodec/__pycache__/distrib.cpython-310.pyc | Bin 0 -> 3754 bytes encodec/__pycache__/model.cpython-310.pyc | Bin 0 -> 11725 bytes encodec/__pycache__/utils.cpython-310.pyc | Bin 0 -> 2692 bytes encodec/distrib.py | 124 + encodec/model.py | 324 ++ encodec/modules/__init__.py | 22 + .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 569 bytes .../modules/__pycache__/conv.cpython-310.pyc | Bin 0 -> 9216 bytes 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| Bin 0 -> 6857 bytes vocos/dataset.py | 81 + vocos/discriminators.py | 202 + vocos/experiment.py | 449 ++ vocos/feature_extractors.py | 117 + vocos/heads.py | 157 + vocos/helpers.py | 71 + vocos/loss.py | 114 + vocos/models.py | 117 + vocos/modules.py | 213 + vocos/pretrained.py | 232 + vocos/pretrained_model.py | 192 + vocos/spectral_ops.py | 192 + 73 files changed, 10688 insertions(+), 2 deletions(-) create mode 100644 baselines/infer_encodec.py create mode 100644 code/datasplit.py create mode 100644 code/datasplit2.py create mode 100644 code/getdata.py create mode 100644 code/predata.py create mode 100644 code/random_demo.py create mode 100644 code/test.py create mode 100644 configs/languagecodec.yaml create mode 100644 data/libritts_testother.txt create mode 100644 encodec/__init__.py create mode 100644 encodec/__pycache__/__init__.cpython-310.pyc create mode 100644 encodec/__pycache__/distrib.cpython-310.pyc create mode 100644 encodec/__pycache__/model.cpython-310.pyc create mode 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100644 vocos/spectral_ops.py diff --git a/README.md b/README.md index 62835ed..c30096f 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,133 @@ -# languagecodec -Official code repository of Language-Codec +# Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models + +[Audio samples](https://languagecodec.github.io) | +Paper [[abs]](https://arxiv.org/abs/2402.12208) [[pdf]](https://arxiv.org/pdf/2402.12208.pdf) + + +## Installation + +To use Language-Codec, install it using: + +```bash +conda create -n xxx python=3.8 +conda activate xxx +pip install -r requirement.txt +``` + +## Infer + +### Part1: Reconstruct audio from raw wav + +```python + +from encodec.utils import convert_audio +import torchaudio +import torch +from vocos.pretrained import Vocos + +device=torch.device('cpu') + +config_path = "xxx/languagecodec/configs/languagecodec.yaml" +model_path = "xxx/xxx.ckpt" +audio_outpath = "xxx" +vocos = Vocos.from_pretrained0802(config_path, model_path) +vocos = vocos.to(device) + +wav, sr = torchaudio.load(audio_path) +wav = convert_audio(wav, sr, 24000, 1) +bandwidth_id = torch.tensor([0]) +wav=wav.to(device) +features,discrete_code= vocos.encode(wav, bandwidth_id=bandwidth_id) +audio_out = vocos.decode(features, bandwidth_id=bandwidth_id) +torchaudio.save(audio_outpath, audio_out, sample_rate=24000, encoding='PCM_S', bits_per_sample=16) +``` + + +### Part2: Generating Discrete Codecs +```python + +from encodec.utils import convert_audio +import torchaudio +import torch +from vocos.pretrained import Vocos + +device=torch.device('cpu') + +config_path = "xxx/languagecodec/configs/languagecodec.yaml" +model_path = "xxx/xxx.ckpt" +vocos = Vocos.from_pretrained0802(config_path, model_path) +vocos = vocos.to(device) + +wav, sr = torchaudio.load(audio_path) +wav = convert_audio(wav, sr, 24000, 1) +bandwidth_id = torch.tensor([0]) +wav=wav.to(device) +_,discrete_code= vocos.encode(wav, bandwidth_id=bandwidth_id) +print(discrete_code) +``` + + + +### Part3: Audio reconstruction through codecs +```python +# audio_tokens [n_q,1,t]/[n_q,t] +features = vocos.codes_to_features(audio_tokens) +bandwidth_id = torch.tensor([0]) +audio_out = vocos.decode(features, bandwidth_id=bandwidth_id) +``` + + + + +## Pre-trained models + +Currently, we have only released the results from our paper, and we plan to release additional checkpoints trained on a larger training dataset within the next two months. + +| Model Name | Dataset | Training Iterations +-------------------------------------------------------------------------------------|---------------|--------------------- +| [languagecodec_paper_8nq](https://huggingface.co/charactr/vocos-mel-24khz) | 3W Hours | 2.0 M + + +## Training + +### Step1: Prepare train dataset +```python +# Process the data into a form similar to xxx/languagecodec/data/libritts_testother.txt +``` + +### Step2: Modifying configuration files +```python +# xxx/languagecodec/configs/languagecodec.yaml +# Modify the values of parameters such as batch_size, filelist_path, save_dir, device +``` + +### Step3: Start training process +Refer to [Pytorch Lightning documentation](https://lightning.ai/docs/pytorch/stable/) for details about customizing the +training pipeline. + +```bash +cd xxx/languagecodec +python train.py fit --config xxx/languagecodec/configs/languagecodec.yaml +``` + + + +## Citation + +If this code contributes to your research, please cite our work: + +``` +@misc{ji2024languagecodec, + title={Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models}, + author={Shengpeng Ji and Minghui Fang and Ziyue Jiang and Rongjie Huang and Jialung Zuo and Shulei Wang and Zhou Zhao}, + year={2024}, + eprint={2402.12208}, + archivePrefix={arXiv}, + primaryClass={eess.AS} +} +``` + +## License + +The code in this repository is released under the MIT license as found in the +[LICENSE](LICENSE) file. \ No newline at end of file diff --git a/baselines/infer_encodec.py b/baselines/infer_encodec.py new file mode 100644 index 0000000..1e992bc --- /dev/null +++ b/baselines/infer_encodec.py @@ -0,0 +1,77 @@ +from encodec import EncodecModel +from encodec.utils import convert_audio + +import torchaudio +import torch + +import os + +import logging + +infer_log_path="/home/jovyan/honor/big-disk/speech/code/languagecodec/测试比较encodec_encodec.log" + +os.system("rm %s"%(infer_log_path)) + +# 设置输出的格式 +LOG_FORMAT = "时间: %(asctime)s - 日志等级: %(levelname)s - 日志信息: %(message)s" +# 对logger进行配置——日志等级&输出格式 +logging.basicConfig(level=logging.INFO, format=LOG_FORMAT, filename=infer_log_path) + + +# Instantiate a pretrained EnCodec model +model = EncodecModel.encodec_model_24khz() +# The number of codebooks used will be determined bythe bandwidth selected. +# E.g. for a bandwidth of 6kbps, `n_q = 8` codebooks are used. +# Supported bandwidths are 1.5kbps (n_q = 2), 3 kbps (n_q = 4), 6 kbps (n_q = 8) and 12 kbps (n_q =16) and 24kbps (n_q=32). +# For the 48 kHz model, only 3, 6, 12, and 24 kbps are supported. The number +# of codebooks for each is half that of the 24 kHz model as the frame rate is twice as much. +model = model.cuda() +model.set_target_bandwidth(12.0) + +# Load and pre-process the audio waveform +# wav, sr = torchaudio.load("") +input_path = "/home/jovyan/honor/big-disk/speech/code/languagecodec/data/infer/lirbitts_vctk_testclean_500" +out_folder = '/home/jovyan/honor/big-disk/speech/code/languagecodec/result/infer/encodec' +# os.system("rm -r %s"%(out_folder)) +# os.system("mkdir -p %s"%(out_folder)) +# ll="libritts_testclean500_nq16" +ll = "debug" + +tmptmp=out_folder+"/"+ll + +os.system("rm -r %s"%(tmptmp)) +os.system("mkdir -p %s"%(tmptmp)) + +with open(input_path,'r') as fin: + x=fin.readlines() + +x = [i.strip() for i in x] + +for i in range(20): + + print(i) + + wav, sr = torchaudio.load(x[i]) + + wav = wav.cuda() + wav = convert_audio(wav, sr, model.sample_rate, model.channels) + wav = wav.unsqueeze(0) + + # Extract discrete codes from EnCodec + with torch.no_grad(): + encoded_frames = model.encode(wav) + codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1) # [B, n_q, T] + + if(codes.size()[2]>100): + logging.info(f"{x[i]}|{codes[:,:2,:]}") + else: + logging.info(f"{x[i]}|{codes}") + + # frames=[(codes,None)] + + # with torch.no_grad(): + # wav_output = model.decode(frames) + + # audio_path = out_folder + '/' + ll + '/' + x[i].split('/')[-1] + + # torchaudio.save(audio_path,wav_output.squeeze(0).cpu(),model.sample_rate) diff --git a/code/datasplit.py b/code/datasplit.py new file mode 100644 index 0000000..fc74ef5 --- /dev/null +++ b/code/datasplit.py @@ -0,0 +1,45 @@ +# 将librilight的数据切分成八秒中一段 + +import os +import glob +import torchaudio +import torch + +input_path="/home/jovyan/honor/big-disk/speech/Data/librilight/small/small" +output_path="/home/jovyan/honor/big-disk/speech/Data/librilight/small/small_split" + +aa=glob.glob(os.path.join(input_path,"*/*/*.flac")) + +os.system("rm -r %s"%(output_path)) +os.system("mkdir -p %s"%(output_path)) + +sum_audio=0 + +for i in aa: + # sum_audio+=1 + # if(sum_audio>10): + # break + wav, sr = torchaudio.load(i) + if(wav.size()[1]>(sr*8)): + wav_tmp=wav[:,:wav.size()[1]//(sr*8)*(sr*8)].reshape(-1,sr*8) + wav_left=wav[:,wav.size()[1]//(sr*8)*(sr*8):] + name=i.split('/')[-3] + id=i.split('/')[-1].split('.')[0] + for id_i in range(wav_tmp.size()[0]): + out_folder=output_path+"/"+name + os.makedirs(out_folder, exist_ok=True) + audio_path=out_folder+"/"+id+"_"+str(id_i+1)+".flac" + torchaudio.save(audio_path, wav_tmp[id_i].unsqueeze(0), sample_rate=sr) + if(wav_left.size()[1]>sr): + audio_path=out_folder+"/"+id+"_"+str(id_i+1+1)+".flac" + torchaudio.save(audio_path, wav_left, sample_rate=sr) + else: + if(wav.size()[1]>sr): + name=i.split('/')[-3] + id=i.split('/')[-1].split('.')[0] + out_folder=output_path+"/"+name + os.makedirs(out_folder, exist_ok=True) + audio_path=out_folder+"/"+id+"_1.flac" + torchaudio.save(audio_path, wav, sample_rate=sr) + + diff --git a/code/datasplit2.py b/code/datasplit2.py new file mode 100644 index 0000000..5487912 --- /dev/null +++ b/code/datasplit2.py @@ -0,0 +1,45 @@ +# 将librilight的数据切分成八秒中一段 + +import os +import glob +import torchaudio +import torch + +input_path="/home/jovyan/honor/big-disk/speech/Data/DNS/pdns_training_set/raw/clean" +output_path="/home/jovyan/honor/big-disk/speech/Data/DNS/trainclean_split" + +aa=glob.glob(os.path.join(input_path,"*/*.wav")) + +# os.system("rm -r %s"%(output_path)) +# os.system("mkdir -p %s"%(output_path)) + +sum_audio=0 + +for i in aa: + # sum_audio+=1 + # if(sum_audio>10): + # break + wav, sr = torchaudio.load(i) + if(wav.size()[1]>(sr*8)): + wav_tmp=wav[:,:wav.size()[1]//(sr*8)*(sr*8)].reshape(-1,sr*8) + wav_left=wav[:,wav.size()[1]//(sr*8)*(sr*8):] + name=i.split('/')[-2] + id=i.split('/')[-1].split('.')[0] + for id_i in range(wav_tmp.size()[0]): + out_folder=output_path+"/"+name + os.makedirs(out_folder, exist_ok=True) + audio_path=out_folder+"/"+id+"_"+str(id_i+1)+".wav" + torchaudio.save(audio_path, wav_tmp[id_i].unsqueeze(0), sample_rate=sr) + if(wav_left.size()[1]>sr): + audio_path=out_folder+"/"+id+"_"+str(id_i+1+1)+".wav" + torchaudio.save(audio_path, wav_left, sample_rate=sr) + else: + if(wav.size()[1]>sr): + name=i.split('/')[-2] + id=i.split('/')[-1].split('.')[0] + out_folder=output_path+"/"+name + os.makedirs(out_folder, exist_ok=True) + audio_path=out_folder+"/"+id+"_1.wav" + torchaudio.save(audio_path, wav, sample_rate=sr) + + diff --git a/code/getdata.py b/code/getdata.py new file mode 100644 index 0000000..617958b --- /dev/null +++ b/code/getdata.py @@ -0,0 +1,12 @@ +# 下载common voice + +from datasets import load_dataset + + +print(0) + +# ['ab', 'af', 'am', 'ar', 'as', 'ast', 'az', 'ba', 'bas', 'be', 'bg', 'bn', 'br', 'ca', 'ckb', 'cnh', 'cs', 'cv', 'cy', 'da', 'de', 'dv', 'dyu', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy-NL', 'ga-IE', 'gl', 'gn', 'ha', 'he', 'hi', 'hsb', 'hu', 'hy-AM', 'ia', 'id', 'ig', 'is', 'it', 'ja', 'ka', 'kab', 'kk', 'kmr', 'ko', 'ky', 'lg', 'lij', 'lo', 'lt', 'ltg', 'lv', 'mdf', 'mhr', 'mk', 'ml', 'mn', 'mr', 'mrj', 'mt', 'myv', 'nan-tw', 'ne-NP', 'nhi', 'nl', 'nn-NO', 'oc', 'or', 'os', 'pa-IN', 'pl', 'ps', 'pt', 'quy', 'rm-sursilv', 'rm-vallader', 'ro', 'ru', 'rw', 'sah', 'sat', 'sc', 'sk', 'skr', 'sl', 'sq', 'sr', 'sv-SE', 'sw', 'ta', 'te', 'th', 'ti', 'tig', 'tk', 'tok', 'tr', 'tt', 'tw', 'ug', 'uk', 'ur', 'uz', 'vi', 'vot', 'yi', 'yo', 'yue', 'zgh', 'zh-CN', 'zh-HK', 'zh-TW'] + +ds=load_dataset("mozilla-foundation/common_voice_16_1","hu") + +# https://huggingface.co/datasets/reach-vb/common_voice_16_1/resolve/main/audio/af/train/af_train_0.tar \ No newline at end of file diff --git a/code/predata.py b/code/predata.py new file mode 100644 index 0000000..cc5f974 --- /dev/null +++ b/code/predata.py @@ -0,0 +1,144 @@ +# 准备好训练的list +import os +import glob +import random + + +# # 训练集 +# inputpath1="/home/jovyan/honor/big-disk/speech/Data/libritts/train-clean-100" +# inputpath2="/home/jovyan/honor/big-disk/speech/Data/libritts/train-clean-360" +# inputpath3="/home/jovyan/honor/big-disk/speech/Data/libritts/train-other-500" +# inputpath4="/home/jovyan/honor/big-disk/speech/Data/vctk/wav48" + +# outputpath="/home/jovyan/honor/big-disk/speech/code/languagecodec/data/train/vctk" + +# os.system("rm %s"%(outputpath)) +# os.system("touch %s"%(outputpath)) + +# aa1=glob.glob(os.path.join(inputpath1,"*/*/*.wav")) +# aa2=glob.glob(os.path.join(inputpath2,"*/*/*.wav")) +# aa3=glob.glob(os.path.join(inputpath3,"*/*/*.wav")) +# aa4=glob.glob(os.path.join(inputpath4,"*/*.wav")) + +# aa=aa1+aa2+aa3+aa4 + +# aa=aa4 + +# random.shuffle(aa) + +# # print(len(aa),aa[:10]) + +# with open(outputpath,'w') as fin: +# for i in aa: +# fin.write(i+"\n") + + +# # 验证集 +# inputpath1="/home/jovyan/honor/big-disk/speech/Data/LibriSpeech/test-clean" +# inputpath2="/home/jovyan/honor/big-disk/speech/Data/LibriSpeech/test-other" + +# outputpath="/home/jovyan/honor/big-disk/speech/code/languagecodec/data/train/lirbitts_vctk_val" + +# os.system("rm %s"%(outputpath)) +# os.system("touch %s"%(outputpath)) + +# aa1=glob.glob(os.path.join(inputpath1,"*/*/*.flac")) +# aa2=glob.glob(os.path.join(inputpath2,"*/*/*.flac")) + + +# aa=aa1+aa2 + +# random.shuffle(aa) + +# # print(len(aa),aa[:10]) + +# with open(outputpath,'w') as fin: +# for i in aa: +# fin.write(i+"\n") + +# 测试集 +# inputpath1="/home/jovyan/honor/big-disk/speech/Data/libritts/test-clean" + +# outputpath="/home/jovyan/honor/big-disk/speech/code/languagecodec/data/train/lirbitts_vctk_test" + +# os.system("rm %s"%(outputpath)) +# os.system("touch %s"%(outputpath)) + +# aa1=glob.glob(os.path.join(inputpath1,"*/*/*.wav")) + +# aa=aa1 + +# random.shuffle(aa) + +# # print(len(aa),aa[:10]) + +# with open(outputpath,'w') as fin: +# for i in aa: +# fin.write(i+"\n") + + +# # 测试集 +# inputpath1="/home/jovyan/honor/big-disk/speech/Data/LJSpeech-1.1/wavs" + +# outputpath="/home/jovyan/honor/big-disk/speech/code/languagecodec/data/train/ljspeech" + +# os.system("rm %s"%(outputpath)) +# os.system("touch %s"%(outputpath)) + +# aa1=glob.glob(os.path.join(inputpath1,"*.wav")) + +# aa=aa1 + +# random.shuffle(aa) + +# # print(len(aa),aa[:10]) + +# with open(outputpath,'w') as fin: +# for i in aa: +# fin.write(i+"\n") + + +# 搞一份所有的作为language_codec训练的 +inputpath1="/home/jovyan/honor/big-disk/speech/Data/libritts/train-clean-100" +inputpath2="/home/jovyan/honor/big-disk/speech/Data/libritts/train-clean-360" +inputpath3="/home/jovyan/honor/big-disk/speech/Data/libritts/train-other-500" +inputpath4="/home/jovyan/honor/big-disk/speech/Data/vctk/wav48" +inputpath5="/home/jovyan/honor/big-disk/speech/Data/librilight/small/small_split" +inputpath6="/home/jovyan/honor/big-disk/speech/Data/librilight/medium/medium_split" +inputpath7="/home/jovyan/honor/big-disk/speech/Data/commonvoice/cv-corpus-16.1-2023-12-06" +inputpath8="/home/jovyan/honor/big-disk/speech/Data/commonvoice/cv-corpus-15.0-2023-09-08" +inputpath9="/home/jovyan/honor/big-disk/speech/Data/DNS/trainclean_split" + +outputpath="/home/jovyan/honor/big-disk/speech/code/languagecodec/data/train/languagecodec_large" + +os.system("rm %s"%(outputpath)) +os.system("touch %s"%(outputpath)) + +aa1=glob.glob(os.path.join(inputpath1,"*/*/*.wav")) +aa2=glob.glob(os.path.join(inputpath2,"*/*/*.wav")) +aa3=glob.glob(os.path.join(inputpath3,"*/*/*.wav")) +aa4=glob.glob(os.path.join(inputpath4,"*/*.wav")) + +aa5=glob.glob(os.path.join(inputpath5,"*/*.flac")) +aa6=glob.glob(os.path.join(inputpath6,"*/*.flac")) +aa7=glob.glob(os.path.join(inputpath7,"*/clips/*.mp3")) +aa8=glob.glob(os.path.join(inputpath8,"*/clips/*.mp3")) +aa9=glob.glob(os.path.join(inputpath9,"*/*.wav")) + +aa=aa1+aa2+aa3+aa4+aa5+aa6+aa7+aa8+aa9 + +random.shuffle(aa) + +# print(len(aa),aa[:10]) + +with open(outputpath,'w') as fin: + for i in aa: + fin.write(i+"\n") + + + + + + + + diff --git a/code/random_demo.py b/code/random_demo.py new file mode 100644 index 0000000..cd1490e --- /dev/null +++ b/code/random_demo.py @@ -0,0 +1,23 @@ +# 随机选择一些作为demo展示 + +import os + +input_path="/home/jovyan/honor/big-disk/speech/code/languagecodec/result/infer/languagecodec/libritts_testclean_nq4_large_epoch24" +input_path2="/home/jovyan/honor/big-disk/speech/code/languagecodec/result/infer/encodec/libritts_testclean500_nq8" +output_path="/home/jovyan/honor/big-disk/speech/code/languagecodec/result/infer/demo" + +os.system("rm -r %s"%(output_path)) +os.system("mkdir -p %s"%(output_path)) +x=os.listdir(input_path2) + +# print(x[0]) + +for i in range(400): + out1=output_path+"/"+"encodec_"+x[i] + out2=output_path+"/"+"languagecodec_"+x[i] + int2=os.path.join(input_path,x[i]) + int1=os.path.join(input_path2,x[i]) + os.system("cp %s %s"%(int1,out1)) + os.system("cp %s %s"%(int2,out2)) + + diff --git a/code/test.py b/code/test.py new file mode 100644 index 0000000..a9967d4 --- /dev/null +++ b/code/test.py @@ -0,0 +1,36 @@ + +import torch +import torchaudio +import glob +import os +import soundfile + +# path="/home/jovyan/honor/big-disk/speech/Data/commonvoice/cv-corpus-15.0-2023-09-08" + +# aa=glob.glob(os.path.join(path,"*/clips/*.mp3")) + +# for i in aa: +# try: +# y, sr = torchaudio.load(i) +# except: +# print(i) +# for i in range(100): +# print(int(torch.randint(0, 3, (1,)).item())) + +# audio_path="/home/jovyan/honor/big-disk/speech/Data/libritts/test-clean/61/70970/61_70970_000007_000001.wav" +# y1, sr1 = soundfile.read(audio_path) +# y2,sr2=torchaudio.load(audio_path) +# y3=torch.tensor(y1).unsqueeze(0) + + +# print(sr1,sr2) + +# print(y1.shape[0],y2.ndim,y3.size()) + +# for i in range(y3.size()[1]): +# assert y3[0][i]==y2[0][i] + +tensor3d=torch.tensor([[[1.0,2.0,3.0],[4.0,5.0,6.0]],[[1.0,3.0,5.0],[2.0,4.0,6.0]],[[7.0,8.0,9.0],[8.0,5.0,4.0]]]) + +print(tensor3d.size(),tensor3d.mean(dim=1,keepdim=False).size()) + diff --git a/configs/languagecodec.yaml b/configs/languagecodec.yaml new file mode 100644 index 0000000..0f4d597 --- /dev/null +++ b/configs/languagecodec.yaml @@ -0,0 +1,89 @@ +seed_everything: 4444 + +data: + class_path: vocos.dataset.VocosDataModule + init_args: + train_params: + filelist_path: xxx/xxx + sampling_rate: 24000 + num_samples: 24000 + batch_size: 100 + num_workers: 8 + + val_params: + filelist_path: xxx/xxx + sampling_rate: 24000 + num_samples: 24000 + batch_size: 10 + num_workers: 8 + +model: + class_path: vocos.experiment.VocosEncodecExp + init_args: + sample_rate: 24000 + initial_learning_rate: 2e-4 + mel_loss_coeff: 45 + mrd_loss_coeff: 1.0 + num_warmup_steps: 0 # Optimizers warmup steps + pretrain_mel_steps: 0 # 0 means GAN objective from the first iteration + + # automatic evaluation + evaluate_utmos: true + evaluate_pesq: true + evaluate_periodicty: true + + resume: false + resume_config: xxx/config.yaml + resume_model: xxx/xxxx.ckpt + + feature_extractor: + class_path: vocos.feature_extractors.EncodecFeatures + init_args: + encodec_model: encodec_24khz + bandwidths: [6.6, 6.6, 6.6, 6.6] + train_codebooks: true + + backbone: + class_path: vocos.models.VocosBackbone + init_args: + input_channels: 128 + dim: 384 + intermediate_dim: 1152 + num_layers: 8 + adanorm_num_embeddings: 4 # len(bandwidths) + + head: + class_path: vocos.heads.ISTFTHead + init_args: + dim: 384 + n_fft: 1280 + hop_length: 320 + padding: same + +trainer: + logger: + class_path: pytorch_lightning.loggers.TensorBoardLogger + init_args: + save_dir: /home/jovyan/honor/big-disk/speech/code/languagecodec/result/ + callbacks: + - class_path: pytorch_lightning.callbacks.LearningRateMonitor + - class_path: pytorch_lightning.callbacks.ModelSummary + init_args: + max_depth: 2 + - class_path: pytorch_lightning.callbacks.ModelCheckpoint + init_args: + monitor: val_loss + filename: vocos_checkpoint_{epoch}_{step}_{val_loss:.4f} + save_top_k: 50 + save_last: true + - class_path: vocos.helpers.GradNormCallback + + # Lightning calculates max_steps across all optimizer steps (rather than number of batches) + # This equals to 1M steps per generator and 1M per discriminator + max_steps: 20000000 + # You might want to limit val batches when evaluating all the metrics, as they are time-consuming + limit_val_batches: 100 + accelerator: gpu + strategy: ddp + devices: [4,5,6,7] + log_every_n_steps: 1000 diff --git a/data/libritts_testother.txt b/data/libritts_testother.txt new file mode 100644 index 0000000..9e59ace --- /dev/null +++ b/data/libritts_testother.txt @@ -0,0 +1,5120 @@ +/home/jovyan/honor/big-disk/jishengpeng/Data/libritts/test-other/3005/163390/3005_163390_000008_000000.wav 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reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# flake8: noqa + +"""EnCodec neural audio codec.""" + +__version__ = "0.1.2a3" + +from .model import EncodecModel diff --git a/encodec/__pycache__/__init__.cpython-310.pyc b/encodec/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7980e1e5d9b256ba9e073971da10e412779dfc90 GIT binary patch literal 280 zcmYk0zfQw25XNmMAqc3%h{TdL12L2pAtcm+p)71!PHuck4US#Kq*D7v47>-=kd=v7 zVB#E>o^*e{`=tAwpUqAf#aH%;bI#az6o(;5JWz%UDw@?y^O|d+6Dzu;PB_!)3oEnz z<-$FBjZNWj=#?$hpp7q@xT^N~b#+tC)$I=vSjoq;g`1ou+4i|i#~eWW24Fk|*kT`y zcObE=PXm-ArE7M+S6W(OlAU10m?@Oy(JE#^Xn8bgm c1TfCL15}&cczj2U!Qy@VEJQ?_r{Y}v0c!C}X#fBK literal 0 HcmV?d00001 diff --git a/encodec/__pycache__/distrib.cpython-310.pyc b/encodec/__pycache__/distrib.cpython-310.pyc new file mode 100644 index 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directory of this source tree. + +"""Torch distributed utilities.""" + +import typing as tp + +import torch + + +def rank(): + if torch.distributed.is_initialized(): + return torch.distributed.get_rank() + else: + return 0 + + +def world_size(): + if torch.distributed.is_initialized(): + return torch.distributed.get_world_size() + else: + return 1 + + +def is_distributed(): + return world_size() > 1 + + +def all_reduce(tensor: torch.Tensor, op=torch.distributed.ReduceOp.SUM): + if is_distributed(): + return torch.distributed.all_reduce(tensor, op) + + +def _is_complex_or_float(tensor): + return torch.is_floating_point(tensor) or torch.is_complex(tensor) + + +def _check_number_of_params(params: tp.List[torch.Tensor]): + # utility function to check that the number of params in all workers is the same, + # and thus avoid a deadlock with distributed all reduce. + if not is_distributed() or not params: + return + tensor = torch.tensor([len(params)], device=params[0].device, dtype=torch.long) + all_reduce(tensor) + if tensor.item() != len(params) * world_size(): + # If not all the workers have the same number, for at least one of them, + # this inequality will be verified. + raise RuntimeError(f"Mismatch in number of params: ours is {len(params)}, " + "at least one worker has a different one.") + + +def broadcast_tensors(tensors: tp.Iterable[torch.Tensor], src: int = 0): + """Broadcast the tensors from the given parameters to all workers. + This can be used to ensure that all workers have the same model to start with. + """ + if not is_distributed(): + return + tensors = [tensor for tensor in tensors if _is_complex_or_float(tensor)] + _check_number_of_params(tensors) + handles = [] + for tensor in tensors: + handle = torch.distributed.broadcast(tensor.data, src=src, async_op=True) + handles.append(handle) + for handle in handles: + handle.wait() + + +def sync_buffer(buffers, average=True): + """ + Sync grad for buffers. If average is False, broadcast instead of averaging. + """ + if not is_distributed(): + return + handles = [] + for buffer in buffers: + if torch.is_floating_point(buffer.data): + if average: + handle = torch.distributed.all_reduce( + buffer.data, op=torch.distributed.ReduceOp.SUM, async_op=True) + else: + handle = torch.distributed.broadcast( + buffer.data, src=0, async_op=True) + handles.append((buffer, handle)) + for buffer, handle in handles: + handle.wait() + if average: + buffer.data /= world_size + + +def sync_grad(params): + """ + Simpler alternative to DistributedDataParallel, that doesn't rely + on any black magic. For simple models it can also be as fast. + Just call this on your model parameters after the call to backward! + """ + if not is_distributed(): + return + handles = [] + for p in params: + if p.grad is not None: + handle = torch.distributed.all_reduce( + p.grad.data, op=torch.distributed.ReduceOp.SUM, async_op=True) + handles.append((p, handle)) + for p, handle in handles: + handle.wait() + p.grad.data /= world_size() + + +def average_metrics(metrics: tp.Dict[str, float], count=1.): + """Average a dictionary of metrics across all workers, using the optional + `count` as unnormalized weight. + """ + if not is_distributed(): + return metrics + keys, values = zip(*metrics.items()) + device = 'cuda' if torch.cuda.is_available() else 'cpu' + tensor = torch.tensor(list(values) + [1], device=device, dtype=torch.float32) + tensor *= count + all_reduce(tensor) + averaged = (tensor[:-1] / tensor[-1]).cpu().tolist() + return dict(zip(keys, averaged)) diff --git a/encodec/model.py b/encodec/model.py new file mode 100644 index 0000000..8914e79 --- /dev/null +++ b/encodec/model.py @@ -0,0 +1,324 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""EnCodec model implementation.""" + +import math +from pathlib import Path +import typing as tp + +import numpy as np +import torch +from torch import nn + +from . import quantization as qt +from . import modules as m +from .utils import _check_checksum, _linear_overlap_add, _get_checkpoint_url + + +ROOT_URL = 'https://dl.fbaipublicfiles.com/encodec/v0/' + +EncodedFrame = tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]] + + +class LMModel(nn.Module): + """Language Model to estimate probabilities of each codebook entry. + We predict all codebooks in parallel for a given time step. + + Args: + n_q (int): number of codebooks. + card (int): codebook cardinality. + dim (int): transformer dimension. + **kwargs: passed to `encodec.modules.transformer.StreamingTransformerEncoder`. + """ + def __init__(self, n_q: int = 32, card: int = 1024, dim: int = 200, **kwargs): + super().__init__() + self.card = card + self.n_q = n_q + self.dim = dim + self.transformer = m.StreamingTransformerEncoder(dim=dim, **kwargs) + self.emb = nn.ModuleList([nn.Embedding(card + 1, dim) for _ in range(n_q)]) + self.linears = nn.ModuleList([nn.Linear(dim, card) for _ in range(n_q)]) + + def forward(self, indices: torch.Tensor, + states: tp.Optional[tp.List[torch.Tensor]] = None, offset: int = 0): + """ + Args: + indices (torch.Tensor): indices from the previous time step. Indices + should be 1 + actual index in the codebook. The value 0 is reserved for + when the index is missing (i.e. first time step). Shape should be + `[B, n_q, T]`. + states: state for the streaming decoding. + offset: offset of the current time step. + + Returns a 3-tuple `(probabilities, new_states, new_offset)` with probabilities + with a shape `[B, card, n_q, T]`. + + """ + B, K, T = indices.shape + input_ = sum([self.emb[k](indices[:, k]) for k in range(K)]) + out, states, offset = self.transformer(input_, states, offset) + logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1).permute(0, 3, 1, 2) + return torch.softmax(logits, dim=1), states, offset + + +class EncodecModel(nn.Module): + """EnCodec model operating on the raw waveform. + Args: + target_bandwidths (list of float): Target bandwidths. + encoder (nn.Module): Encoder network. + decoder (nn.Module): Decoder network. + sample_rate (int): Audio sample rate. + channels (int): Number of audio channels. + normalize (bool): Whether to apply audio normalization. + segment (float or None): segment duration in sec. when doing overlap-add. + overlap (float): overlap between segment, given as a fraction of the segment duration. + name (str): name of the model, used as metadata when compressing audio. + """ + def __init__(self, + encoder: m.SEANetEncoder, + decoder: m.SEANetDecoder, + quantizer: qt.ResidualVectorQuantizer, + target_bandwidths: tp.List[float], + sample_rate: int, + channels: int, + normalize: bool = False, + segment: tp.Optional[float] = None, + overlap: float = 0.01, + name: str = 'unset'): + super().__init__() + self.bandwidth: tp.Optional[float] = None + self.target_bandwidths = target_bandwidths + self.encoder = encoder + self.quantizer = quantizer + self.decoder = decoder + self.sample_rate = sample_rate + self.channels = channels + self.normalize = normalize + self.segment = segment + self.overlap = overlap + self.frame_rate = math.ceil(self.sample_rate / np.prod(self.encoder.ratios)) + self.name = name + self.bits_per_codebook = int(math.log2(self.quantizer.bins)) + assert 2 ** self.bits_per_codebook == self.quantizer.bins, \ + "quantizer bins must be a power of 2." + + @property + def segment_length(self) -> tp.Optional[int]: + if self.segment is None: + return None + return int(self.segment * self.sample_rate) + + @property + def segment_stride(self) -> tp.Optional[int]: + segment_length = self.segment_length + if segment_length is None: + return None + return max(1, int((1 - self.overlap) * segment_length)) + + def encode(self, x: torch.Tensor) -> tp.List[EncodedFrame]: + """Given a tensor `x`, returns a list of frames containing + the discrete encoded codes for `x`, along with rescaling factors + for each segment, when `self.normalize` is True. + + Each frames is a tuple `(codebook, scale)`, with `codebook` of + shape `[B, K, T]`, with `K` the number of codebooks. + """ + assert x.dim() == 3 + _, channels, length = x.shape + assert channels > 0 and channels <= 2 + segment_length = self.segment_length + if segment_length is None: + segment_length = length + stride = length + else: + stride = self.segment_stride # type: ignore + assert stride is not None + + encoded_frames: tp.List[EncodedFrame] = [] + for offset in range(0, length, stride): + frame = x[:, :, offset: offset + segment_length] + encoded_frames.append(self._encode_frame(frame)) + return encoded_frames + + def _encode_frame(self, x: torch.Tensor) -> EncodedFrame: + length = x.shape[-1] + duration = length / self.sample_rate + assert self.segment is None or duration <= 1e-5 + self.segment + + if self.normalize: + mono = x.mean(dim=1, keepdim=True) + volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt() + scale = 1e-8 + volume + x = x / scale + scale = scale.view(-1, 1) + else: + scale = None + + emb = self.encoder(x) + codes = self.quantizer.encode(emb, self.frame_rate, self.bandwidth) + codes = codes.transpose(0, 1) + # codes is [B, K, T], with T frames, K nb of codebooks. + return codes, scale + + def decode(self, encoded_frames: tp.List[EncodedFrame]) -> torch.Tensor: + """Decode the given frames into a waveform. + Note that the output might be a bit bigger than the input. In that case, + any extra steps at the end can be trimmed. + """ + segment_length = self.segment_length + if segment_length is None: + assert len(encoded_frames) == 1 + return self._decode_frame(encoded_frames[0]) + + frames = [self._decode_frame(frame) for frame in encoded_frames] + return _linear_overlap_add(frames, self.segment_stride or 1) + + def _decode_frame(self, encoded_frame: EncodedFrame) -> torch.Tensor: + codes, scale = encoded_frame + codes = codes.transpose(0, 1) + emb = self.quantizer.decode(codes) + out = self.decoder(emb) + if scale is not None: + out = out * scale.view(-1, 1, 1) + return out + + def forward(self, x: torch.Tensor) -> torch.Tensor: + frames = self.encode(x) + return self.decode(frames)[:, :, :x.shape[-1]] + + def set_target_bandwidth(self, bandwidth: float): + if bandwidth not in self.target_bandwidths: + raise ValueError(f"This model doesn't support the bandwidth {bandwidth}. " + f"Select one of {self.target_bandwidths}.") + self.bandwidth = bandwidth + + def get_lm_model(self) -> LMModel: + """Return the associated LM model to improve the compression rate. + """ + device = next(self.parameters()).device + lm = LMModel(self.quantizer.n_q, self.quantizer.bins, num_layers=5, dim=200, + past_context=int(3.5 * self.frame_rate)).to(device) + checkpoints = { + 'encodec_24khz': 'encodec_lm_24khz-1608e3c0.th', + 'encodec_48khz': 'encodec_lm_48khz-7add9fc3.th', + } + try: + checkpoint_name = checkpoints[self.name] + except KeyError: + raise RuntimeError("No LM pre-trained for the current Encodec model.") + url = _get_checkpoint_url(ROOT_URL, checkpoint_name) + state = torch.hub.load_state_dict_from_url( + url, map_location='cpu', check_hash=True) # type: ignore + lm.load_state_dict(state) + lm.eval() + return lm + + @staticmethod + def _get_model(target_bandwidths: tp.List[float], + sample_rate: int = 24_000, + channels: int = 1, + causal: bool = True, + model_norm: str = 'weight_norm', + audio_normalize: bool = False, + segment: tp.Optional[float] = None, + name: str = 'unset'): + encoder = m.SEANetEncoder(channels=channels, norm=model_norm, causal=causal) + decoder = m.SEANetDecoder(channels=channels, norm=model_norm, causal=causal) + n_q = int(1000 * target_bandwidths[-1] // (math.ceil(sample_rate / encoder.hop_length) * 10)) + quantizer = qt.ResidualVectorQuantizer( + dimension=encoder.dimension, + n_q=n_q, + bins=1024, + ) + model = EncodecModel( + encoder, + decoder, + quantizer, + target_bandwidths, + sample_rate, + channels, + normalize=audio_normalize, + segment=segment, + name=name, + ) + return model + + @staticmethod + def _get_pretrained(checkpoint_name: str, repository: tp.Optional[Path] = None): + if repository is not None: + if not repository.is_dir(): + raise ValueError(f"{repository} must exist and be a directory.") + file = repository / checkpoint_name + checksum = file.stem.split('-')[1] + _check_checksum(file, checksum) + return torch.load(file) + else: + url = _get_checkpoint_url(ROOT_URL, checkpoint_name) + return torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True) # type:ignore + + @staticmethod + def encodec_model_24khz(pretrained: bool = True, repository: tp.Optional[Path] = None): + """Return the pretrained causal 24khz model. + """ + if repository: + assert pretrained + target_bandwidths = [1.5, 3., 6, 12., 24.] + checkpoint_name = 'encodec_24khz-d7cc33bc.th' + sample_rate = 24_000 + channels = 1 + model = EncodecModel._get_model( + target_bandwidths, sample_rate, channels, + causal=True, model_norm='weight_norm', audio_normalize=False, + name='encodec_24khz' if pretrained else 'unset') + if pretrained: + state_dict = EncodecModel._get_pretrained(checkpoint_name, repository) + model.load_state_dict(state_dict) + model.eval() + return model + + @staticmethod + def encodec_model_48khz(pretrained: bool = True, repository: tp.Optional[Path] = None): + """Return the pretrained 48khz model. + """ + if repository: + assert pretrained + target_bandwidths = [3., 6., 12., 24.] + checkpoint_name = 'encodec_48khz-7e698e3e.th' + sample_rate = 48_000 + channels = 2 + model = EncodecModel._get_model( + target_bandwidths, sample_rate, channels, + causal=False, model_norm='time_group_norm', audio_normalize=True, + segment=1., name='encodec_48khz' if pretrained else 'unset') + if pretrained: + state_dict = EncodecModel._get_pretrained(checkpoint_name, repository) + model.load_state_dict(state_dict) + model.eval() + return model + + +def test(): + from itertools import product + import torchaudio + bandwidths = [3, 6, 12, 24] + models = { + 'encodec_24khz': EncodecModel.encodec_model_24khz, + 'encodec_48khz': EncodecModel.encodec_model_48khz + } + for model_name, bw in product(models.keys(), bandwidths): + model = models[model_name]() + model.set_target_bandwidth(bw) + audio_suffix = model_name.split('_')[1][:3] + wav, sr = torchaudio.load(f"test_{audio_suffix}.wav") + wav = wav[:, :model.sample_rate * 2] + wav_in = wav.unsqueeze(0) + wav_dec = model(wav_in)[0] + assert wav.shape == wav_dec.shape, (wav.shape, wav_dec.shape) + + +if __name__ == '__main__': + test() diff --git a/encodec/modules/__init__.py b/encodec/modules/__init__.py new file mode 100644 index 0000000..b8e2f98 --- /dev/null +++ b/encodec/modules/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Torch modules.""" + +# flake8: noqa +from .conv import ( + pad1d, + unpad1d, + NormConv1d, + NormConvTranspose1d, + NormConv2d, + NormConvTranspose2d, + SConv1d, + SConvTranspose1d, +) +from .lstm import SLSTM +from .seanet import SEANetEncoder, SEANetDecoder +from .transformer import StreamingTransformerEncoder diff --git a/encodec/modules/__pycache__/__init__.cpython-310.pyc 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If causal is True, this will ensure the returned + module is causal, or return an error if the normalization doesn't support causal evaluation. + """ + assert norm in CONV_NORMALIZATIONS + if norm == 'layer_norm': + assert isinstance(module, nn.modules.conv._ConvNd) + return ConvLayerNorm(module.out_channels, **norm_kwargs) + elif norm == 'time_group_norm': + if causal: + raise ValueError("GroupNorm doesn't support causal evaluation.") + assert isinstance(module, nn.modules.conv._ConvNd) + return nn.GroupNorm(1, module.out_channels, **norm_kwargs) + else: + return nn.Identity() + + +def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, + padding_total: int = 0) -> int: + """See `pad_for_conv1d`. + """ + length = x.shape[-1] + n_frames = (length - kernel_size + padding_total) / stride + 1 + ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total) + return ideal_length - length + + +def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0): + """Pad for a convolution to make sure that the last window is full. + Extra padding is added at the end. This is required to ensure that we can rebuild + an output of the same length, as otherwise, even with padding, some time steps + might get removed. + For instance, with total padding = 4, kernel size = 4, stride = 2: + 0 0 1 2 3 4 5 0 0 # (0s are padding) + 1 2 3 # (output frames of a convolution, last 0 is never used) + 0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding) + 1 2 3 4 # once you removed padding, we are missing one time step ! + """ + extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total) + return F.pad(x, (0, extra_padding)) + + +def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', value: float = 0.): + """Tiny wrapper around F.pad, just to allow for reflect padding on small input. + If this is the case, we insert extra 0 padding to the right before the reflection happen. + """ + length = x.shape[-1] + padding_left, padding_right = paddings + assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) + if mode == 'reflect': + max_pad = max(padding_left, padding_right) + extra_pad = 0 + if length <= max_pad: + extra_pad = max_pad - length + 1 + x = F.pad(x, (0, extra_pad)) + padded = F.pad(x, paddings, mode, value) + end = padded.shape[-1] - extra_pad + return padded[..., :end] + else: + return F.pad(x, paddings, mode, value) + + +def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]): + """Remove padding from x, handling properly zero padding. Only for 1d!""" + padding_left, padding_right = paddings + assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) + assert (padding_left + padding_right) <= x.shape[-1] + end = x.shape[-1] - padding_right + return x[..., padding_left: end] + + +class NormConv1d(nn.Module): + """Wrapper around Conv1d and normalization applied to this conv + to provide a uniform interface across normalization approaches. + """ + def __init__(self, *args, causal: bool = False, norm: str = 'none', + norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): + super().__init__() + self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm) + self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs) + self.norm_type = norm + + def forward(self, x): + x = self.conv(x) + x = self.norm(x) + return x + + +class NormConv2d(nn.Module): + """Wrapper around Conv2d and normalization applied to this conv + to provide a uniform interface across normalization approaches. + """ + def __init__(self, *args, norm: str = 'none', + norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): + super().__init__() + self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm) + self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs) + self.norm_type = norm + + def forward(self, x): + x = self.conv(x) + x = self.norm(x) + return x + + +class NormConvTranspose1d(nn.Module): + """Wrapper around ConvTranspose1d and normalization applied to this conv + to provide a uniform interface across normalization approaches. + """ + def __init__(self, *args, causal: bool = False, norm: str = 'none', + norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): + super().__init__() + self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm) + self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs) + self.norm_type = norm + + def forward(self, x): + x = self.convtr(x) + x = self.norm(x) + return x + + +class NormConvTranspose2d(nn.Module): + """Wrapper around ConvTranspose2d and normalization applied to this conv + to provide a uniform interface across normalization approaches. + """ + def __init__(self, *args, norm: str = 'none', + norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): + super().__init__() + self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm) + self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs) + + def forward(self, x): + x = self.convtr(x) + x = self.norm(x) + return x + + +class SConv1d(nn.Module): + """Conv1d with some builtin handling of asymmetric or causal padding + and normalization. + """ + def __init__(self, in_channels: int, out_channels: int, + kernel_size: int, stride: int = 1, dilation: int = 1, + groups: int = 1, bias: bool = True, causal: bool = False, + norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, + pad_mode: str = 'reflect'): + super().__init__() + # warn user on unusual setup between dilation and stride + if stride > 1 and dilation > 1: + warnings.warn('SConv1d has been initialized with stride > 1 and dilation > 1' + f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).') + self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride, + dilation=dilation, groups=groups, bias=bias, causal=causal, + norm=norm, norm_kwargs=norm_kwargs) + self.causal = causal + self.pad_mode = pad_mode + + def forward(self, x): + B, C, T = x.shape + kernel_size = self.conv.conv.kernel_size[0] + stride = self.conv.conv.stride[0] + dilation = self.conv.conv.dilation[0] + kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations + padding_total = kernel_size - stride + extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total) + if self.causal: + # Left padding for causal + x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode) + else: + # Asymmetric padding required for odd strides + padding_right = padding_total // 2 + padding_left = padding_total - padding_right + x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode) + return self.conv(x) + + +class SConvTranspose1d(nn.Module): + """ConvTranspose1d with some builtin handling of asymmetric or causal padding + and normalization. + """ + def __init__(self, in_channels: int, out_channels: int, + kernel_size: int, stride: int = 1, causal: bool = False, + norm: str = 'none', trim_right_ratio: float = 1., + norm_kwargs: tp.Dict[str, tp.Any] = {}): + super().__init__() + self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride, + causal=causal, norm=norm, norm_kwargs=norm_kwargs) + self.causal = causal + self.trim_right_ratio = trim_right_ratio + assert self.causal or self.trim_right_ratio == 1., \ + "`trim_right_ratio` != 1.0 only makes sense for causal convolutions" + assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1. + + def forward(self, x): + kernel_size = self.convtr.convtr.kernel_size[0] + stride = self.convtr.convtr.stride[0] + padding_total = kernel_size - stride + + y = self.convtr(x) + + # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be + # removed at the very end, when keeping only the right length for the output, + # as removing it here would require also passing the length at the matching layer + # in the encoder. + if self.causal: + # Trim the padding on the right according to the specified ratio + # if trim_right_ratio = 1.0, trim everything from right + padding_right = math.ceil(padding_total * self.trim_right_ratio) + padding_left = padding_total - padding_right + y = unpad1d(y, (padding_left, padding_right)) + else: + # Asymmetric padding required for odd strides + padding_right = padding_total // 2 + padding_left = padding_total - padding_right + y = unpad1d(y, (padding_left, padding_right)) + return y diff --git a/encodec/modules/lstm.py b/encodec/modules/lstm.py new file mode 100644 index 0000000..4990819 --- /dev/null +++ b/encodec/modules/lstm.py @@ -0,0 +1,39 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""LSTM layers module.""" + +from torch import nn + + +class SLSTM(nn.Module): + """ + LSTM without worrying about the hidden state, nor the layout of the data. + Expects input as convolutional layout. + """ + def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True): + super().__init__() + self.skip = skip + self.lstm = nn.LSTM(dimension, dimension, num_layers) + + # def forward(self, x): + # x = x.permute(2, 0, 1) + # y, _ = self.lstm(x) + # if self.skip: + # y = y + x + # y = y.permute(1, 2, 0) + # return y + + # 修改transpose顺序 + def forward(self, x): + # # 插入reshape + # x = x.reshape(x.shape) + x1 = x.permute(2, 0, 1) + y, _ = self.lstm(x1) + y = y.permute(1, 2, 0) + if self.skip: + y = y + x + return y diff --git a/encodec/modules/norm.py b/encodec/modules/norm.py new file mode 100644 index 0000000..19970e0 --- /dev/null +++ b/encodec/modules/norm.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Normalization modules.""" + +import typing as tp + +import einops +import torch +from torch import nn + + +class ConvLayerNorm(nn.LayerNorm): + """ + Convolution-friendly LayerNorm that moves channels to last dimensions + before running the normalization and moves them back to original position right after. + """ + def __init__(self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs): + super().__init__(normalized_shape, **kwargs) + + def forward(self, x): + x = einops.rearrange(x, 'b ... t -> b t ...') + x = super().forward(x) + x = einops.rearrange(x, 'b t ... -> b ... t') + return diff --git a/encodec/modules/seanet.py b/encodec/modules/seanet.py new file mode 100644 index 0000000..ea1c02d --- /dev/null +++ b/encodec/modules/seanet.py @@ -0,0 +1,253 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Encodec SEANet-based encoder and decoder implementation.""" + +import typing as tp + +import numpy as np +import torch.nn as nn + +from . import ( + SConv1d, + SConvTranspose1d, + SLSTM +) + + +class SEANetResnetBlock(nn.Module): + """Residual block from SEANet model. + Args: + dim (int): Dimension of the input/output + kernel_sizes (list): List of kernel sizes for the convolutions. + dilations (list): List of dilations for the convolutions. + activation (str): Activation function. + activation_params (dict): Parameters to provide to the activation function + norm (str): Normalization method. + norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. + causal (bool): Whether to use fully causal convolution. + pad_mode (str): Padding mode for the convolutions. + compress (int): Reduced dimensionality in residual branches (from Demucs v3) + true_skip (bool): Whether to use true skip connection or a simple convolution as the skip connection. + """ + def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1], + activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, + norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False, + pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True): + super().__init__() + assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations' + act = getattr(nn, activation) + hidden = dim // compress + block = [] + for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)): + in_chs = dim if i == 0 else hidden + out_chs = dim if i == len(kernel_sizes) - 1 else hidden + block += [ + act(**activation_params), + SConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation, + norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode), + ] + self.block = nn.Sequential(*block) + self.shortcut: nn.Module + if true_skip: + self.shortcut = nn.Identity() + else: + self.shortcut = SConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode) + + def forward(self, x): + return self.shortcut(x) + self.block(x) + + +class SEANetEncoder(nn.Module): + """SEANet encoder. + Args: + channels (int): Audio channels. + dimension (int): Intermediate representation dimension. + n_filters (int): Base width for the model. + n_residual_layers (int): nb of residual layers. + ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of + upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here + that must match the decoder order + activation (str): Activation function. + activation_params (dict): Parameters to provide to the activation function + norm (str): Normalization method. + norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. + kernel_size (int): Kernel size for the initial convolution. + last_kernel_size (int): Kernel size for the initial convolution. + residual_kernel_size (int): Kernel size for the residual layers. + dilation_base (int): How much to increase the dilation with each layer. + causal (bool): Whether to use fully causal convolution. + pad_mode (str): Padding mode for the convolutions. + true_skip (bool): Whether to use true skip connection or a simple + (streamable) convolution as the skip connection in the residual network blocks. + compress (int): Reduced dimensionality in residual branches (from Demucs v3). + lstm (int): Number of LSTM layers at the end of the encoder. + """ + def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, + ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, + norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, + last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, + pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2): + super().__init__() + self.channels = channels + self.dimension = dimension + self.n_filters = n_filters + self.ratios = list(reversed(ratios)) + del ratios + self.n_residual_layers = n_residual_layers + self.hop_length = np.prod(self.ratios) + + act = getattr(nn, activation) + mult = 1 + model: tp.List[nn.Module] = [ + SConv1d(channels, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode) + ] + # Downsample to raw audio scale + for i, ratio in enumerate(self.ratios): + # Add residual layers + for j in range(n_residual_layers): + model += [ + SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1], + dilations=[dilation_base ** j, 1], + norm=norm, norm_params=norm_params, + activation=activation, activation_params=activation_params, + causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] + + # Add downsampling layers + model += [ + act(**activation_params), + SConv1d(mult * n_filters, mult * n_filters * 2, + kernel_size=ratio * 2, stride=ratio, + norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode), + ] + mult *= 2 + + if lstm: + model += [SLSTM(mult * n_filters, num_layers=lstm)] + + model += [ + act(**activation_params), + SConv1d(mult * n_filters, dimension, last_kernel_size, norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode) + ] + + self.model = nn.Sequential(*model) + + def forward(self, x): + return self.model(x) + + +class SEANetDecoder(nn.Module): + """SEANet decoder. + Args: + channels (int): Audio channels. + dimension (int): Intermediate representation dimension. + n_filters (int): Base width for the model. + n_residual_layers (int): nb of residual layers. + ratios (Sequence[int]): kernel size and stride ratios + activation (str): Activation function. + activation_params (dict): Parameters to provide to the activation function + final_activation (str): Final activation function after all convolutions. + final_activation_params (dict): Parameters to provide to the activation function + norm (str): Normalization method. + norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. + kernel_size (int): Kernel size for the initial convolution. + last_kernel_size (int): Kernel size for the initial convolution. + residual_kernel_size (int): Kernel size for the residual layers. + dilation_base (int): How much to increase the dilation with each layer. + causal (bool): Whether to use fully causal convolution. + pad_mode (str): Padding mode for the convolutions. + true_skip (bool): Whether to use true skip connection or a simple + (streamable) convolution as the skip connection in the residual network blocks. + compress (int): Reduced dimensionality in residual branches (from Demucs v3). + lstm (int): Number of LSTM layers at the end of the encoder. + trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup. + If equal to 1.0, it means that all the trimming is done at the right. + """ + def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, + ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, + final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None, + norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, + last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, + pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2, + trim_right_ratio: float = 1.0): + super().__init__() + self.dimension = dimension + self.channels = channels + self.n_filters = n_filters + self.ratios = ratios + del ratios + self.n_residual_layers = n_residual_layers + self.hop_length = np.prod(self.ratios) + + act = getattr(nn, activation) + mult = int(2 ** len(self.ratios)) + model: tp.List[nn.Module] = [ + SConv1d(dimension, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode) + ] + + if lstm: + model += [SLSTM(mult * n_filters, num_layers=lstm)] + + # Upsample to raw audio scale + for i, ratio in enumerate(self.ratios): + # Add upsampling layers + model += [ + act(**activation_params), + SConvTranspose1d(mult * n_filters, mult * n_filters // 2, + kernel_size=ratio * 2, stride=ratio, + norm=norm, norm_kwargs=norm_params, + causal=causal, trim_right_ratio=trim_right_ratio), + ] + # Add residual layers + for j in range(n_residual_layers): + model += [ + SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1], + dilations=[dilation_base ** j, 1], + activation=activation, activation_params=activation_params, + norm=norm, norm_params=norm_params, causal=causal, + pad_mode=pad_mode, compress=compress, true_skip=true_skip)] + + mult //= 2 + + # Add final layers + model += [ + act(**activation_params), + SConv1d(n_filters, channels, last_kernel_size, norm=norm, norm_kwargs=norm_params, + causal=causal, pad_mode=pad_mode) + ] + # Add optional final activation to decoder (eg. tanh) + if final_activation is not None: + final_act = getattr(nn, final_activation) + final_activation_params = final_activation_params or {} + model += [ + final_act(**final_activation_params) + ] + self.model = nn.Sequential(*model) + + def forward(self, z): + y = self.model(z) + return y + + +def test(): + import torch + encoder = SEANetEncoder() + decoder = SEANetDecoder() + x = torch.randn(1, 1, 24000) + z = encoder(x) + assert list(z.shape) == [1, 128, 75], z.shape + y = decoder(z) + assert y.shape == x.shape, (x.shape, y.shape) + + +if __name__ == '__main__': + test() diff --git a/encodec/modules/transformer.py b/encodec/modules/transformer.py new file mode 100644 index 0000000..44b4791 --- /dev/null +++ b/encodec/modules/transformer.py @@ -0,0 +1,119 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""A streamable transformer.""" + +import typing as tp + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000): + """Create time embedding for the given positions, target dimension `dim`. + """ + # We aim for BTC format + assert dim % 2 == 0 + half_dim = dim // 2 + adim = torch.arange(half_dim, device=positions.device).view(1, 1, -1) + phase = positions / (max_period ** (adim / (half_dim - 1))) + return torch.cat([ + torch.cos(phase), + torch.sin(phase), + ], dim=-1) + + +class StreamingTransformerEncoderLayer(nn.TransformerEncoderLayer): + def forward(self, x: torch.Tensor, x_past: torch.Tensor, past_context: int): # type: ignore + if self.norm_first: + sa_input = self.norm1(x) + x = x + self._sa_block(sa_input, x_past, past_context) + x = x + self._ff_block(self.norm2(x)) + else: + sa_input = x + x = self.norm1(x + self._sa_block(sa_input, x_past, past_context)) + x = self.norm2(x + self._ff_block(x)) + + return x, sa_input + + # self-attention block + def _sa_block(self, x: torch.Tensor, x_past: torch.Tensor, past_context: int): # type: ignore + _, T, _ = x.shape + _, H, _ = x_past.shape + + queries = x + keys = torch.cat([x_past, x], dim=1) + values = keys + + queries_pos = torch.arange(H, T + H, device=x.device).view(-1, 1) + keys_pos = torch.arange(T + H, device=x.device).view(1, -1) + delta = queries_pos - keys_pos + valid_access = (delta >= 0) & (delta <= past_context) + x = self.self_attn(queries, keys, values, + attn_mask=~valid_access, + need_weights=False)[0] + return self.dropout1(x) + + +class StreamingTransformerEncoder(nn.Module): + """TransformerEncoder with streaming support. + + Args: + dim (int): dimension of the data. + hidden_scale (int): intermediate dimension of FF module is this times the dimension. + num_heads (int): number of heads. + num_layers (int): number of layers. + max_period (float): maxium period of cosines in the positional embedding. + past_context (int or None): receptive field for the causal mask, infinite if None. + gelu (bool): if true uses GeLUs, otherwise use ReLUs. + norm_in (bool): normalize the input. + dropout (float): dropout probability. + **kwargs: See `nn.TransformerEncoderLayer`. + """ + def __init__(self, dim, hidden_scale: float = 4., num_heads: int = 8, num_layers: int = 5, + max_period: float = 10000, past_context: int = 1000, gelu: bool = True, + norm_in: bool = True, dropout: float = 0., **kwargs): + super().__init__() + assert dim % num_heads == 0 + hidden_dim = int(dim * hidden_scale) + + self.max_period = max_period + self.past_context = past_context + activation: tp.Any = F.gelu if gelu else F.relu + + self.norm_in: nn.Module + if norm_in: + self.norm_in = nn.LayerNorm(dim) + else: + self.norm_in = nn.Identity() + + self.layers = nn.ModuleList() + for idx in range(num_layers): + self.layers.append( + StreamingTransformerEncoderLayer( + dim, num_heads, hidden_dim, + activation=activation, batch_first=True, dropout=dropout, **kwargs)) + + def forward(self, x: torch.Tensor, + states: tp.Optional[tp.List[torch.Tensor]] = None, + offset: tp.Union[int, torch.Tensor] = 0): + B, T, C = x.shape + if states is None: + states = [torch.zeros_like(x[:, :1]) for _ in range(1 + len(self.layers))] + + positions = torch.arange(T, device=x.device).view(1, -1, 1) + offset + pos_emb = create_sin_embedding(positions, C, max_period=self.max_period) + + new_state: tp.List[torch.Tensor] = [] + x = self.norm_in(x) + x = x + pos_emb + + for layer_state, layer in zip(states, self.layers): + x, new_layer_state = layer(x, layer_state, self.past_context) + new_layer_state = torch.cat([layer_state, new_layer_state], dim=1) + new_state.append(new_layer_state[:, -self.past_context:, :]) + return x, new_state, offset + T diff --git a/encodec/msstftd.py b/encodec/msstftd.py new file mode 100644 index 0000000..a1d3242 --- /dev/null +++ b/encodec/msstftd.py @@ -0,0 +1,147 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""MS-STFT discriminator, provided here for reference.""" + +import typing as tp + +import torchaudio +import torch +from torch import nn +from einops import rearrange + +from .modules import NormConv2d + + +FeatureMapType = tp.List[torch.Tensor] +LogitsType = torch.Tensor +DiscriminatorOutput = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]] + + +def get_2d_padding(kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1)): + return (((kernel_size[0] - 1) * dilation[0]) // 2, ((kernel_size[1] - 1) * dilation[1]) // 2) + + +class DiscriminatorSTFT(nn.Module): + """STFT sub-discriminator. + Args: + filters (int): Number of filters in convolutions + in_channels (int): Number of input channels. Default: 1 + out_channels (int): Number of output channels. Default: 1 + n_fft (int): Size of FFT for each scale. Default: 1024 + hop_length (int): Length of hop between STFT windows for each scale. Default: 256 + kernel_size (tuple of int): Inner Conv2d kernel sizes. Default: ``(3, 9)`` + stride (tuple of int): Inner Conv2d strides. Default: ``(1, 2)`` + dilations (list of int): Inner Conv2d dilation on the time dimension. Default: ``[1, 2, 4]`` + win_length (int): Window size for each scale. Default: 1024 + normalized (bool): Whether to normalize by magnitude after stft. Default: True + norm (str): Normalization method. Default: `'weight_norm'` + activation (str): Activation function. Default: `'LeakyReLU'` + activation_params (dict): Parameters to provide to the activation function. + growth (int): Growth factor for the filters. Default: 1 + """ + def __init__(self, filters: int, in_channels: int = 1, out_channels: int = 1, + n_fft: int = 1024, hop_length: int = 256, win_length: int = 1024, max_filters: int = 1024, + filters_scale: int = 1, kernel_size: tp.Tuple[int, int] = (3, 9), dilations: tp.List = [1, 2, 4], + stride: tp.Tuple[int, int] = (1, 2), normalized: bool = True, norm: str = 'weight_norm', + activation: str = 'LeakyReLU', activation_params: dict = {'negative_slope': 0.2}): + super().__init__() + assert len(kernel_size) == 2 + assert len(stride) == 2 + self.filters = filters + self.in_channels = in_channels + self.out_channels = out_channels + self.n_fft = n_fft + self.hop_length = hop_length + self.win_length = win_length + self.normalized = normalized + self.activation = getattr(torch.nn, activation)(**activation_params) + self.spec_transform = torchaudio.transforms.Spectrogram( + n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window_fn=torch.hann_window, + normalized=self.normalized, center=False, pad_mode=None, power=None) + spec_channels = 2 * self.in_channels + self.convs = nn.ModuleList() + self.convs.append( + NormConv2d(spec_channels, self.filters, kernel_size=kernel_size, padding=get_2d_padding(kernel_size)) + ) + in_chs = min(filters_scale * self.filters, max_filters) + for i, dilation in enumerate(dilations): + out_chs = min((filters_scale ** (i + 1)) * self.filters, max_filters) + self.convs.append(NormConv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride, + dilation=(dilation, 1), padding=get_2d_padding(kernel_size, (dilation, 1)), + norm=norm)) + in_chs = out_chs + out_chs = min((filters_scale ** (len(dilations) + 1)) * self.filters, max_filters) + self.convs.append(NormConv2d(in_chs, out_chs, kernel_size=(kernel_size[0], kernel_size[0]), + padding=get_2d_padding((kernel_size[0], kernel_size[0])), + norm=norm)) + self.conv_post = NormConv2d(out_chs, self.out_channels, + kernel_size=(kernel_size[0], kernel_size[0]), + padding=get_2d_padding((kernel_size[0], kernel_size[0])), + norm=norm) + + def forward(self, x: torch.Tensor): + fmap = [] + z = self.spec_transform(x) # [B, 2, Freq, Frames, 2] + z = torch.cat([z.real, z.imag], dim=1) + z = rearrange(z, 'b c w t -> b c t w') + for i, layer in enumerate(self.convs): + z = layer(z) + z = self.activation(z) + fmap.append(z) + z = self.conv_post(z) + return z, fmap + + +class MultiScaleSTFTDiscriminator(nn.Module): + """Multi-Scale STFT (MS-STFT) discriminator. + Args: + filters (int): Number of filters in convolutions + in_channels (int): Number of input channels. Default: 1 + out_channels (int): Number of output channels. Default: 1 + n_ffts (Sequence[int]): Size of FFT for each scale + hop_lengths (Sequence[int]): Length of hop between STFT windows for each scale + win_lengths (Sequence[int]): Window size for each scale + **kwargs: additional args for STFTDiscriminator + """ + def __init__(self, filters: int, in_channels: int = 1, out_channels: int = 1, + n_ffts: tp.List[int] = [1024, 2048, 512], hop_lengths: tp.List[int] = [256, 512, 128], + win_lengths: tp.List[int] = [1024, 2048, 512], **kwargs): + super().__init__() + assert len(n_ffts) == len(hop_lengths) == len(win_lengths) + self.discriminators = nn.ModuleList([ + DiscriminatorSTFT(filters, in_channels=in_channels, out_channels=out_channels, + n_fft=n_ffts[i], win_length=win_lengths[i], hop_length=hop_lengths[i], **kwargs) + for i in range(len(n_ffts)) + ]) + self.num_discriminators = len(self.discriminators) + + def forward(self, x: torch.Tensor) -> DiscriminatorOutput: + logits = [] + fmaps = [] + for disc in self.discriminators: + logit, fmap = disc(x) + logits.append(logit) + fmaps.append(fmap) + return logits, fmaps + + +def test(): + disc = MultiScaleSTFTDiscriminator(filters=32) + y = torch.randn(1, 1, 24000) + y_hat = torch.randn(1, 1, 24000) + + y_disc_r, fmap_r = disc(y) + y_disc_gen, fmap_gen = disc(y_hat) + assert len(y_disc_r) == len(y_disc_gen) == len(fmap_r) == len(fmap_gen) == disc.num_discriminators + + assert all([len(fm) == 5 for fm in fmap_r + fmap_gen]) + assert all([list(f.shape)[:2] == [1, 32] for fm in fmap_r + fmap_gen for f in fm]) + assert all([len(logits.shape) == 4 for logits in y_disc_r + y_disc_gen]) + + +if __name__ == '__main__': + test() diff --git a/encodec/quantization/__init__.py b/encodec/quantization/__init__.py new file mode 100644 index 0000000..bfabe52 --- /dev/null +++ b/encodec/quantization/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the 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@@ -0,0 +1,292 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Arithmetic coder.""" + +import io +import math +import random +import typing as tp +import torch + +from ..binary import BitPacker, BitUnpacker + + +def build_stable_quantized_cdf(pdf: torch.Tensor, total_range_bits: int, + roundoff: float = 1e-8, min_range: int = 2, + check: bool = True) -> torch.Tensor: + """Turn the given PDF into a quantized CDF that splits + [0, 2 ** self.total_range_bits - 1] into chunks of size roughly proportional + to the PDF. + + Args: + pdf (torch.Tensor): probability distribution, shape should be `[N]`. + total_range_bits (int): see `ArithmeticCoder`, the typical range we expect + during the coding process is `[0, 2 ** total_range_bits - 1]`. + roundoff (float): will round the pdf up to that level to remove difference coming + from e.g. evaluating the Language Model on different architectures. + min_range (int): minimum range width. Should always be at least 2 for numerical + stability. Use this to avoid pathological behavior is a value + that is expected to be rare actually happens in real life. + check (bool): if True, checks that nothing bad happened, can be deactivated for speed. + """ + pdf = pdf.detach() + if roundoff: + pdf = (pdf / roundoff).floor() * roundoff + # interpolate with uniform distribution to achieve desired minimum probability. + total_range = 2 ** total_range_bits + cardinality = len(pdf) + alpha = min_range * cardinality / total_range + assert alpha <= 1, "you must reduce min_range" + ranges = (((1 - alpha) * total_range) * pdf).floor().long() + ranges += min_range + quantized_cdf = torch.cumsum(ranges, dim=-1) + if min_range < 2: + raise ValueError("min_range must be at least 2.") + if check: + assert quantized_cdf[-1] <= 2 ** total_range_bits, quantized_cdf[-1] + if ((quantized_cdf[1:] - quantized_cdf[:-1]) < min_range).any() or quantized_cdf[0] < min_range: + raise ValueError("You must increase your total_range_bits.") + return quantized_cdf + + +class ArithmeticCoder: + """ArithmeticCoder, + Let us take a distribution `p` over `N` symbols, and assume we have a stream + of random variables `s_t` sampled from `p`. Let us assume that we have a budget + of `B` bits that we can afford to write on device. There are `2**B` possible numbers, + corresponding to the range `[0, 2 ** B - 1]`. We can map each of those number to a single + sequence `(s_t)` by doing the following: + + 1) Initialize the current range to` [0 ** 2 B - 1]`. + 2) For each time step t, split the current range into contiguous chunks, + one for each possible outcome, with size roughly proportional to `p`. + For instance, if `p = [0.75, 0.25]`, and the range is `[0, 3]`, the chunks + would be `{[0, 2], [3, 3]}`. + 3) Select the chunk corresponding to `s_t`, and replace the current range with this. + 4) When done encoding all the values, just select any value remaining in the range. + + You will notice that this procedure can fail: for instance if at any point in time + the range is smaller than `N`, then we can no longer assign a non-empty chunk to each + possible outcome. Intuitively, the more likely a value is, the less the range width + will reduce, and the longer we can go on encoding values. This makes sense: for any efficient + coding scheme, likely outcomes would take less bits, and more of them can be coded + with a fixed budget. + + In practice, we do not know `B` ahead of time, but we have a way to inject new bits + when the current range decreases below a given limit (given by `total_range_bits`), without + having to redo all the computations. If we encode mostly likely values, we will seldom + need to inject new bits, but a single rare value can deplete our stock of entropy! + + In this explanation, we assumed that the distribution `p` was constant. In fact, the present + code works for any sequence `(p_t)` possibly different for each timestep. + We also assume that `s_t ~ p_t`, but that doesn't need to be true, although the smaller + the KL between the true distribution and `p_t`, the most efficient the coding will be. + + Args: + fo (IO[bytes]): file-like object to which the bytes will be written to. + total_range_bits (int): the range `M` described above is `2 ** total_range_bits. + Any time the current range width fall under this limit, new bits will + be injected to rescale the initial range. + """ + + def __init__(self, fo: tp.IO[bytes], total_range_bits: int = 24): + assert total_range_bits <= 30 + self.total_range_bits = total_range_bits + self.packer = BitPacker(bits=1, fo=fo) # we push single bits at a time. + self.low: int = 0 + self.high: int = 0 + self.max_bit: int = -1 + self._dbg: tp.List[tp.Any] = [] + self._dbg2: tp.List[tp.Any] = [] + + @property + def delta(self) -> int: + """Return the current range width.""" + return self.high - self.low + 1 + + def _flush_common_prefix(self): + # If self.low and self.high start with the sames bits, + # those won't change anymore as we always just increase the range + # by powers of 2, and we can flush them out to the bit stream. + assert self.high >= self.low, (self.low, self.high) + assert self.high < 2 ** (self.max_bit + 1) + while self.max_bit >= 0: + b1 = self.low >> self.max_bit + b2 = self.high >> self.max_bit + if b1 == b2: + self.low -= (b1 << self.max_bit) + self.high -= (b1 << self.max_bit) + assert self.high >= self.low, (self.high, self.low, self.max_bit) + assert self.low >= 0 + self.max_bit -= 1 + self.packer.push(b1) + else: + break + + def push(self, symbol: int, quantized_cdf: torch.Tensor): + """Push the given symbol on the stream, flushing out bits + if possible. + + Args: + symbol (int): symbol to encode with the AC. + quantized_cdf (torch.Tensor): use `build_stable_quantized_cdf` + to build this from your pdf estimate. + """ + while self.delta < 2 ** self.total_range_bits: + self.low *= 2 + self.high = self.high * 2 + 1 + self.max_bit += 1 + + range_low = 0 if symbol == 0 else quantized_cdf[symbol - 1].item() + range_high = quantized_cdf[symbol].item() - 1 + effective_low = int(math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))) + effective_high = int(math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))) + assert self.low <= self.high + self.high = self.low + effective_high + self.low = self.low + effective_low + assert self.low <= self.high, (effective_low, effective_high, range_low, range_high) + self._dbg.append((self.low, self.high)) + self._dbg2.append((self.low, self.high)) + outs = self._flush_common_prefix() + assert self.low <= self.high + assert self.max_bit >= -1 + assert self.max_bit <= 61, self.max_bit + return outs + + def flush(self): + """Flush the remaining information to the stream. + """ + while self.max_bit >= 0: + b1 = (self.low >> self.max_bit) & 1 + self.packer.push(b1) + self.max_bit -= 1 + self.packer.flush() + + +class ArithmeticDecoder: + """ArithmeticDecoder, see `ArithmeticCoder` for a detailed explanation. + + Note that this must be called with **exactly** the same parameters and sequence + of quantized cdf as the arithmetic encoder or the wrong values will be decoded. + + If the AC encoder current range is [L, H], with `L` and `H` having the some common + prefix (i.e. the same most significant bits), then this prefix will be flushed to the stream. + For instances, having read 3 bits `b1 b2 b3`, we know that `[L, H]` is contained inside + `[b1 b2 b3 0 ... 0 b1 b3 b3 1 ... 1]`. Now this specific sub-range can only be obtained + for a specific sequence of symbols and a binary-search allows us to decode those symbols. + At some point, the prefix `b1 b2 b3` will no longer be sufficient to decode new symbols, + and we will need to read new bits from the stream and repeat the process. + + """ + def __init__(self, fo: tp.IO[bytes], total_range_bits: int = 24): + self.total_range_bits = total_range_bits + self.low: int = 0 + self.high: int = 0 + self.current: int = 0 + self.max_bit: int = -1 + self.unpacker = BitUnpacker(bits=1, fo=fo) # we pull single bits at a time. + # Following is for debugging + self._dbg: tp.List[tp.Any] = [] + self._dbg2: tp.List[tp.Any] = [] + self._last: tp.Any = None + + @property + def delta(self) -> int: + return self.high - self.low + 1 + + def _flush_common_prefix(self): + # Given the current range [L, H], if both have a common prefix, + # we know we can remove it from our representation to avoid handling large numbers. + while self.max_bit >= 0: + b1 = self.low >> self.max_bit + b2 = self.high >> self.max_bit + if b1 == b2: + self.low -= (b1 << self.max_bit) + self.high -= (b1 << self.max_bit) + self.current -= (b1 << self.max_bit) + assert self.high >= self.low + assert self.low >= 0 + self.max_bit -= 1 + else: + break + + def pull(self, quantized_cdf: torch.Tensor) -> tp.Optional[int]: + """Pull a symbol, reading as many bits from the stream as required. + This returns `None` when the stream has been exhausted. + + Args: + quantized_cdf (torch.Tensor): use `build_stable_quantized_cdf` + to build this from your pdf estimate. This must be **exatly** + the same cdf as the one used at encoding time. + """ + while self.delta < 2 ** self.total_range_bits: + bit = self.unpacker.pull() + if bit is None: + return None + self.low *= 2 + self.high = self.high * 2 + 1 + self.current = self.current * 2 + bit + self.max_bit += 1 + + def bin_search(low_idx: int, high_idx: int): + # Binary search is not just for coding interviews :) + if high_idx < low_idx: + raise RuntimeError("Binary search failed") + mid = (low_idx + high_idx) // 2 + range_low = quantized_cdf[mid - 1].item() if mid > 0 else 0 + range_high = quantized_cdf[mid].item() - 1 + effective_low = int(math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))) + effective_high = int(math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))) + low = effective_low + self.low + high = effective_high + self.low + if self.current >= low: + if self.current <= high: + return (mid, low, high, self.current) + else: + return bin_search(mid + 1, high_idx) + else: + return bin_search(low_idx, mid - 1) + + self._last = (self.low, self.high, self.current, self.max_bit) + sym, self.low, self.high, self.current = bin_search(0, len(quantized_cdf) - 1) + self._dbg.append((self.low, self.high, self.current)) + self._flush_common_prefix() + self._dbg2.append((self.low, self.high, self.current)) + + return sym + + +def test(): + torch.manual_seed(1234) + random.seed(1234) + for _ in range(4): + pdfs = [] + cardinality = random.randrange(4000) + steps = random.randrange(100, 500) + fo = io.BytesIO() + encoder = ArithmeticCoder(fo) + symbols = [] + for step in range(steps): + pdf = torch.softmax(torch.randn(cardinality), dim=0) + pdfs.append(pdf) + q_cdf = build_stable_quantized_cdf(pdf, encoder.total_range_bits) + symbol = torch.multinomial(pdf, 1).item() + symbols.append(symbol) + encoder.push(symbol, q_cdf) + encoder.flush() + + fo.seek(0) + decoder = ArithmeticDecoder(fo) + for idx, (pdf, symbol) in enumerate(zip(pdfs, symbols)): + q_cdf = build_stable_quantized_cdf(pdf, encoder.total_range_bits) + decoded_symbol = decoder.pull(q_cdf) + assert decoded_symbol == symbol, idx + assert decoder.pull(torch.zeros(1)) is None + + +if __name__ == "__main__": + test() diff --git a/encodec/quantization/core_vq.py b/encodec/quantization/core_vq.py new file mode 100644 index 0000000..4bc3045 --- /dev/null +++ b/encodec/quantization/core_vq.py @@ -0,0 +1,460 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# This implementation is inspired from +# https://github.com/lucidrains/vector-quantize-pytorch +# which is released under MIT License. Hereafter, the original license: +# MIT License +# +# Copyright (c) 2020 Phil Wang +# +# 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. + +"""Core vector quantization implementation.""" + +import typing as tp +import warnings + +from einops import rearrange, repeat +import torch +from torch import nn +import torch.nn.functional as F + +from .. import distrib + + +def default(val: tp.Any, d: tp.Any) -> tp.Any: + return val if val is not None else d + + +def ema_inplace(moving_avg, new, decay: float): + moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) + + +def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5): + return (x + epsilon) / (x.sum() + n_categories * epsilon) + + +def uniform_init(*shape: int): + t = torch.empty(shape) + nn.init.kaiming_uniform_(t) + return t + + +def sample_vectors(samples, num: int): + num_samples, device = samples.shape[0], samples.device + + if num_samples >= num: + indices = torch.randperm(num_samples, device=device)[:num] + else: + indices = torch.randint(0, num_samples, (num,), device=device) + + return samples[indices] + + +def kmeans(samples, num_clusters: int, num_iters: int = 10): + dim, dtype = samples.shape[-1], samples.dtype + + means = sample_vectors(samples, num_clusters) + + for _ in range(num_iters): + diffs = rearrange(samples, "n d -> n () d") - rearrange( + means, "c d -> () c d" + ) + dists = -(diffs ** 2).sum(dim=-1) + + buckets = dists.max(dim=-1).indices + bins = torch.bincount(buckets, minlength=num_clusters) + zero_mask = bins == 0 + bins_min_clamped = bins.masked_fill(zero_mask, 1) + + new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype) + new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples) + new_means = new_means / bins_min_clamped[..., None] + + means = torch.where(zero_mask[..., None], means, new_means) + + return means, bins + + +class EuclideanCodebook(nn.Module): + """Codebook with Euclidean distance. + Args: + dim (int): Dimension. + codebook_size (int): Codebook size. + kmeans_init (bool): Whether to use k-means to initialize the codebooks. + If set to true, run the k-means algorithm on the first training batch and use + the learned centroids as initialization. + kmeans_iters (int): Number of iterations used for k-means algorithm at initialization. + decay (float): Decay for exponential moving average over the codebooks. + epsilon (float): Epsilon value for numerical stability. + threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes + that have an exponential moving average cluster size less than the specified threshold with + randomly selected vector from the current batch. + """ + def __init__( + self, + dim: int, + codebook_size: int, + kmeans_init: int = False, + kmeans_iters: int = 10, + decay: float = 0.99, + epsilon: float = 1e-5, + threshold_ema_dead_code: int = 2, + ): + super().__init__() + self.decay = decay + init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros + embed = init_fn(codebook_size, dim) + + self.codebook_size = codebook_size + + self.kmeans_iters = kmeans_iters + self.epsilon = epsilon + self.threshold_ema_dead_code = threshold_ema_dead_code + + self.register_buffer("inited", torch.Tensor([not kmeans_init])) + self.register_buffer("cluster_size", torch.zeros(codebook_size)) + self.register_buffer("embed", embed) + self.register_buffer("embed_avg", embed.clone()) + + @torch.jit.ignore + def init_embed_(self, data): + if self.inited: + return + + embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters) #data不变 + self.embed.data.copy_(embed) + self.embed_avg.data.copy_(embed.clone()) + self.cluster_size.data.copy_(cluster_size) + self.inited.data.copy_(torch.Tensor([True])) + # Make sure all buffers across workers are in sync after initialization + distrib.broadcast_tensors(self.buffers()) + + def replace_(self, samples, mask): + modified_codebook = torch.where( + mask[..., None], sample_vectors(samples, self.codebook_size), self.embed + ) + self.embed.data.copy_(modified_codebook) + + def expire_codes_(self, batch_samples): + if self.threshold_ema_dead_code == 0: + return + + expired_codes = self.cluster_size < self.threshold_ema_dead_code + if not torch.any(expired_codes): + return + + batch_samples = rearrange(batch_samples, "... d -> (...) d") + self.replace_(batch_samples, mask=expired_codes) + distrib.broadcast_tensors(self.buffers()) + + def preprocess(self, x): + x = rearrange(x, "... d -> (...) d") + return x + + def quantize(self, x): + embed = self.embed.t() + dist = -( + x.pow(2).sum(1, keepdim=True) + - 2 * x @ embed + + embed.pow(2).sum(0, keepdim=True) + ) + embed_ind = dist.max(dim=-1).indices + return embed_ind + + def postprocess_emb(self, embed_ind, shape): + return embed_ind.view(*shape[:-1]) + + def dequantize(self, embed_ind): + quantize = F.embedding(embed_ind, self.embed) + return quantize + + def encode(self, x): + shape = x.shape + # pre-process + x = self.preprocess(x) + # quantize + embed_ind = self.quantize(x) + # post-process + embed_ind = self.postprocess_emb(embed_ind, shape) + return embed_ind + + def decode(self, embed_ind): + quantize = self.dequantize(embed_ind) + return quantize + + def forward(self, x): + shape, dtype = x.shape, x.dtype + x = self.preprocess(x) + + self.init_embed_(x) + + embed_ind = self.quantize(x) + embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype) + embed_ind = self.postprocess_emb(embed_ind, shape) + quantize = self.dequantize(embed_ind) + + if self.training: + # We do the expiry of code at that point as buffers are in sync + # and all the workers will take the same decision. + self.expire_codes_(x) + ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay) + embed_sum = x.t() @ embed_onehot + ema_inplace(self.embed_avg, embed_sum.t(), self.decay) + cluster_size = ( + laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon) + * self.cluster_size.sum() + ) + embed_normalized = self.embed_avg / cluster_size.unsqueeze(1) + self.embed.data.copy_(embed_normalized) + + return quantize, embed_ind + + +class VectorQuantization(nn.Module): + """Vector quantization implementation. + Currently supports only euclidean distance. + Args: + dim (int): Dimension + codebook_size (int): Codebook size + codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim. + decay (float): Decay for exponential moving average over the codebooks. + epsilon (float): Epsilon value for numerical stability. + kmeans_init (bool): Whether to use kmeans to initialize the codebooks. + kmeans_iters (int): Number of iterations used for kmeans initialization. + threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes + that have an exponential moving average cluster size less than the specified threshold with + randomly selected vector from the current batch. + commitment_weight (float): Weight for commitment loss. + """ + def __init__( + self, + dim: int, + codebook_size: int, + codebook_dim: tp.Optional[int] = None, + decay: float = 0.99, + epsilon: float = 1e-5, + kmeans_init: bool = True, + kmeans_iters: int = 50, + threshold_ema_dead_code: int = 2, + commitment_weight: float = 1., + ): + super().__init__() + _codebook_dim: int = default(codebook_dim, dim) + + requires_projection = _codebook_dim != dim + self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()) + self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()) + + self.epsilon = epsilon + self.commitment_weight = commitment_weight + + self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size, + kmeans_init=kmeans_init, kmeans_iters=kmeans_iters, + decay=decay, epsilon=epsilon, + threshold_ema_dead_code=threshold_ema_dead_code) + self.codebook_size = codebook_size + + @property + def codebook(self): + return self._codebook.embed + + def encode(self, x): + x = rearrange(x, "b d n -> b n d") + x = self.project_in(x) + embed_in = self._codebook.encode(x) + return embed_in + + def decode(self, embed_ind): + quantize = self._codebook.decode(embed_ind) + quantize = self.project_out(quantize) + quantize = rearrange(quantize, "b n d -> b d n") + return quantize + + def forward(self, x): + device = x.device + x = rearrange(x, "b d n -> b n d") + x = self.project_in(x) + quantize, embed_ind = self._codebook(x) + if self.training: + quantize = x + (quantize - x).detach() + loss = torch.tensor([0.0], device=device, requires_grad=self.training) + + if self.training: + # warnings.warn('When using RVQ in training model, first check ' + # 'https://github.com/facebookresearch/encodec/issues/25 . ' + # 'The bug wasn\'t fixed here for reproducibility.') + if self.commitment_weight > 0: + commit_loss = F.mse_loss(quantize.detach(), x) + loss = loss + commit_loss * self.commitment_weight + + quantize = self.project_out(quantize) + quantize = rearrange(quantize, "b n d -> b d n") + return quantize, embed_ind, loss + + +class ResidualVectorQuantization(nn.Module): + """Residual vector quantization implementation. + Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf + """ + def __init__(self, *, num_quantizers, **kwargs): + super().__init__() + self.layers = nn.ModuleList( + [VectorQuantization(**kwargs) for _ in range(num_quantizers)] + ) + + def forward(self, x, n_q: tp.Optional[int] = None): + quantized_out = 0.0 + residual = x + + all_losses = [] + all_indices = [] + + n_q = n_q or len(self.layers) + for layer in self.layers[:n_q]: + quantized, indices, loss = layer(residual) + residual = residual - quantized.detach() + quantized_out = quantized_out + quantized + all_indices.append(indices) + all_losses.append(loss) + + out_losses, out_indices = map(torch.stack, (all_losses, all_indices)) + return quantized_out, out_indices, out_losses + + def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor: + residual = x + all_indices = [] + n_q = n_q or len(self.layers) + for layer in self.layers[:n_q]: + indices = layer.encode(residual) + all_indices.append(indices) + quantized = layer.decode(indices) + residual = residual - quantized.detach() + out_indices = torch.stack(all_indices) + return out_indices + + def decode(self, q_indices: torch.Tensor) -> torch.Tensor: + quantized_out = torch.tensor(0.0, device=q_indices.device) + for i, indices in enumerate(q_indices): + layer = self.layers[i] + quantized = layer.decode(indices) + quantized_out = quantized_out + quantized + return quantized_out + + +class LanguageVectorQuantization(nn.Module): + """Residual vector quantization implementation. + Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf + """ + def __init__(self, *, num_quantizers, **kwargs): + super().__init__() + self.layers = nn.ModuleList( + [VectorQuantization(**kwargs) for _ in range(num_quantizers)] + ) + # print("core_vq.py:self.layers",self.layers) + + def forward(self, x, n_q: tp.Optional[int] = None): + # breakpoint() x[b,t,c] #[64,75,128] + quantized_out = 0.0 + residual = x + shape1,shape2=residual.size()[1]//3,2*residual.size()[1]//3 + + # breakpoint() + + all_losses = [] + all_indices = [] + + # breakpoint() + + n_q = n_q or len(self.layers) + # debug + # breakpoint() #[64,128,75] + + # 构造分组 + for i in range(n_q): + if(i==0): + # breakpoint() + residual_0=torch.cat((residual[:,:shape1,:],torch.zeros_like(residual[:,shape1:,:])),dim=1) + out_quantized_0, indices, loss = self.layers[i](residual_0) + quantized_out = quantized_out + out_quantized_0 + all_indices.append(indices) + all_losses.append(loss) + elif(i==1): + residual_1=torch.cat((torch.zeros_like(residual[:,:shape1,:]),residual[:,shape1:shape2,:],torch.zeros_like(residual[:,shape2:,:])),dim=1) + # residual_1[:,:shape1,:]=0 + # residual_1[:,shape2:,:]=0 + out_quantized_1, indices, loss = self.layers[i](residual_1) + quantized_out = quantized_out + out_quantized_1 + all_indices.append(indices) + all_losses.append(loss) + elif(i==2): + residual_2=torch.cat((torch.zeros_like(residual[:,:shape2,:]),residual[:,shape2:,:]),dim=1) + out_quantized_2, indices, loss = self.layers[i](residual_2) + quantized_out = quantized_out + out_quantized_2 + all_indices.append(indices) + all_losses.append(loss) + elif(i==3): + # assert i==3 + residual=residual-out_quantized_0.detach()-out_quantized_1.detach()-out_quantized_2.detach() + quantized, indices, loss = self.layers[i](residual) + quantized_out = quantized_out + quantized + all_indices.append(indices) + all_losses.append(loss) + else: + residual = residual - quantized.detach() + quantized, indices, loss = self.layers[i](residual) #得到该层的表征，该层的indices,该层的loss [64,75] + quantized_out = quantized_out + quantized + all_indices.append(indices) + all_losses.append(loss) + + # for layer in self.layers[:n_q]: + # quantized, indices, loss = layer(residual) #得到该层的表征，该层的indices,该层的loss [64,75] + # residual = residual - quantized.detach() + # quantized_out = quantized_out + quantized + # all_indices.append(indices) + # all_losses.append(loss) + # # breakpoint() + # breakpoint() + + out_losses, out_indices = map(torch.stack, (all_losses, all_indices)) + return quantized_out, out_indices, out_losses + + def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor: + residual = x + all_indices = [] + n_q = n_q or len(self.layers) + for layer in self.layers[:n_q]: + indices = layer.encode(residual) + all_indices.append(indices) + quantized = layer.decode(indices) + residual = residual - quantized.detach() + out_indices = torch.stack(all_indices) + return out_indices + + def decode(self, q_indices: torch.Tensor) -> torch.Tensor: + quantized_out = torch.tensor(0.0, device=q_indices.device) + for i, indices in enumerate(q_indices): + layer = self.layers[i] + quantized = layer.decode(indices) + quantized_out = quantized_out + quantized + return quantized_out \ No newline at end of file diff --git a/encodec/quantization/vq.py b/encodec/quantization/vq.py new file mode 100644 index 0000000..82521ed --- /dev/null +++ b/encodec/quantization/vq.py @@ -0,0 +1,134 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Residual vector quantizer implementation.""" + +from dataclasses import dataclass, field +import math +import typing as tp + +import torch +from torch import nn + +from .core_vq import ResidualVectorQuantization,LanguageVectorQuantization + + +@dataclass +class QuantizedResult: + quantized: torch.Tensor + codes: torch.Tensor + bandwidth: torch.Tensor # bandwidth in kb/s used, per batch item. + penalty: tp.Optional[torch.Tensor] = None + metrics: dict = field(default_factory=dict) + + +class ResidualVectorQuantizer(nn.Module): + """Residual Vector Quantizer. + Args: + dimension (int): Dimension of the codebooks. + n_q (int): Number of residual vector quantizers used. + bins (int): Codebook size. + decay (float): Decay for exponential moving average over the codebooks. + kmeans_init (bool): Whether to use kmeans to initialize the codebooks. + kmeans_iters (int): Number of iterations used for kmeans initialization. + threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes + that have an exponential moving average cluster size less than the specified threshold with + randomly selected vector from the current batch. + """ + def __init__( + self, + dimension: int = 256, + n_q: int = 8, + bins: int = 1024, + decay: float = 0.99, + kmeans_init: bool = True, + kmeans_iters: int = 50, + threshold_ema_dead_code: int = 2, + ): + super().__init__() + self.n_q = n_q + self.dimension = dimension + self.bins = bins + self.decay = decay + self.kmeans_init = kmeans_init + self.kmeans_iters = kmeans_iters + self.threshold_ema_dead_code = threshold_ema_dead_code + self.vq = LanguageVectorQuantization( + dim=self.dimension, + codebook_size=self.bins, + num_quantizers=self.n_q, + decay=self.decay, + kmeans_init=self.kmeans_init, + kmeans_iters=self.kmeans_iters, + threshold_ema_dead_code=self.threshold_ema_dead_code, + ) + # self.vq = ResidualVectorQuantization( + # dim=self.dimension, + # codebook_size=self.bins, + # num_quantizers=self.n_q, + # decay=self.decay, + # kmeans_init=self.kmeans_init, + # kmeans_iters=self.kmeans_iters, + # threshold_ema_dead_code=self.threshold_ema_dead_code, + # ) + + + def forward(self, x: torch.Tensor, frame_rate: int, bandwidth: tp.Optional[float] = None) -> QuantizedResult: + """Residual vector quantization on the given input tensor. + Args: + x (torch.Tensor): Input tensor. + frame_rate (int): Sample rate of the input tensor. + bandwidth (float): Target bandwidth. + Returns: + QuantizedResult: + The quantized (or approximately quantized) representation with + the associated bandwidth and any penalty term for the loss. + """ + bw_per_q = self.get_bandwidth_per_quantizer(frame_rate) + n_q = self.get_num_quantizers_for_bandwidth(frame_rate, bandwidth) + # assert n_q==4 + # breakpoint() + nq_choice=[3,4,8] + if self.training: + choice = int(torch.randint(0, 3, (1,)).item()) + # breakpoint() + n_q=nq_choice[choice] + # n_q=8 + quantized, codes, commit_loss = self.vq(x, n_q=n_q) + bw = torch.tensor(n_q * bw_per_q).to(x) + return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss)) + + def get_num_quantizers_for_bandwidth(self, frame_rate: int, bandwidth: tp.Optional[float] = None) -> int: + """Return n_q based on specified target bandwidth. + """ + bw_per_q = self.get_bandwidth_per_quantizer(frame_rate) + n_q = self.n_q + if bandwidth and bandwidth > 0.: + # bandwidth is represented as a thousandth of what it is, e.g. 6kbps bandwidth is represented as + # bandwidth == 6.0 + n_q = int(max(1, math.floor(bandwidth * 1000 / bw_per_q))) + return n_q + + def get_bandwidth_per_quantizer(self, frame_rate: int): + """Return bandwidth per quantizer for a given input frame rate. + Each quantizer encodes a frame with lg(bins) bits. + """ + return math.log2(self.bins) * frame_rate + + def encode(self, x: torch.Tensor, frame_rate: int, bandwidth: tp.Optional[float] = None) -> torch.Tensor: + """Encode a given input tensor with the specified frame rate at the given bandwidth. + The RVQ encode method sets the appropriate number of quantizers to use + and returns indices for each quantizer. + """ + n_q = self.get_num_quantizers_for_bandwidth(frame_rate, bandwidth) + codes = self.vq.encode(x, n_q=n_q) + return codes + + def decode(self, codes: torch.Tensor) -> torch.Tensor: + """Decode the given codes to the quantized representation. + """ + quantized = self.vq.decode(codes) + return quantized diff --git a/encodec/utils.py b/encodec/utils.py new file mode 100644 index 0000000..e3f0f9e --- /dev/null +++ b/encodec/utils.py @@ -0,0 +1,103 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Various utilities.""" + +from hashlib import sha256 +from pathlib import Path +import typing as tp + +import torch +import torchaudio + + +def _linear_overlap_add(frames: tp.List[torch.Tensor], stride: int): + # Generic overlap add, with linear fade-in/fade-out, supporting complex scenario + # e.g., more than 2 frames per position. + # The core idea is to use a weight function that is a triangle, + # with a maximum value at the middle of the segment. + # We use this weighting when summing the frames, and divide by the sum of weights + # for each positions at the end. Thus: + # - if a frame is the only one to cover a position, the weighting is a no-op. + # - if 2 frames cover a position: + # ... ... + # / \/ \ + # / /\ \ + # S T , i.e. S offset of second frame starts, T end of first frame. + # Then the weight function for each one is: (t - S), (T - t), with `t` a given offset. + # After the final normalization, the weight of the second frame at position `t` is + # (t - S) / (t - S + (T - t)) = (t - S) / (T - S), which is exactly what we want. + # + # - if more than 2 frames overlap at a given point, we hope that by induction + # something sensible happens. + assert len(frames) + device = frames[0].device + dtype = frames[0].dtype + shape = frames[0].shape[:-1] + total_size = stride * (len(frames) - 1) + frames[-1].shape[-1] + + frame_length = frames[0].shape[-1] + t = torch.linspace(0, 1, frame_length + 2, device=device, dtype=dtype)[1: -1] + weight = 0.5 - (t - 0.5).abs() + + sum_weight = torch.zeros(total_size, device=device, dtype=dtype) + out = torch.zeros(*shape, total_size, device=device, dtype=dtype) + offset: int = 0 + + for frame in frames: + frame_length = frame.shape[-1] + out[..., offset:offset + frame_length] += weight[:frame_length] * frame + sum_weight[offset:offset + frame_length] += weight[:frame_length] + offset += stride + assert sum_weight.min() > 0 + return out / sum_weight + + +def _get_checkpoint_url(root_url: str, checkpoint: str): + if not root_url.endswith('/'): + root_url += '/' + return root_url + checkpoint + + +def _check_checksum(path: Path, checksum: str): + sha = sha256() + with open(path, 'rb') as file: + while True: + buf = file.read(2**20) + if not buf: + break + sha.update(buf) + actual_checksum = sha.hexdigest()[:len(checksum)] + if actual_checksum != checksum: + raise RuntimeError(f'Invalid checksum for file {path}, ' + f'expected {checksum} but got {actual_checksum}') + + +def convert_audio(wav: torch.Tensor, sr: int, target_sr: int, target_channels: int): + assert wav.dim() >= 2, "Audio tensor must have at least 2 dimensions" + assert wav.shape[-2] in [1, 2], "Audio must be mono or stereo." + *shape, channels, length = wav.shape + if target_channels == 1: + wav = wav.mean(-2, keepdim=True) + elif target_channels == 2: + wav = wav.expand(*shape, target_channels, length) + elif channels == 1: + wav = wav.expand(target_channels, -1) + else: + raise RuntimeError(f"Impossible to convert from {channels} to {target_channels}") + wav = torchaudio.transforms.Resample(sr, target_sr)(wav) + return wav + + +def save_audio(wav: torch.Tensor, path: tp.Union[Path, str], + sample_rate: int, rescale: bool = False): + limit = 0.99 + mx = wav.abs().max() + if rescale: + wav = wav * min(limit / mx, 1) + else: + wav = wav.clamp(-limit, limit) + torchaudio.save(str(path), wav, sample_rate=sample_rate, encoding='PCM_S', bits_per_sample=16) diff --git a/metrics/UTMOS.py b/metrics/UTMOS.py new file mode 100644 index 0000000..5e6e9a5 --- /dev/null +++ b/metrics/UTMOS.py @@ -0,0 +1,223 @@ +import os + +import fairseq +import pytorch_lightning as pl +import requests +import torch +import torch.nn as nn +from tqdm import tqdm + +UTMOS_CKPT_URL = "https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt" +WAV2VEC_URL = "https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt" + +""" +UTMOS score, automatic Mean Opinion Score (MOS) prediction system, +adapted from https://huggingface.co/spaces/sarulab-speech/UTMOS-demo +""" + + +class UTMOSScore: + """Predicting score for each audio clip.""" + + def __init__(self, device, ckpt_path="epoch=3-step=7459.ckpt"): + self.device = device + filepath = os.path.join(os.path.dirname(__file__), ckpt_path) + if not os.path.exists(filepath): + download_file(UTMOS_CKPT_URL, filepath) + self.model = BaselineLightningModule.load_from_checkpoint(filepath).eval().to(device) + + def score(self, wavs: torch.tensor) -> torch.tensor: + """ + Args: + wavs: audio waveform to be evaluated. When len(wavs) == 1 or 2, + the model processes the input as a single audio clip. The model + performs batch processing when len(wavs) == 3. + """ + if len(wavs.shape) == 1: + out_wavs = wavs.unsqueeze(0).unsqueeze(0) + elif len(wavs.shape) == 2: + out_wavs = wavs.unsqueeze(0) + elif len(wavs.shape) == 3: + out_wavs = wavs + else: + raise ValueError("Dimension of input tensor needs to be <= 3.") + bs = out_wavs.shape[0] + batch = { + "wav": out_wavs, + "domains": torch.zeros(bs, dtype=torch.int).to(self.device), + "judge_id": torch.ones(bs, dtype=torch.int).to(self.device) * 288, + } + with torch.no_grad(): + output = self.model(batch) + + return output.mean(dim=1).squeeze(1).cpu().detach() * 2 + 3 + + +def download_file(url, filename): + """ + Downloads a file from the given URL + + Args: + url (str): The URL of the file to download. + filename (str): The name to save the file as. + """ + print(f"Downloading file {filename}...") + response = requests.get(url, stream=True) + response.raise_for_status() + + total_size_in_bytes = int(response.headers.get("content-length", 0)) + progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True) + + with open(filename, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + progress_bar.update(len(chunk)) + f.write(chunk) + + progress_bar.close() + + +def load_ssl_model(ckpt_path="wav2vec_small.pt"): + filepath = os.path.join(os.path.dirname(__file__), ckpt_path) + if not os.path.exists(filepath): + download_file(WAV2VEC_URL, filepath) + SSL_OUT_DIM = 768 + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([filepath]) + ssl_model = model[0] + ssl_model.remove_pretraining_modules() + return SSL_model(ssl_model, SSL_OUT_DIM) + + +class BaselineLightningModule(pl.LightningModule): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.construct_model() + self.save_hyperparameters() + + def construct_model(self): + self.feature_extractors = nn.ModuleList( + [load_ssl_model(ckpt_path="wav2vec_small.pt"), DomainEmbedding(3, 128),] + ) + output_dim = sum([feature_extractor.get_output_dim() for feature_extractor in self.feature_extractors]) + output_layers = [LDConditioner(judge_dim=128, num_judges=3000, input_dim=output_dim)] + output_dim = output_layers[-1].get_output_dim() + output_layers.append( + Projection(hidden_dim=2048, activation=torch.nn.ReLU(), range_clipping=False, input_dim=output_dim) + ) + + self.output_layers = nn.ModuleList(output_layers) + + def forward(self, inputs): + outputs = {} + for feature_extractor in self.feature_extractors: + outputs.update(feature_extractor(inputs)) + x = outputs + for output_layer in self.output_layers: + x = output_layer(x, inputs) + return x + + +class SSL_model(nn.Module): + def __init__(self, ssl_model, ssl_out_dim) -> None: + super(SSL_model, self).__init__() + self.ssl_model, self.ssl_out_dim = ssl_model, ssl_out_dim + + def forward(self, batch): + wav = batch["wav"] + wav = wav.squeeze(1) # [batches, audio_len] + res = self.ssl_model(wav, mask=False, features_only=True) + x = res["x"] + return {"ssl-feature": x} + + def get_output_dim(self): + return self.ssl_out_dim + + +class DomainEmbedding(nn.Module): + def __init__(self, n_domains, domain_dim) -> None: + super().__init__() + self.embedding = nn.Embedding(n_domains, domain_dim) + self.output_dim = domain_dim + + def forward(self, batch): + return {"domain-feature": self.embedding(batch["domains"])} + + def get_output_dim(self): + return self.output_dim + + +class LDConditioner(nn.Module): + """ + Conditions ssl output by listener embedding + """ + + def __init__(self, input_dim, judge_dim, num_judges=None): + super().__init__() + self.input_dim = input_dim + self.judge_dim = judge_dim + self.num_judges = num_judges + assert num_judges != None + self.judge_embedding = nn.Embedding(num_judges, self.judge_dim) + # concat [self.output_layer, phoneme features] + + self.decoder_rnn = nn.LSTM( + input_size=self.input_dim + self.judge_dim, + hidden_size=512, + num_layers=1, + batch_first=True, + bidirectional=True, + ) # linear? + self.out_dim = self.decoder_rnn.hidden_size * 2 + + def get_output_dim(self): + return self.out_dim + + def forward(self, x, batch): + judge_ids = batch["judge_id"] + if "phoneme-feature" in x.keys(): + concatenated_feature = torch.cat( + (x["ssl-feature"], x["phoneme-feature"].unsqueeze(1).expand(-1, x["ssl-feature"].size(1), -1)), dim=2 + ) + else: + concatenated_feature = x["ssl-feature"] + if "domain-feature" in x.keys(): + concatenated_feature = torch.cat( + (concatenated_feature, x["domain-feature"].unsqueeze(1).expand(-1, concatenated_feature.size(1), -1),), + dim=2, + ) + if judge_ids != None: + concatenated_feature = torch.cat( + ( + concatenated_feature, + self.judge_embedding(judge_ids).unsqueeze(1).expand(-1, concatenated_feature.size(1), -1), + ), + dim=2, + ) + decoder_output, (h, c) = self.decoder_rnn(concatenated_feature) + return decoder_output + + +class Projection(nn.Module): + def __init__(self, input_dim, hidden_dim, activation, range_clipping=False): + super(Projection, self).__init__() + self.range_clipping = range_clipping + output_dim = 1 + if range_clipping: + self.proj = nn.Tanh() + + self.net = nn.Sequential( + nn.Linear(input_dim, hidden_dim), activation, nn.Dropout(0.3), nn.Linear(hidden_dim, output_dim), + ) + self.output_dim = output_dim + + def forward(self, x, batch): + output = self.net(x) + + # range clipping + if self.range_clipping: + return self.proj(output) * 2.0 + 3 + else: + return output + + def get_output_dim(self): + return self.output_dim diff --git a/metrics/__pycache__/UTMOS.cpython-310.pyc b/metrics/__pycache__/UTMOS.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..29fc32f04bdfb5144b43dc2d5e6c8f5c3d76fe47 GIT binary patch literal 7978 zcmb_h%a0t#eeUYl^z_cm4wp;Plqq>4SeC{%cV#g`5{0BCaY-lka;1n%DQ-(?_fFN$ z_Hd?qSk2eUxnCE(=17(V2Z2QZsM00sea3Xng+IS0w154q$-Acr8nF^HJ&S3QrN 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+device=torch.device('cuda:3') + +# 如果是ljspeech，需要更换路径，更换数据读取逻辑，更换stoi的采样率 + +def main(): + prepath="/home/jovyan/honor/big-disk/jishengpeng/code/languagecodec/result/infer/languagecodec/ljspeech_nq4_large_epoch24_resample" + rawpath="/home/jovyan/honor/big-disk/jishengpeng/Data/libritts/test-other" + # rawpath="/home/jovyan/honor/big-disk/jishengpeng/Data/LJSpeech-1.1/wavs" + preaudio = os.listdir(prepath) + rawaudio = [] + + UTMOS=UTMOSScore(device='cuda:3') + + # libritts + for i in range(len(preaudio)): + id1=preaudio[i].split('_')[0] + id2=preaudio[i].split('_')[1] + rawaudio.append(rawpath+"/"+id1+"/"+id2+"/"+preaudio[i]) + + # # ljspeech + # for i in range(len(preaudio)): + # rawaudio.append(rawpath+"/"+preaudio[i]) + + utmos_sumgt=0 + utmos_sumencodec=0 + pesq_sumpre=0 + f1score_sumpre=0 + stoi_sumpre=[] + f1score_filt=0 + + for i in range(len(preaudio)): + print(i) + rawwav,rawwav_sr=torchaudio.load(rawaudio[i]) + prewav,prewav_sr=torchaudio.load(prepath+"/"+preaudio[i]) + # breakpoint() + rawwav=rawwav.to(device) + prewav=prewav.to(device) + # print(rawwav.size(),prewav.size()) + # breakpoint() + rawwav_16k=torchaudio.functional.resample(rawwav, orig_freq=rawwav_sr, new_freq=16000) #测试UTMOS的时候必须重采样 + prewav_16k=torchaudio.functional.resample(prewav, orig_freq=prewav_sr, new_freq=16000) + + + # 1.UTMOS + print("****UTMOS_raw",i,UTMOS.score(rawwav_16k.unsqueeze(1))[0].item()) + print("****UTMOS_encodec",i,UTMOS.score(prewav_16k.unsqueeze(1))[0].item()) + utmos_sumgt+=UTMOS.score(rawwav_16k.unsqueeze(1))[0].item() + utmos_sumencodec+=UTMOS.score(prewav_16k.unsqueeze(1))[0].item() + + + # breakpoint() + + ## 2.PESQ + min_len=min(rawwav_16k.size()[1],prewav_16k.size()[1]) + rawwav_16k_pesq=rawwav_16k[:,:min_len].squeeze(0) + prewav_16k_pesq=prewav_16k[:,:min_len].squeeze(0) + pesq_score = pesq(16000, rawwav_16k_pesq.cpu().numpy(), prewav_16k_pesq.cpu().numpy(), "wb", on_error=1) + print("****PESQ",i,pesq_score) + pesq_sumpre+=pesq_score + # breakpoint() + + ## 3.F1-score + min_len=min(rawwav_16k.size()[1],prewav_16k.size()[1]) + rawwav_16k_f1score=rawwav_16k[:,:min_len] + prewav_16k_f1score=prewav_16k[:,:min_len] + periodicity_loss, pitch_loss, f1_score = calculate_periodicity_metrics(rawwav_16k_f1score,prewav_16k_f1score) + print("****f1",periodicity_loss, pitch_loss, f1_score,f1score_sumpre) + if(math.isnan(f1_score)): + f1score_filt+=1 + print("*****",f1score_filt) + else: + f1score_sumpre+=f1_score + # breakpoint() + + + ## 4.STOI + # # 针对重采样的ljspeech + # rawwav_24k=torchaudio.functional.resample(rawwav, orig_freq=rawwav_sr, new_freq=24000) + # min_len=min(rawwav_24k.size()[1],prewav.size()[1]) + # rawwav_stoi=rawwav_24k[:,:min_len].squeeze(0) + # prewav_stoi=prewav[:,:min_len].squeeze(0) + # tmp_stoi=stoi(rawwav_stoi.cpu(),prewav_stoi.cpu(),24000,extended=False) + # print("****stoi",tmp_stoi) + # stoi_sumpre.append(tmp_stoi) + # # breakpoint() + + # 针对libritts采样率是24k的 + min_len=min(rawwav.size()[1],prewav.size()[1]) + rawwav_stoi=rawwav[:,:min_len].squeeze(0) + prewav_stoi=prewav[:,:min_len].squeeze(0) + tmp_stoi=stoi(rawwav_stoi.cpu(),prewav_stoi.cpu(),rawwav_sr,extended=False) + print("****stoi",tmp_stoi) + stoi_sumpre.append(tmp_stoi) + + print("*************UTMOS_raw",utmos_sumgt,utmos_sumgt/len(preaudio)) + print("*************UTMOS_encodec",utmos_sumgt,utmos_sumencodec/len(preaudio)) + print("*************PESQ:",pesq_sumpre,pesq_sumpre/len(preaudio)) + print("*************F1_score:",f1score_sumpre,f1score_sumpre/(len(preaudio)-f1score_filt),f1score_filt) + print("*************STOI:",np.mean(stoi_sumpre)) + + + +if __name__=="__main__": + main() \ No newline at end of file diff --git a/metrics/periodicity.py b/metrics/periodicity.py new file mode 100644 index 0000000..728017c --- /dev/null +++ b/metrics/periodicity.py @@ -0,0 +1,105 @@ +import librosa +import numpy as np +import torch +import torchaudio +import torchcrepe +from torchcrepe.loudness import REF_DB + +SILENCE_THRESHOLD = -60 +UNVOICED_THRESHOLD = 0.21 + +""" +Periodicity metrics adapted from https://github.com/descriptinc/cargan +""" + + +def predict_pitch( + audio: torch.Tensor, silence_threshold: float = SILENCE_THRESHOLD, unvoiced_treshold: float = UNVOICED_THRESHOLD +): + """ + Predicts pitch and periodicity for the given audio. + + Args: + audio (Tensor): The audio waveform. + silence_threshold (float): The threshold for silence detection. + unvoiced_treshold (float): The threshold for unvoiced detection. + + Returns: + pitch (ndarray): The predicted pitch. + periodicity (ndarray): The predicted periodicity. + """ + # torchcrepe inference + pitch, periodicity = torchcrepe.predict( + audio, + fmin=50.0, + fmax=550, + sample_rate=torchcrepe.SAMPLE_RATE, + model="full", + return_periodicity=True, + device=audio.device, + pad=False, + ) + pitch = pitch.cpu().numpy() + periodicity = periodicity.cpu().numpy() + + # Calculate dB-scaled spectrogram and set low energy frames to unvoiced + hop_length = torchcrepe.SAMPLE_RATE // 100 # default CREPE + stft = torchaudio.functional.spectrogram( + audio, + window=torch.hann_window(torchcrepe.WINDOW_SIZE, device=audio.device), + n_fft=torchcrepe.WINDOW_SIZE, + hop_length=hop_length, + win_length=torchcrepe.WINDOW_SIZE, + power=2, + normalized=False, + pad=0, + center=False, + ) + + # Perceptual weighting + freqs = librosa.fft_frequencies(sr=torchcrepe.SAMPLE_RATE, n_fft=torchcrepe.WINDOW_SIZE) + perceptual_stft = librosa.perceptual_weighting(stft.cpu().numpy(), freqs) - REF_DB + silence = perceptual_stft.mean(axis=1) < silence_threshold + + periodicity[silence] = 0 + pitch[periodicity < unvoiced_treshold] = torchcrepe.UNVOICED + + return pitch, periodicity + + +def calculate_periodicity_metrics(y: torch.Tensor, y_hat: torch.Tensor): + """ + Calculates periodicity metrics for the predicted and true audio data. + + Args: + y (Tensor): The true audio data. + y_hat (Tensor): The predicted audio data. + + Returns: + periodicity_loss (float): The periodicity loss. + pitch_loss (float): The pitch loss. + f1 (float): The F1 score for voiced/unvoiced classification + """ + true_pitch, true_periodicity = predict_pitch(y) + pred_pitch, pred_periodicity = predict_pitch(y_hat) + + true_voiced = ~np.isnan(true_pitch) + pred_voiced = ~np.isnan(pred_pitch) + + periodicity_loss = np.sqrt(((pred_periodicity - true_periodicity) ** 2).mean(axis=1)).mean() + + # Update pitch rmse + voiced = true_voiced & pred_voiced + difference_cents = 1200 * (np.log2(true_pitch[voiced]) - np.log2(pred_pitch[voiced])) + pitch_loss = np.sqrt((difference_cents ** 2).mean()) + + # voiced/unvoiced precision and recall + true_positives = (true_voiced & pred_voiced).sum() + false_positives = (~true_voiced & pred_voiced).sum() + false_negatives = (true_voiced & ~pred_voiced).sum() + + precision = true_positives / (true_positives + false_positives) + recall = true_positives / (true_positives + false_negatives) + f1 = 2 * precision * recall / (precision + recall) + + return periodicity_loss, pitch_loss, f1 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..410d868 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,8 @@ +torch +torchaudio +numpy +scipy +einops +pyyaml +huggingface_hub +encodec==0.1.1 \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..6f72868 --- /dev/null +++ b/setup.py @@ -0,0 +1,39 @@ +import io +import os + +from setuptools import find_packages, setup + +for line in open("vocos/__init__.py"): + line = line.strip() + if "__version__" in line: + context = {} + exec(line, context) + VERSION = context["__version__"] + + +def read(*paths, **kwargs): + content = "" + with io.open( + os.path.join(os.path.dirname(__file__), *paths), encoding=kwargs.get("encoding", "utf8"), + ) as open_file: + content = open_file.read().strip() + return content + + +def read_requirements(path): + return [line.strip() for line in read(path).split("\n") if not line.startswith(('"', "#", "-", "git+"))] + + +setup( + name="vocos", + version=VERSION, + author="Hubert Siuzdak", + author_email="huberts@charactr.com", + description="Fourier-based neural vocoder for high-quality audio synthesis", + url="https://github.com/charactr-platform/vocos", + long_description=read("README.md"), + long_description_content_type="text/markdown", + packages=find_packages(), + install_requires=read_requirements("requirements.txt"), + extras_require={"train": read_requirements("requirements-train.txt")}, +) diff --git a/train.py b/train.py new file mode 100644 index 0000000..ddd12ab --- /dev/null +++ b/train.py @@ -0,0 +1,14 @@ +import os +os.environ['CUDA_LAUNCH_BLOCKING'] = '1' +from pytorch_lightning.cli import LightningCLI, ArgsType + + +def cli_main(args: ArgsType = None): + # breakpoint() + cli = LightningCLI(args=args) + # breakpoint() + cli.trainer.fit(model=cli.model, datamodule=cli.datamodule) + + +if __name__ == "__main__": + cli_main() diff --git a/vocos/__init__.py b/vocos/__init__.py new file mode 100644 index 0000000..f363437 --- /dev/null +++ b/vocos/__init__.py @@ -0,0 +1,4 @@ +from vocos.pretrained import Vocos + + +__version__ = "0.0.3" diff --git a/vocos/__pycache__/__init__.cpython-310.pyc b/vocos/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dbca3d2f6d8d3a481a2351034c27924a0c2c7b3c GIT binary patch literal 236 zcmYjKy9&ZU5WGtah!3#vCsLRbEETb{vk@%P95J`V)8sbXX+ZzT!rFgHYvnIkxo73T z&hE^@3=W4qMtDXKnA3iF@sEJSipGtJ6jQup6;C;5Do6trZdu%U(}%^pjdqx$yG7Gv zn#?wFn2s86B(;UJ&9nh!eOlX>vH|Nf8Y%D0cF6nDXDtp`fjDAw%?#ZJt=Q{gqO>~+ tR|Dh+k)r}pW~R7g1$dE*UyG1Z8-12CsjqE!QK7n&u;i5Oe8GTq!!OM#Jj4J1 literal 0 HcmV?d00001 diff --git a/vocos/__pycache__/dataset.cpython-310.pyc b/vocos/__pycache__/dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..587a708c797636dc0db0f5a3edf3b5cfe124408a GIT binary patch literal 3356 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import LightningDataModule +from torch.utils.data import Dataset, DataLoader + +import soundfile + +torch.set_num_threads(1) + + +@dataclass +class DataConfig: + filelist_path: str + sampling_rate: int + num_samples: int + batch_size: int + num_workers: int + + +class VocosDataModule(LightningDataModule): + def __init__(self, train_params: DataConfig, val_params: DataConfig): + super().__init__() + self.train_config = train_params + self.val_config = val_params + + def _get_dataloder(self, cfg: DataConfig, train: bool): + dataset = VocosDataset(cfg, train=train) + dataloader = DataLoader( + dataset, batch_size=cfg.batch_size, num_workers=cfg.num_workers, shuffle=train, pin_memory=True, + ) + return dataloader + + def train_dataloader(self) -> DataLoader: + return self._get_dataloder(self.train_config, train=True) + + def val_dataloader(self) -> DataLoader: + return self._get_dataloder(self.val_config, train=False) + + +class VocosDataset(Dataset): + def __init__(self, cfg: DataConfig, train: bool): + with open(cfg.filelist_path) as f: + self.filelist = f.read().splitlines() + self.sampling_rate = cfg.sampling_rate + self.num_samples = cfg.num_samples + self.train = train + + def __len__(self) -> int: + return len(self.filelist) + + def __getitem__(self, index: int) -> torch.Tensor: + audio_path = self.filelist[index] + # y, sr = torchaudio.load(audio_path) + y1, sr = soundfile.read(audio_path) + y = torch.tensor(y1).float().unsqueeze(0) + # if y.size(0) > 1: + # # mix to mono + # y = y.mean(dim=0, keepdim=True) + if y.ndim > 2: + # mix to mono + print("有问题哈,数据处理部分") + y = y.mean(dim=1, keepdim=False) + gain = np.random.uniform(-1, -6) if self.train else -3 + y, _ = torchaudio.sox_effects.apply_effects_tensor(y, sr, [["norm", f"{gain:.2f}"]]) + if sr != self.sampling_rate: + y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=self.sampling_rate) + if y.size(-1) < self.num_samples: + pad_length = self.num_samples - y.size(-1) + padding_tensor = y.repeat(1, 1 + pad_length // y.size(-1)) + y = torch.cat((y, padding_tensor[:, :pad_length]), dim=1) + elif self.train: + start = np.random.randint(low=0, high=y.size(-1) - self.num_samples + 1) + y = y[:, start : start + self.num_samples] + else: + # During validation, take always the first segment for determinism + y = y[:, : self.num_samples] + + return y[0] diff --git a/vocos/discriminators.py b/vocos/discriminators.py new file mode 100644 index 0000000..2f6dece --- /dev/null +++ b/vocos/discriminators.py @@ -0,0 +1,202 @@ +from typing import Tuple, List + +import torch +from torch import nn +from torch.nn import Conv2d +from torch.nn.utils import weight_norm + + +class MultiPeriodDiscriminator(nn.Module): + """ + Multi-Period Discriminator module adapted from https://github.com/jik876/hifi-gan. + Additionally, it allows incorporating conditional information with a learned embeddings table. + + Args: + periods (tuple[int]): Tuple of periods for each discriminator. + num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator. + Defaults to None. + """ + + def __init__(self, periods: Tuple[int] = (2, 3, 5, 7, 11), num_embeddings: int = None): + super().__init__() + self.discriminators = nn.ModuleList([DiscriminatorP(period=p, num_embeddings=num_embeddings) for p in periods]) + + 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 DiscriminatorP(nn.Module): + def __init__( + self, + period: int, + in_channels: int = 1, + kernel_size: int = 5, + stride: int = 3, + lrelu_slope: float = 0.1, + num_embeddings: int = None, + ): + super().__init__() + self.period = period + self.convs = nn.ModuleList( + [ + weight_norm(Conv2d(in_channels, 32, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))), + weight_norm(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))), + weight_norm(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))), + weight_norm(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))), + weight_norm(Conv2d(1024, 1024, (kernel_size, 1), (1, 1), padding=(kernel_size // 2, 0))), + ] + ) + if num_embeddings is not None: + self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=1024) + torch.nn.init.zeros_(self.emb.weight) + + self.conv_post = weight_norm(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + self.lrelu_slope = lrelu_slope + + def forward( + self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + x = x.unsqueeze(1) + fmap = [] + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = torch.nn.functional.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for i, l in enumerate(self.convs): + x = l(x) + x = torch.nn.functional.leaky_relu(x, self.lrelu_slope) + if i > 0: + fmap.append(x) + 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 + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiResolutionDiscriminator(nn.Module): + def __init__( + self, + resolutions: Tuple[Tuple[int, int, int]] = ((1024, 256, 1024), (2048, 512, 2048), (512, 128, 512)), + num_embeddings: int = None, + ): + """ + Multi-Resolution Discriminator module adapted from https://github.com/mindslab-ai/univnet. + Additionally, it allows incorporating conditional information with a learned embeddings table. + + Args: + resolutions (tuple[tuple[int, int, int]]): Tuple of resolutions for each discriminator. + Each resolution should be a tuple of (n_fft, hop_length, win_length). + num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator. + Defaults to None. + """ + super().__init__() + self.discriminators = nn.ModuleList( + [DiscriminatorR(resolution=r, num_embeddings=num_embeddings) for r in resolutions] + ) + + 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, + resolution: Tuple[int, int, int], + channels: int = 64, + in_channels: int = 1, + num_embeddings: int = None, + lrelu_slope: float = 0.1, + ): + super().__init__() + self.resolution = resolution + self.in_channels = in_channels + self.lrelu_slope = lrelu_slope + self.convs = nn.ModuleList( + [ + weight_norm(nn.Conv2d(in_channels, channels, kernel_size=(7, 5), stride=(2, 2), padding=(3, 2))), + weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 1), padding=(2, 1))), + weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 2), padding=(2, 1))), + weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 1), padding=1)), + weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 2), padding=1)), + ] + ) + 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), padding=(1, 1))) + + def forward( + self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + fmap = [] + x = self.spectrogram(x) + x = x.unsqueeze(1) + for l in self.convs: + x = l(x) + x = torch.nn.functional.leaky_relu(x, self.lrelu_slope) + fmap.append(x) + 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 + x = torch.flatten(x, 1, -1) + + return x, fmap + + def spectrogram(self, x: torch.Tensor) -> torch.Tensor: + n_fft, hop_length, win_length = self.resolution + magnitude_spectrogram = torch.stft( + x, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=None, # interestingly rectangular window kind of works here + center=True, + return_complex=True, + ).abs() + + return magnitude_spectrogram diff --git a/vocos/experiment.py b/vocos/experiment.py new file mode 100644 index 0000000..41e80dd --- /dev/null +++ b/vocos/experiment.py @@ -0,0 +1,449 @@ +import math + +import numpy as np +import pytorch_lightning as pl +import torch +import torchaudio +import transformers +import yaml + +from vocos.discriminators import MultiPeriodDiscriminator, MultiResolutionDiscriminator +from vocos.feature_extractors import FeatureExtractor +from vocos.heads import FourierHead +from vocos.helpers import plot_spectrogram_to_numpy +from vocos.loss import DiscriminatorLoss, GeneratorLoss, FeatureMatchingLoss, MelSpecReconstructionLoss +from vocos.models import Backbone +from vocos.modules import safe_log +from vocos.pretrained_model import instantiate_class + + +class VocosExp(pl.LightningModule): + # noinspection PyUnusedLocal + def __init__( + self, + feature_extractor: FeatureExtractor, + backbone: Backbone, + head: FourierHead, + resume_config: str, + resume_model: str, + sample_rate: int = 24000, + initial_learning_rate: float = 2e-4, + num_warmup_steps: int = 0, + mel_loss_coeff: float = 45, + mrd_loss_coeff: float = 1.0, + pretrain_mel_steps: int = 0, + decay_mel_coeff: bool = False, + evaluate_utmos: bool = False, + evaluate_pesq: bool = False, + evaluate_periodicty: bool = False, + resume: bool = False, + ): + """ + Args: + feature_extractor (FeatureExtractor): An instance of FeatureExtractor to extract features from audio signals. + backbone (Backbone): An instance of Backbone model. + head (FourierHead): An instance of Fourier head to generate spectral coefficients and reconstruct a waveform. + sample_rate (int): Sampling rate of the audio signals. + initial_learning_rate (float): Initial learning rate for the optimizer. + num_warmup_steps (int): Number of steps for the warmup phase of learning rate scheduler. Default is 0. + mel_loss_coeff (float, optional): Coefficient for Mel-spectrogram loss in the loss function. Default is 45. + mrd_loss_coeff (float, optional): Coefficient for Multi Resolution Discriminator loss. Default is 1.0. + pretrain_mel_steps (int, optional): Number of steps to pre-train the model without the GAN objective. Default is 0. + decay_mel_coeff (bool, optional): If True, the Mel-spectrogram loss coefficient is decayed during training. Default is False. + evaluate_utmos (bool, optional): If True, UTMOS scores are computed for each validation run. + evaluate_pesq (bool, optional): If True, PESQ scores are computed for each validation run. + evaluate_periodicty (bool, optional): If True, periodicity scores are computed for each validation run. + """ + super().__init__() + self.save_hyperparameters(ignore=["feature_extractor", "backbone", "head"]) + + self.feature_extractor = feature_extractor + self.backbone = backbone + self.head = head + + self.resume_config = resume_config + self.resume_model = resume_model + self.resume = resume + + self.multiperioddisc = MultiPeriodDiscriminator() + self.multiresddisc = MultiResolutionDiscriminator() + + self.disc_loss = DiscriminatorLoss() + self.gen_loss = GeneratorLoss() + self.feat_matching_loss = FeatureMatchingLoss() + self.melspec_loss = MelSpecReconstructionLoss(sample_rate=sample_rate) + + self.train_discriminator = False + self.base_mel_coeff = self.mel_loss_coeff = mel_loss_coeff + + def configure_optimizers(self): + disc_params = [ + {"params": self.multiperioddisc.parameters()}, + {"params": self.multiresddisc.parameters()}, + ] + gen_params = [ + {"params": self.feature_extractor.parameters()}, + {"params": self.backbone.parameters()}, + {"params": self.head.parameters()}, + ] + + opt_disc = torch.optim.AdamW(disc_params, lr=self.hparams.initial_learning_rate) + opt_gen = torch.optim.AdamW(gen_params, lr=self.hparams.initial_learning_rate) + + max_steps = self.trainer.max_steps // 2 # Max steps per optimizer + scheduler_disc = transformers.get_cosine_schedule_with_warmup( + opt_disc, num_warmup_steps=self.hparams.num_warmup_steps, num_training_steps=max_steps, + ) + scheduler_gen = transformers.get_cosine_schedule_with_warmup( + opt_gen, num_warmup_steps=self.hparams.num_warmup_steps, num_training_steps=max_steps, + ) + + return ( + [opt_disc, opt_gen], + [{"scheduler": scheduler_disc, "interval": "step"}, {"scheduler": scheduler_gen, "interval": "step"}], + ) + + def forward(self, audio_input, **kwargs): + features, _, commit_loss = self.feature_extractor(audio_input, **kwargs) + # print('1111', self.feature_extractor.state_dict()['encodec.decoder.model.3.convtr.convtr.weight_g']) + x = self.backbone(features, **kwargs) + audio_output = self.head(x) + return audio_output, commit_loss + + def training_step(self, batch, batch_idx, optimizer_idx, **kwargs): + audio_input = batch + + # train discriminator + if optimizer_idx == 0 and self.train_discriminator: + with torch.no_grad(): + audio_hat, _ = self(audio_input, **kwargs) + real_score_mp, gen_score_mp, _, _ = self.multiperioddisc(y=audio_input, y_hat=audio_hat, **kwargs,) + real_score_mrd, gen_score_mrd, _, _ = self.multiresddisc(y=audio_input, y_hat=audio_hat, **kwargs,) + loss_mp, loss_mp_real, _ = self.disc_loss( + disc_real_outputs=real_score_mp, disc_generated_outputs=gen_score_mp + ) + loss_mrd, loss_mrd_real, _ = self.disc_loss( + disc_real_outputs=real_score_mrd, disc_generated_outputs=gen_score_mrd + ) + loss_mp /= len(loss_mp_real) + loss_mrd /= len(loss_mrd_real) + loss = loss_mp + self.hparams.mrd_loss_coeff * loss_mrd + + self.log("discriminator/total", loss, prog_bar=True) + self.log("discriminator/multi_period_loss", loss_mp) + self.log("discriminator/multi_res_loss", loss_mrd) + return loss + + # train generator + if optimizer_idx == 1: + audio_hat, commit_loss = self(audio_input, **kwargs) + if self.train_discriminator: + _, gen_score_mp, fmap_rs_mp, fmap_gs_mp = self.multiperioddisc( + y=audio_input, y_hat=audio_hat, **kwargs, + ) + _, gen_score_mrd, fmap_rs_mrd, fmap_gs_mrd = self.multiresddisc( + y=audio_input, y_hat=audio_hat, **kwargs, + ) + loss_gen_mp, list_loss_gen_mp = self.gen_loss(disc_outputs=gen_score_mp) + loss_gen_mrd, list_loss_gen_mrd = self.gen_loss(disc_outputs=gen_score_mrd) + loss_gen_mp = loss_gen_mp / len(list_loss_gen_mp) + loss_gen_mrd = loss_gen_mrd / len(list_loss_gen_mrd) + loss_fm_mp = self.feat_matching_loss(fmap_r=fmap_rs_mp, fmap_g=fmap_gs_mp) / len(fmap_rs_mp) + loss_fm_mrd = self.feat_matching_loss(fmap_r=fmap_rs_mrd, fmap_g=fmap_gs_mrd) / len(fmap_rs_mrd) + + self.log("generator/multi_period_loss", loss_gen_mp) + self.log("generator/multi_res_loss", loss_gen_mrd) + self.log("generator/feature_matching_mp", loss_fm_mp) + self.log("generator/feature_matching_mrd", loss_fm_mrd) + else: + loss_gen_mp = loss_gen_mrd = loss_fm_mp = loss_fm_mrd = 0 + + mel_loss = self.melspec_loss(audio_hat, audio_input) + loss = ( + loss_gen_mp + + self.hparams.mrd_loss_coeff * loss_gen_mrd + + loss_fm_mp + + self.hparams.mrd_loss_coeff * loss_fm_mrd + + self.mel_loss_coeff * mel_loss + + 1000 * commit_loss + ) + + self.log("generator/total_loss", loss, prog_bar=True) + self.log("mel_loss_coeff", self.mel_loss_coeff) + self.log("generator/mel_loss", mel_loss) + self.log("commit_loss", commit_loss) + + if self.global_step % 1000 == 0 and self.global_rank == 0: + self.logger.experiment.add_audio( + "train/audio_in", audio_input[0].data.cpu(), self.global_step, self.hparams.sample_rate + ) + self.logger.experiment.add_audio( + "train/audio_pred", audio_hat[0].data.cpu(), self.global_step, self.hparams.sample_rate + ) + with torch.no_grad(): + mel = safe_log(self.melspec_loss.mel_spec(audio_input[0])) + mel_hat = safe_log(self.melspec_loss.mel_spec(audio_hat[0])) + self.logger.experiment.add_image( + "train/mel_target", + plot_spectrogram_to_numpy(mel.data.cpu().numpy()), + self.global_step, + dataformats="HWC", + ) + self.logger.experiment.add_image( + "train/mel_pred", + plot_spectrogram_to_numpy(mel_hat.data.cpu().numpy()), + self.global_step, + dataformats="HWC", + ) + + return loss + + def on_validation_epoch_start(self): + if self.hparams.evaluate_utmos: + from metrics.UTMOS import UTMOSScore + + if not hasattr(self, "utmos_model"): + self.utmos_model = UTMOSScore(device=self.device) + + def validation_step(self, batch, batch_idx, **kwargs): + audio_input = batch + audio_hat, commit_loss = self(audio_input, **kwargs) + + audio_16_khz = torchaudio.functional.resample(audio_input, orig_freq=self.hparams.sample_rate, new_freq=16000) + audio_hat_16khz = torchaudio.functional.resample(audio_hat, orig_freq=self.hparams.sample_rate, new_freq=16000) + + if self.hparams.evaluate_periodicty: + from metrics.periodicity import calculate_periodicity_metrics + + periodicity_loss, pitch_loss, f1_score = calculate_periodicity_metrics(audio_16_khz, audio_hat_16khz) + else: + periodicity_loss = pitch_loss = f1_score = 0 + + if self.hparams.evaluate_utmos: + utmos_score = self.utmos_model.score(audio_hat_16khz.unsqueeze(1)).mean() + else: + utmos_score = torch.zeros(1, device=self.device) + + if self.hparams.evaluate_pesq: + from pesq import pesq + + pesq_score = 0 + for ref, deg in zip(audio_16_khz.cpu().numpy(), audio_hat_16khz.cpu().numpy()): + pesq_score += pesq(16000, ref, deg, "wb", on_error=1) + pesq_score /= len(audio_16_khz) + pesq_score = torch.tensor(pesq_score) + else: + pesq_score = torch.zeros(1, device=self.device) + + mel_loss = self.melspec_loss(audio_hat.unsqueeze(1), audio_input.unsqueeze(1)) + total_loss = mel_loss + (5 - utmos_score) + (5 - pesq_score) + 1000 * commit_loss + + return { + "val_loss": total_loss, + "mel_loss": mel_loss, + "utmos_score": utmos_score, + "pesq_score": pesq_score, + "periodicity_loss": periodicity_loss, + "pitch_loss": pitch_loss, + "f1_score": f1_score, + "audio_input": audio_input[0], + "audio_pred": audio_hat[0], + } + + def validation_epoch_end(self, outputs): + if self.global_rank == 0: + *_, audio_in, audio_pred = outputs[0].values() + self.logger.experiment.add_audio( + "val_in", audio_in.data.cpu().numpy(), self.global_step, self.hparams.sample_rate + ) + self.logger.experiment.add_audio( + "val_pred", audio_pred.data.cpu().numpy(), self.global_step, self.hparams.sample_rate + ) + mel_target = safe_log(self.melspec_loss.mel_spec(audio_in)) + mel_hat = safe_log(self.melspec_loss.mel_spec(audio_pred)) + self.logger.experiment.add_image( + "val_mel_target", + plot_spectrogram_to_numpy(mel_target.data.cpu().numpy()), + self.global_step, + dataformats="HWC", + ) + self.logger.experiment.add_image( + "val_mel_hat", + plot_spectrogram_to_numpy(mel_hat.data.cpu().numpy()), + self.global_step, + dataformats="HWC", + ) + avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean() + mel_loss = torch.stack([x["mel_loss"] for x in outputs]).mean() + utmos_score = torch.stack([x["utmos_score"] for x in outputs]).mean() + pesq_score = torch.stack([x["pesq_score"] for x in outputs]).mean() + periodicity_loss = np.array([x["periodicity_loss"] for x in outputs]).mean() + pitch_loss = np.array([x["pitch_loss"] for x in outputs]).mean() + f1_score = np.array([x["f1_score"] for x in outputs]).mean() + + self.log("val_loss", avg_loss, sync_dist=True) + self.log("val/mel_loss", mel_loss, sync_dist=True) + self.log("val/utmos_score", utmos_score, sync_dist=True) + self.log("val/pesq_score", pesq_score, sync_dist=True) + self.log("val/periodicity_loss", periodicity_loss, sync_dist=True) + self.log("val/pitch_loss", pitch_loss, sync_dist=True) + self.log("val/f1_score", f1_score, sync_dist=True) + + @property + def global_step(self): + """ + Override global_step so that it returns the total number of batches processed + """ + return self.trainer.fit_loop.epoch_loop.total_batch_idx + + def on_train_batch_start(self, *args): + if self.global_step >= self.hparams.pretrain_mel_steps: + self.train_discriminator = True + else: + self.train_discriminator = False + + def on_train_batch_end(self, *args): + def mel_loss_coeff_decay(current_step, num_cycles=0.5): + max_steps = self.trainer.max_steps // 2 + if current_step < self.hparams.num_warmup_steps: + return 1.0 + progress = float(current_step - self.hparams.num_warmup_steps) / float( + max(1, max_steps - self.hparams.num_warmup_steps) + ) + return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))) + + if self.hparams.decay_mel_coeff: + self.mel_loss_coeff = self.base_mel_coeff * mel_loss_coeff_decay(self.global_step + 1) + + +class VocosEncodecExp(VocosExp): + """ + VocosEncodecExp is a subclass of VocosExp that overrides the parent experiment to function as a conditional GAN. + It manages an additional `bandwidth_id` attribute, which denotes a learnable embedding corresponding to + a specific bandwidth value of EnCodec. During training, a random bandwidth_id is generated for each step, + while during validation, a fixed bandwidth_id is used. + """ + + def __init__( + self, + feature_extractor: FeatureExtractor, + backbone: Backbone, + head: FourierHead, + resume_config: str, + resume_model: str, + sample_rate: int = 24000, + initial_learning_rate: float = 2e-4, + num_warmup_steps: int = 0, + mel_loss_coeff: float = 45, + mrd_loss_coeff: float = 1.0, + pretrain_mel_steps: int = 0, + decay_mel_coeff: bool = False, + evaluate_utmos: bool = False, + evaluate_pesq: bool = False, + evaluate_periodicty: bool = False, + resume: bool = False, + ): + super().__init__( + feature_extractor, + backbone, + head, + resume_config, + resume_model, + sample_rate, + initial_learning_rate, + num_warmup_steps, + mel_loss_coeff, + mrd_loss_coeff, + pretrain_mel_steps, + decay_mel_coeff, + evaluate_utmos, + evaluate_pesq, + evaluate_periodicty, + resume + ) + # Override with conditional discriminators + # VocosExp.__init__(self, feature_extractor, backbone, head, resume_config, resume_model) + # if self.resume: + # VocosExp.load_from_checkpoint(self.resume_model) + self.multiperioddisc = MultiPeriodDiscriminator(num_embeddings=len(self.feature_extractor.bandwidths)) + self.multiresddisc = MultiResolutionDiscriminator(num_embeddings=len(self.feature_extractor.bandwidths)) + if self.resume: + print('加载预训练模型:', self.resume_model) + # with open(self.resume_config, "r") as f: + # config = yaml.safe_load(f) + # feature_extractor = instantiate_class(args=(), init=config['model']['init_args']["feature_extractor"]) + # backbone = instantiate_class(args=(), init=config['model']['init_args']["backbone"]) + # head = instantiate_class(args=(), init=config['model']['init_args']["head"]) + + # 不加载量化器部分权重 + state_dict_raw = torch.load(self.resume_model, map_location=self.device)['state_dict'] + state_dict_fa_qa = dict() + state_dict_fa_en = dict() + state_dict_fa_de = dict() + state_dict_bb = dict() + state_dict_hd = dict() + state_dict_mp = dict() + state_dict_mr = dict() + for k, v in state_dict_raw.items(): + # breakpoint() + if k.startswith('feature_extractor.encodec.quantizer'): + # breakpoint() + # print("*****",k) + ss = k[46:48] + if ss[-1] == '.': + num = int(ss[0]) + # print("num,k",num,k[36:]) + if num <= 7: + state_dict_fa_qa[k[36:]] = v + if k.startswith('feature_extractor.encodec.encoder'): + state_dict_fa_en[k[34:]] = v + if k.startswith('feature_extractor.encodec.decoder'): + state_dict_fa_de[k[34:]] = v + if k.startswith('backbone.'): + state_dict_bb[k[9:]] = v + if k.startswith('head.'): + state_dict_hd[k[5:]] = v + if k.startswith('multiperioddisc.'): + state_dict_mp[k[16:]] = v + if k.startswith('multiresddisc.'): + state_dict_mr[k[14:]] = v + # breakpoint() + feature_extractor.encodec.quantizer.load_state_dict(state_dict_fa_qa, strict=True) + feature_extractor.encodec.encoder.load_state_dict(state_dict_fa_en, strict=True) + feature_extractor.encodec.decoder.load_state_dict(state_dict_fa_de, strict=True) + backbone.load_state_dict(state_dict_bb, strict=True) + head.load_state_dict(state_dict_hd, strict=True) + self.feature_extractor = feature_extractor.to(self.device) + self.backbone = backbone.to(self.device) + self.head = head.to(self.device) + self.multiperioddisc.load_state_dict(state_dict_mp, strict=True) + self.multiresddisc.load_state_dict(state_dict_mr, strict=True) + + def training_step(self, *args): + # print('-------------------train--------------------') + # if self.global_rank == 0 and self.resume: + # config_path = self.resume_config + # model_path = self.resume_model + # self.pretrained_load(config_path, model_path) + # print('加载预训练模型:', model_path) + bandwidth_id = torch.randint(low=0, high=len(self.feature_extractor.bandwidths), size=(1,), device=self.device,) + output = super().training_step(*args, bandwidth_id=bandwidth_id) + return output + + def validation_step(self, *args): + # print('-------------------valid--------------------') + bandwidth_id = torch.tensor([0], device=self.device) + output = super().validation_step(*args, bandwidth_id=bandwidth_id) + return output + + def validation_epoch_end(self, outputs): + if self.global_rank == 0: + *_, audio_in, _ = outputs[0].values() + # Resynthesis with encodec for reference + self.feature_extractor.encodec.set_target_bandwidth(self.feature_extractor.bandwidths[0]) + encodec_audio = self.feature_extractor.encodec(audio_in[None, None, :]) + self.logger.experiment.add_audio( + "encodec", encodec_audio[0, 0].data.cpu().numpy(), self.global_step, self.hparams.sample_rate, + ) + + super().validation_epoch_end(outputs) diff --git a/vocos/feature_extractors.py b/vocos/feature_extractors.py new file mode 100644 index 0000000..67b83cb --- /dev/null +++ b/vocos/feature_extractors.py @@ -0,0 +1,117 @@ +from typing import List + +import torch +import torchaudio +# from encodec import EncodecModel +from torch import nn +import math +from vocos.modules import safe_log +from encodec.modules import SEANetEncoder, SEANetDecoder +from encodec import EncodecModel +from encodec.quantization import ResidualVectorQuantizer + + +class FeatureExtractor(nn.Module): + """Base class for feature extractors.""" + + def forward(self, audio: torch.Tensor, **kwargs) -> torch.Tensor: + """ + Extract features from the given audio. + + Args: + audio (Tensor): Input audio waveform. + + Returns: + Tensor: Extracted features of shape (B, C, L), where B is the batch size, + C denotes output features, and L is the sequence length. + """ + raise NotImplementedError("Subclasses must implement the forward method.") + + +class MelSpectrogramFeatures(FeatureExtractor): + def __init__(self, sample_rate=24000, n_fft=1024, hop_length=256, n_mels=100, padding="center"): + super().__init__() + if padding not in ["center", "same"]: + raise ValueError("Padding must be 'center' or 'same'.") + self.padding = padding + self.mel_spec = torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, + n_fft=n_fft, + hop_length=hop_length, + n_mels=n_mels, + center=padding == "center", + power=1, + ) + + def forward(self, audio, **kwargs): + if self.padding == "same": + pad = self.mel_spec.win_length - self.mel_spec.hop_length + audio = torch.nn.functional.pad(audio, (pad // 2, pad // 2), mode="reflect") + mel = self.mel_spec(audio) + features = safe_log(mel) + return features + + +class EncodecFeatures(FeatureExtractor): + def __init__( + self, + encodec_model: str = "encodec_24khz", + bandwidths: List[float] = [1.5, 3.0, 6.0, 12.0], + train_codebooks: bool = False, + ): + super().__init__() + self.frame_rate = int(24000/320) + n_q = int(bandwidths[-1]*1000/(math.log2(2048) * self.frame_rate)) + encoder = SEANetEncoder(causal=False, n_residual_layers=1, norm='weight_norm', pad_mode='reflect', lstm=2, + dimension=128, channels=1, n_filters=32, ratios=[8, 5, 4, 2], activation='ELU', + kernel_size=7, residual_kernel_size=3, last_kernel_size=7, dilation_base=2, + true_skip=False, compress=2) + decoder = SEANetDecoder(causal=False, n_residual_layers=1, norm='weight_norm', pad_mode='reflect', lstm=2, + dimension=128, channels=1, n_filters=32, ratios=[8, 5, 4, 2], activation='ELU', + kernel_size=7, residual_kernel_size=3, last_kernel_size=7, dilation_base=2, + true_skip=False, compress=2) + quantizer = ResidualVectorQuantizer(dimension=128, n_q=n_q, bins=2048, kmeans_iters=50, + decay=0.99, kmeans_init=True) + if encodec_model == "encodec_24khz": + self.encodec = EncodecModel(encoder=encoder, decoder=decoder, quantizer=quantizer, + target_bandwidths=bandwidths, sample_rate=24000, channels=1) + else: + raise ValueError( + f"Unsupported encodec_model: {encodec_model}. Supported options are 'encodec_24khz'." + ) + for param in self.encodec.parameters(): + param.requires_grad = True + self.num_q = n_q + # codebook_weights = torch.cat([vq.codebook for vq in self.encodec.quantizer.vq.layers[: self.num_q]], dim=0) + # self.codebook_weights = torch.nn.Parameter(codebook_weights, requires_grad=train_codebooks) + self.bandwidths = bandwidths + + # @torch.no_grad() + # def get_encodec_codes(self, audio): + # audio = audio.unsqueeze(1) + # emb = self.encodec.encoder(audio) + # codes = self.encodec.quantizer.encode(emb, self.encodec.frame_rate, self.encodec.bandwidth) + # return codes + + def forward(self, audio: torch.Tensor, bandwidth_id: torch.Tensor): + if self.training: + self.encodec.train() + + audio = audio.unsqueeze(1) # audio(16,24000) + emb = self.encodec.encoder(audio) + q_res = self.encodec.quantizer(emb, self.frame_rate, bandwidth=self.bandwidths[bandwidth_id]) + quantized = q_res.quantized + codes = q_res.codes + commit_loss = q_res.penalty # codes(8,16,75),features(16,128,75) + + return quantized, codes, commit_loss + + # codes = self.get_encodec_codes(audio) + # # Instead of summing in the loop, it stores subsequent VQ dictionaries in a single `self.codebook_weights` + # # with offsets given by the number of bins, and finally summed in a vectorized operation. + # offsets = torch.arange( + # 0, self.encodec.quantizer.bins * len(codes), self.encodec.quantizer.bins, device=audio.device + # ) + # embeddings_idxs = codes + offsets.view(-1, 1, 1) + # features = torch.nn.functional.embedding(embeddings_idxs, self.codebook_weights).sum(dim=0) + # return features.transpose(1, 2) diff --git a/vocos/heads.py b/vocos/heads.py new file mode 100644 index 0000000..21863f1 --- /dev/null +++ b/vocos/heads.py @@ -0,0 +1,157 @@ +import torch +from torch import nn +from torchaudio.functional.functional import _hz_to_mel, _mel_to_hz + +from vocos.spectral_ops import IMDCT, ISTFT +from vocos.modules import symexp + + +class FourierHead(nn.Module): + """Base class for inverse fourier modules.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x (Tensor): Input tensor of shape (B, L, H), where B is the batch size, + L is the sequence length, and H denotes the model dimension. + + Returns: + Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal. + """ + raise NotImplementedError("Subclasses must implement the forward method.") + + +class ISTFTHead(FourierHead): + """ + ISTFT Head module for predicting STFT complex coefficients. + + Args: + dim (int): Hidden dimension of the model. + n_fft (int): Size of Fourier transform. + hop_length (int): The distance between neighboring sliding window frames, which should align with + the resolution of the input features. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + """ + + def __init__(self, dim: int, n_fft: int, hop_length: int, padding: str = "same"): + super().__init__() + out_dim = n_fft + 2 + self.out = torch.nn.Linear(dim, out_dim) + self.istft = ISTFT(n_fft=n_fft, hop_length=hop_length, win_length=n_fft, padding=padding) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the ISTFTHead module. + + Args: + x (Tensor): Input tensor of shape (B, L, H), where B is the batch size, + L is the sequence length, and H denotes the model dimension. + + Returns: + Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal. + """ + x = self.out(x).transpose(1, 2) + mag, p = x.chunk(2, dim=1) + mag = torch.exp(mag) + mag = torch.clip(mag, max=1e2) # safeguard to prevent excessively large magnitudes + # wrapping happens here. These two lines produce real and imaginary value + x = torch.cos(p) + y = torch.sin(p) + # recalculating phase here does not produce anything new + # only costs time + # phase = torch.atan2(y, x) + # S = mag * torch.exp(phase * 1j) + # better directly produce the complex value + S = mag * (x + 1j * y) + audio = self.istft(S) + return audio + + +class IMDCTSymExpHead(FourierHead): + """ + IMDCT Head module for predicting MDCT coefficients with symmetric exponential function + + Args: + dim (int): Hidden dimension of the model. + mdct_frame_len (int): Length of the MDCT frame. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + sample_rate (int, optional): The sample rate of the audio. If provided, the last layer will be initialized + based on perceptual scaling. Defaults to None. + clip_audio (bool, optional): Whether to clip the audio output within the range of [-1.0, 1.0]. Defaults to False. + """ + + def __init__( + self, dim: int, mdct_frame_len: int, padding: str = "same", sample_rate: int = None, clip_audio: bool = False, + ): + super().__init__() + out_dim = mdct_frame_len // 2 + self.out = nn.Linear(dim, out_dim) + self.imdct = IMDCT(frame_len=mdct_frame_len, padding=padding) + self.clip_audio = clip_audio + + if sample_rate is not None: + # optionally init the last layer following mel-scale + m_max = _hz_to_mel(sample_rate // 2) + m_pts = torch.linspace(0, m_max, out_dim) + f_pts = _mel_to_hz(m_pts) + scale = 1 - (f_pts / f_pts.max()) + + with torch.no_grad(): + self.out.weight.mul_(scale.view(-1, 1)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the IMDCTSymExpHead module. + + Args: + x (Tensor): Input tensor of shape (B, L, H), where B is the batch size, + L is the sequence length, and H denotes the model dimension. + + Returns: + Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal. + """ + x = self.out(x) + x = symexp(x) + x = torch.clip(x, min=-1e2, max=1e2) # safeguard to prevent excessively large magnitudes + audio = self.imdct(x) + if self.clip_audio: + audio = torch.clip(x, min=-1.0, max=1.0) + + return audio + + +class IMDCTCosHead(FourierHead): + """ + IMDCT Head module for predicting MDCT coefficients with parametrizing MDCT = exp(m) · cos(p) + + Args: + dim (int): Hidden dimension of the model. + mdct_frame_len (int): Length of the MDCT frame. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + clip_audio (bool, optional): Whether to clip the audio output within the range of [-1.0, 1.0]. Defaults to False. + """ + + def __init__(self, dim: int, mdct_frame_len: int, padding: str = "same", clip_audio: bool = False): + super().__init__() + self.clip_audio = clip_audio + self.out = nn.Linear(dim, mdct_frame_len) + self.imdct = IMDCT(frame_len=mdct_frame_len, padding=padding) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the IMDCTCosHead module. + + Args: + x (Tensor): Input tensor of shape (B, L, H), where B is the batch size, + L is the sequence length, and H denotes the model dimension. + + Returns: + Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal. + """ + x = self.out(x) + m, p = x.chunk(2, dim=2) + m = torch.exp(m).clip(max=1e2) # safeguard to prevent excessively large magnitudes + audio = self.imdct(m * torch.cos(p)) + if self.clip_audio: + audio = torch.clip(x, min=-1.0, max=1.0) + return audio diff --git a/vocos/helpers.py b/vocos/helpers.py new file mode 100644 index 0000000..3d30301 --- /dev/null +++ b/vocos/helpers.py @@ -0,0 +1,71 @@ +import matplotlib +import numpy as np +import torch +from matplotlib import pyplot as plt +from pytorch_lightning import Callback + +matplotlib.use("Agg") + + +def save_figure_to_numpy(fig: plt.Figure) -> np.ndarray: + """ + Save a matplotlib figure to a numpy array. + + Args: + fig (Figure): Matplotlib figure object. + + Returns: + ndarray: Numpy array representing the figure. + """ + 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_spectrogram_to_numpy(spectrogram: np.ndarray) -> np.ndarray: + """ + Plot a spectrogram and convert it to a numpy array. + + Args: + spectrogram (ndarray): Spectrogram data. + + Returns: + ndarray: Numpy array representing the plotted spectrogram. + """ + spectrogram = spectrogram.astype(np.float32) + 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.tight_layout() + + fig.canvas.draw() + data = save_figure_to_numpy(fig) + plt.close() + return data + + +class GradNormCallback(Callback): + """ + Callback to log the gradient norm. + """ + + def on_after_backward(self, trainer, model): + model.log("grad_norm", gradient_norm(model)) + + +def gradient_norm(model: torch.nn.Module, norm_type: float = 2.0) -> torch.Tensor: + """ + Compute the gradient norm. + + Args: + model (Module): PyTorch model. + norm_type (float, optional): Type of the norm. Defaults to 2.0. + + Returns: + Tensor: Gradient norm. + """ + grads = [p.grad for p in model.parameters() if p.grad is not None] + total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type) for g in grads]), norm_type) + return total_norm diff --git a/vocos/loss.py b/vocos/loss.py new file mode 100644 index 0000000..e6b0ed5 --- /dev/null +++ b/vocos/loss.py @@ -0,0 +1,114 @@ +from typing import List, Tuple + +import torch +import torchaudio +from torch import nn + +from vocos.modules import safe_log + + +class MelSpecReconstructionLoss(nn.Module): + """ + L1 distance between the mel-scaled magnitude spectrograms of the ground truth sample and the generated sample + """ + + def __init__( + self, sample_rate: int = 24000, n_fft: int = 1024, hop_length: int = 256, n_mels: int = 100, + ): + super().__init__() + self.mel_spec = torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels, center=True, power=1, + ) + + def forward(self, y_hat, y) -> torch.Tensor: + """ + Args: + y_hat (Tensor): Predicted audio waveform. + y (Tensor): Ground truth audio waveform. + + Returns: + Tensor: L1 loss between the mel-scaled magnitude spectrograms. + """ + mel_hat = safe_log(self.mel_spec(y_hat)) + mel = safe_log(self.mel_spec(y)) + + loss = torch.nn.functional.l1_loss(mel, mel_hat) + + return loss + + +class GeneratorLoss(nn.Module): + """ + Generator Loss module. Calculates the loss for the generator based on discriminator outputs. + """ + + def forward(self, disc_outputs: List[torch.Tensor]) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Args: + disc_outputs (List[Tensor]): List of discriminator outputs. + + Returns: + Tuple[Tensor, List[Tensor]]: Tuple containing the total loss and a list of loss values from + the sub-discriminators + """ + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean(torch.clamp(1 - dg, min=0)) + gen_losses.append(l) + loss += l + + return loss, gen_losses + + +class DiscriminatorLoss(nn.Module): + """ + Discriminator Loss module. Calculates the loss for the discriminator based on real and generated outputs. + """ + + def forward( + self, disc_real_outputs: List[torch.Tensor], disc_generated_outputs: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor], List[torch.Tensor]]: + """ + Args: + disc_real_outputs (List[Tensor]): List of discriminator outputs for real samples. + disc_generated_outputs (List[Tensor]): List of discriminator outputs for generated samples. + + Returns: + Tuple[Tensor, List[Tensor], List[Tensor]]: A tuple containing the total loss, a list of loss values from + the sub-discriminators for real outputs, and a list of + loss values for generated outputs. + """ + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean(torch.clamp(1 - dr, min=0)) + g_loss = torch.mean(torch.clamp(1 + dg, min=0)) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +class FeatureMatchingLoss(nn.Module): + """ + Feature Matching Loss module. Calculates the feature matching loss between feature maps of the sub-discriminators. + """ + + def forward(self, fmap_r: List[List[torch.Tensor]], fmap_g: List[List[torch.Tensor]]) -> torch.Tensor: + """ + Args: + fmap_r (List[List[Tensor]]): List of feature maps from real samples. + fmap_g (List[List[Tensor]]): List of feature maps from generated samples. + + Returns: + Tensor: The calculated feature matching loss. + """ + 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 diff --git a/vocos/models.py b/vocos/models.py new file mode 100644 index 0000000..886a88a --- /dev/null +++ b/vocos/models.py @@ -0,0 +1,117 @@ +from typing import Optional + +import torch +from torch import nn +from torch.nn.utils import weight_norm + +from vocos.modules import ConvNeXtBlock, ResBlock1, AdaLayerNorm + + +class Backbone(nn.Module): + """Base class for the generator's backbone. It preserves the same temporal resolution across all layers.""" + + def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor: + """ + Args: + x (Tensor): Input tensor of shape (B, C, L), where B is the batch size, + C denotes output features, and L is the sequence length. + + Returns: + Tensor: Output of shape (B, L, H), where B is the batch size, L is the sequence length, + and H denotes the model dimension. + """ + raise NotImplementedError("Subclasses must implement the forward method.") + + +class VocosBackbone(Backbone): + """ + Vocos backbone module built with ConvNeXt blocks. Supports additional conditioning with Adaptive Layer Normalization + + Args: + input_channels (int): Number of input features channels. + dim (int): Hidden dimension of the model. + intermediate_dim (int): Intermediate dimension used in ConvNeXtBlock. + num_layers (int): Number of ConvNeXtBlock layers. + layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to `1 / num_layers`. + adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm. + None means non-conditional model. Defaults to None. + """ + + def __init__( + self, + input_channels: int, + dim: int, + intermediate_dim: int, + num_layers: int, + layer_scale_init_value: Optional[float] = None, + adanorm_num_embeddings: Optional[int] = None, + ): + super().__init__() + self.input_channels = input_channels + self.embed = nn.Conv1d(input_channels, dim, kernel_size=7, padding=3) + self.adanorm = adanorm_num_embeddings is not None + if adanorm_num_embeddings: + self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6) + else: + self.norm = nn.LayerNorm(dim, eps=1e-6) + layer_scale_init_value = layer_scale_init_value or 1 / num_layers + self.convnext = nn.ModuleList( + [ + ConvNeXtBlock( + dim=dim, + intermediate_dim=intermediate_dim, + layer_scale_init_value=layer_scale_init_value, + adanorm_num_embeddings=adanorm_num_embeddings, + ) + for _ in range(num_layers) + ] + ) + self.final_layer_norm = nn.LayerNorm(dim, eps=1e-6) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, (nn.Conv1d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + nn.init.constant_(m.bias, 0) + + def forward(self, x: torch.Tensor, bandwidth_id: Optional[torch.Tensor] = None) -> torch.Tensor: + x = self.embed(x) + if self.adanorm: + assert bandwidth_id is not None + x = self.norm(x.transpose(1, 2), cond_embedding_id=bandwidth_id) + else: + x = self.norm(x.transpose(1, 2)) + x = x.transpose(1, 2) + for conv_block in self.convnext: + x = conv_block(x, cond_embedding_id=bandwidth_id) + x = self.final_layer_norm(x.transpose(1, 2)) + return x + + +class VocosResNetBackbone(Backbone): + """ + Vocos backbone module built with ResBlocks. + + Args: + input_channels (int): Number of input features channels. + dim (int): Hidden dimension of the model. + num_blocks (int): Number of ResBlock1 blocks. + layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to None. + """ + + def __init__( + self, input_channels, dim, num_blocks, layer_scale_init_value=None, + ): + super().__init__() + self.input_channels = input_channels + self.embed = weight_norm(nn.Conv1d(input_channels, dim, kernel_size=3, padding=1)) + layer_scale_init_value = layer_scale_init_value or 1 / num_blocks / 3 + self.resnet = nn.Sequential( + *[ResBlock1(dim=dim, layer_scale_init_value=layer_scale_init_value) for _ in range(num_blocks)] + ) + + def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor: + x = self.embed(x) + x = self.resnet(x) + x = x.transpose(1, 2) + return x diff --git a/vocos/modules.py b/vocos/modules.py new file mode 100644 index 0000000..9688a97 --- /dev/null +++ b/vocos/modules.py @@ -0,0 +1,213 @@ +from typing import Optional + +import torch +from torch import nn +from torch.nn.utils import weight_norm, remove_weight_norm + + +class ConvNeXtBlock(nn.Module): + """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal. + + Args: + dim (int): Number of input channels. + intermediate_dim (int): Dimensionality of the intermediate layer. + layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling. + Defaults to None. + adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm. + None means non-conditional LayerNorm. Defaults to None. + """ + + def __init__( + self, + dim: int, + intermediate_dim: int, + layer_scale_init_value: Optional[float] = None, + adanorm_num_embeddings: Optional[int] = None, + ): + super().__init__() + self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv + self.adanorm = adanorm_num_embeddings is not None + if adanorm_num_embeddings: + self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6) + else: + 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.pwconv2 = nn.Linear(intermediate_dim, dim) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True) + if layer_scale_init_value > 0 + else None + ) + + def forward(self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None) -> torch.Tensor: + residual = x + x = self.dwconv(x) + x = x.transpose(1, 2) # (B, C, T) -> (B, T, C) + if self.adanorm: + assert cond_embedding_id is not None + x = self.norm(x, cond_embedding_id) + else: + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.transpose(1, 2) # (B, T, C) -> (B, C, T) + + x = residual + x + return x + + +class AdaLayerNorm(nn.Module): + """ + Adaptive Layer Normalization module with learnable embeddings per `num_embeddings` classes + + Args: + num_embeddings (int): Number of embeddings. + embedding_dim (int): Dimension of the embeddings. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.dim = embedding_dim + self.scale = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim) + self.shift = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim) + torch.nn.init.ones_(self.scale.weight) + torch.nn.init.zeros_(self.shift.weight) + + def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor) -> torch.Tensor: + scale = self.scale(cond_embedding_id) + shift = self.shift(cond_embedding_id) + x = nn.functional.layer_norm(x, (self.dim,), eps=self.eps) + x = x * scale + shift + return x + + +class ResBlock1(nn.Module): + """ + ResBlock adapted from HiFi-GAN V1 (https://github.com/jik876/hifi-gan) with dilated 1D convolutions, + but without upsampling layers. + + Args: + dim (int): Number of input channels. + kernel_size (int, optional): Size of the convolutional kernel. Defaults to 3. + dilation (tuple[int], optional): Dilation factors for the dilated convolutions. + Defaults to (1, 3, 5). + lrelu_slope (float, optional): Negative slope of the LeakyReLU activation function. + Defaults to 0.1. + layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling. + Defaults to None. + """ + + def __init__( + self, + dim: int, + kernel_size: int = 3, + dilation: tuple[int] = (1, 3, 5), + lrelu_slope: float = 0.1, + layer_scale_init_value: float = None, + ): + super().__init__() + self.lrelu_slope = lrelu_slope + self.convs1 = nn.ModuleList( + [ + weight_norm( + nn.Conv1d( + dim, + dim, + kernel_size, + 1, + dilation=dilation[0], + padding=self.get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + nn.Conv1d( + dim, + dim, + kernel_size, + 1, + dilation=dilation[1], + padding=self.get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + nn.Conv1d( + dim, + dim, + kernel_size, + 1, + dilation=dilation[2], + padding=self.get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + + self.convs2 = nn.ModuleList( + [ + weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))), + weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))), + weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))), + ] + ) + + self.gamma = nn.ParameterList( + [ + nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True) + if layer_scale_init_value is not None + else None, + nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True) + if layer_scale_init_value is not None + else None, + nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True) + if layer_scale_init_value is not None + else None, + ] + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + for c1, c2, gamma in zip(self.convs1, self.convs2, self.gamma): + xt = torch.nn.functional.leaky_relu(x, negative_slope=self.lrelu_slope) + xt = c1(xt) + xt = torch.nn.functional.leaky_relu(xt, negative_slope=self.lrelu_slope) + xt = c2(xt) + if gamma is not None: + xt = gamma * 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) + + @staticmethod + def get_padding(kernel_size: int, dilation: int = 1) -> int: + return int((kernel_size * dilation - dilation) / 2) + + +def safe_log(x: torch.Tensor, clip_val: float = 1e-7) -> torch.Tensor: + """ + Computes the element-wise logarithm of the input tensor with clipping to avoid near-zero values. + + Args: + x (Tensor): Input tensor. + clip_val (float, optional): Minimum value to clip the input tensor. Defaults to 1e-7. + + Returns: + Tensor: Element-wise logarithm of the input tensor with clipping applied. + """ + return torch.log(torch.clip(x, min=clip_val)) + + +def symlog(x: torch.Tensor) -> torch.Tensor: + return torch.sign(x) * torch.log1p(x.abs()) + + +def symexp(x: torch.Tensor) -> torch.Tensor: + return torch.sign(x) * (torch.exp(x.abs()) - 1) diff --git a/vocos/pretrained.py b/vocos/pretrained.py new file mode 100644 index 0000000..507a1aa --- /dev/null +++ b/vocos/pretrained.py @@ -0,0 +1,232 @@ +import os +from typing import Tuple, Any, Union, Dict + +import torch +import yaml +from huggingface_hub import hf_hub_download +from torch import nn +from vocos.feature_extractors import FeatureExtractor, EncodecFeatures +from vocos.heads import FourierHead +from vocos.models import Backbone + + +def instantiate_class(args: Union[Any, Tuple[Any, ...]], init: Dict[str, Any]) -> Any: + """Instantiates a class with the given args and init. + + Args: + args: Positional arguments required for instantiation. + init: Dict of the form {"class_path":...,"init_args":...}. + + Returns: + The instantiated class object. + """ + kwargs = init.get("init_args", {}) + if not isinstance(args, tuple): + args = (args,) + class_module, class_name = init["class_path"].rsplit(".", 1) + module = __import__(class_module, fromlist=[class_name]) + args_class = getattr(module, class_name) + return args_class(*args, **kwargs) + + +class Vocos(nn.Module): + """ + The Vocos class represents a Fourier-based neural vocoder for audio synthesis. + This class is primarily designed for inference, with support for loading from pretrained + model checkpoints. It consists of three main components: a feature extractor, + a backbone, and a head. + """ + + def __init__( + self, feature_extractor: FeatureExtractor, backbone: Backbone, head: FourierHead, + ): + super().__init__() + self.feature_extractor = feature_extractor + self.backbone = backbone + self.head = head + + @classmethod + def from_hparams(cls, config_path: str) -> "Vocos": + """ + Class method to create a new Vocos model instance from hyperparameters stored in a yaml configuration file. + """ + with open(config_path, "r") as f: + config = yaml.safe_load(f) + feature_extractor = instantiate_class(args=(), init=config["feature_extractor"]) + backbone = instantiate_class(args=(), init=config["backbone"]) + head = instantiate_class(args=(), init=config["head"]) + model = cls(feature_extractor=feature_extractor, backbone=backbone, head=head) + return model + + @classmethod + def from_pretrained(self, repo_id: str) -> "Vocos": + """ + Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub. + """ + config_path = hf_hub_download(repo_id=repo_id, filename="config.yaml") + model_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin") + model = self.from_hparams(config_path) + state_dict = torch.load(model_path, map_location="cpu") + if isinstance(model.feature_extractor, EncodecFeatures): + encodec_parameters = { + "feature_extractor.encodec." + key: value + for key, value in model.feature_extractor.encodec.state_dict().items() + } + state_dict.update(encodec_parameters) + model.load_state_dict(state_dict) + model.eval() + return model + + + @classmethod + def from_hparams0802(cls, config_path: str) -> "Vocos": + """ + Class method to create a new Vocos model instance from hyperparameters stored in a yaml configuration file. + """ + with open(config_path, "r") as f: + config = yaml.safe_load(f) + feature_extractor = instantiate_class(args=(), init=config['model']['init_args']["feature_extractor"]) + backbone = instantiate_class(args=(), init=config['model']['init_args']["backbone"]) + head = instantiate_class(args=(), init=config['model']['init_args']["head"]) + model = cls(feature_extractor=feature_extractor, backbone=backbone, head=head) + return model + + + @classmethod + def from_pretrained0802(self, config_path, model_path): + """ + Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub. + """ + model = self.from_hparams0802(config_path) + state_dict_raw = torch.load(model_path, map_location="cpu")['state_dict'] + state_dict = dict() + for k, v in state_dict_raw.items(): + if k.startswith('backbone.') or k.startswith('head.') or k.startswith('feature_extractor.'): + state_dict[k] = v + # if isinstance(model.feature_extractor, EncodecFeatures): + # encodec_parameters = { + # "feature_extractor.encodec." + key: value + # for key, value in model.feature_extractor.encodec.state_dict().items() + # } + # state_dict.update(encodec_parameters) + model.load_state_dict(state_dict) + model.eval() + return model + + + @classmethod + def from_pretrained0911(self, config_path, model_folder_path): + """ + Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub. + """ + model = self.from_hparams0802(config_path) + + models = os.listdir(model_folder_path) + val_loss = [] + for item in models: + if not item.startswith('vocos_'): + continue + val_loss.append(item[-11:-5]) + val_loss.sort() + val_loss = val_loss[:3] # 取前3性能较好的模型平均 + state_dict = dict() + state_dicts = [] + for item in models: + if not item.startswith('vocos_'): + continue + ll = item[-11:-5] + if ll not in val_loss: + continue + model_path = model_folder_path + '/' + item + state_dict_raw = torch.load(model_path, map_location="cpu")['state_dict'] + state_dict_single = dict() + for k, v in state_dict_raw.items(): + if k.startswith('backbone.') or k.startswith('head.') or k.startswith('feature_extractor.'): + state_dict_single[k] = v + state_dicts.append(state_dict_single) + for kk in state_dicts[0].keys(): + vv = state_dicts[0][kk] + for i in range(1, len(state_dicts)): + ss = state_dicts[i] + vv += ss[kk] + vm = vv/len(state_dicts) + state_dict[kk] = vm + model.load_state_dict(state_dict) + model.eval() + return model + + + @torch.inference_mode() + def forward(self, audio_input: torch.Tensor, **kwargs: Any) -> torch.Tensor: + """ + Method to run a copy-synthesis from audio waveform. The feature extractor first processes the audio input, + which is then passed through the backbone and the head to reconstruct the audio output. + + Args: + audio_input (Tensor): The input tensor representing the audio waveform of shape (B, T), + where B is the batch size and L is the waveform length. + + + Returns: + Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T). + """ + features, _, _ = self.feature_extractor(audio_input, **kwargs) # 0818 + audio_output = self.decode(features, **kwargs) + return audio_output + + + # 0818 + @torch.inference_mode() + def encode(self, audio_input: torch.Tensor, **kwargs: Any) -> torch.Tensor: + features, discrete_codes, _ = self.feature_extractor(audio_input, **kwargs) + return features,discrete_codes + + + @torch.inference_mode() + def decode(self, features_input: torch.Tensor, **kwargs: Any) -> torch.Tensor: + """ + Method to decode audio waveform from already calculated features. The features input is passed through + the backbone and the head to reconstruct the audio output. + + Args: + features_input (Tensor): The input tensor of features of shape (B, C, L), where B is the batch size, + C denotes the feature dimension, and L is the sequence length. + + Returns: + Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T). + """ + x = self.backbone(features_input, **kwargs) + audio_output = self.head(x) + return audio_output + + @torch.inference_mode() + def codes_to_features(self, codes: torch.Tensor) -> torch.Tensor: + """ + Transforms an input sequence of discrete tokens (codes) into feature embeddings using the feature extractor's + codebook weights. + + Args: + codes (Tensor): The input tensor. Expected shape is (K, L) or (K, B, L), + where K is the number of codebooks, B is the batch size and L is the sequence length. + + Returns: + Tensor: Features of shape (B, C, L), where B is the batch size, C denotes the feature dimension, + and L is the sequence length. + """ + assert isinstance( + self.feature_extractor, EncodecFeatures + ), "Feature extractor should be an instance of EncodecFeatures" + + if codes.dim() == 2: + codes = codes.unsqueeze(1) + + n_bins = self.feature_extractor.encodec.quantizer.bins + offsets = torch.arange(0, n_bins * len(codes), n_bins, device=codes.device) + embeddings_idxs = codes + offsets.view(-1, 1, 1) + + tmp=torch.cat([vq.codebook for vq in self.feature_extractor.encodec.quantizer.vq.layers],dim=0) + # features = torch.nn.functional.embedding(embeddings_idxs, self.feature_extractor.codebook_weights).sum(dim=0) + features = torch.nn.functional.embedding(embeddings_idxs, tmp).sum(dim=0) + features = features.transpose(1, 2) + + return features diff --git a/vocos/pretrained_model.py b/vocos/pretrained_model.py new file mode 100644 index 0000000..d8cfdaf --- /dev/null +++ b/vocos/pretrained_model.py @@ -0,0 +1,192 @@ +from typing import Tuple, Any, Union, Dict + +import torch +import yaml +from huggingface_hub import hf_hub_download +from torch import nn +from vocos.feature_extractors import FeatureExtractor, EncodecFeatures +from vocos.heads import FourierHead +from vocos.models import Backbone +from vocos.discriminators import MultiPeriodDiscriminator, MultiResolutionDiscriminator + + +def instantiate_class(args: Union[Any, Tuple[Any, ...]], init: Dict[str, Any]) -> Any: + """Instantiates a class with the given args and init. + + Args: + args: Positional arguments required for instantiation. + init: Dict of the form {"class_path":...,"init_args":...}. + + Returns: + The instantiated class object. + """ + kwargs = init.get("init_args", {}) + if not isinstance(args, tuple): + args = (args,) + class_module, class_name = init["class_path"].rsplit(".", 1) + module = __import__(class_module, fromlist=[class_name]) + args_class = getattr(module, class_name) + return args_class(*args, **kwargs) + + +class Vocos(nn.Module): + """ + The Vocos class represents a Fourier-based neural vocoder for audio synthesis. + This class is primarily designed for inference, with support for loading from pretrained + model checkpoints. It consists of three main components: a feature extractor, + a backbone, and a head. + """ + + def __init__( + self, feature_extractor: FeatureExtractor, backbone: Backbone, head: FourierHead, + multiperioddisc: MultiPeriodDiscriminator, multiresddisc: MultiResolutionDiscriminator, + ): + super().__init__() + self.feature_extractor = feature_extractor + self.backbone = backbone + self.head = head + + self.multiperioddisc = multiperioddisc + self.multiresddisc = multiresddisc + + @classmethod + def from_hparams0828(cls, config_path: str) -> "Vocos": + """ + Class method to create a new Vocos model instance from hyperparameters stored in a yaml configuration file. + """ + with open(config_path, "r") as f: + config = yaml.safe_load(f) + feature_extractor = instantiate_class(args=(), init=config['model']['init_args']["feature_extractor"]) + backbone = instantiate_class(args=(), init=config['model']['init_args']["backbone"]) + head = instantiate_class(args=(), init=config['model']['init_args']["head"]) + model = cls(feature_extractor=feature_extractor, backbone=backbone, head=head, + multiperioddisc=MultiPeriodDiscriminator(num_embeddings=4), + multiresddisc=MultiResolutionDiscriminator(num_embeddings=4)) + return model + + @classmethod + def from_pretrained0828(self, config_path, model_path): + """ + Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub. + """ + model = self.from_hparams0828(config_path) + state_dict_raw = torch.load(model_path, map_location="cpu")['state_dict'] + state_dict = dict() + for k, v in state_dict_raw.items(): + if k.startswith('backbone.') or k.startswith('head.') or k.startswith('feature_extractor.') \ + or k.startswith('multiperioddisc.') or k.startswith('multiresddisc.'): + state_dict[k] = v + # if isinstance(model.feature_extractor, EncodecFeatures): + # encodec_parameters = { + # "feature_extractor.encodec." + key: value + # for key, value in model.feature_extractor.encodec.state_dict().items() + # } + # state_dict.update(encodec_parameters) + model.load_state_dict(state_dict) + return model + + @classmethod + def from_hparams0802(cls, config_path: str) -> "Vocos": + """ + Class method to create a new Vocos model instance from hyperparameters stored in a yaml configuration file. + """ + with open(config_path, "r") as f: + config = yaml.safe_load(f) + feature_extractor = instantiate_class(args=(), init=config['model']['init_args']["feature_extractor"]) + backbone = instantiate_class(args=(), init=config['model']['init_args']["backbone"]) + head = instantiate_class(args=(), init=config['model']['init_args']["head"]) + model = cls(feature_extractor=feature_extractor, backbone=backbone, head=head) + return model + + @classmethod + def from_pretrained0802(self, config_path, model_path): + """ + Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub. + """ + model = self.from_hparams0802(config_path) + state_dict_raw = torch.load(model_path, map_location="cpu")['state_dict'] + state_dict = dict() + for k, v in state_dict_raw.items(): + if k.startswith('backbone.') or k.startswith('head.') or k.startswith('feature_extractor.'): + state_dict[k] = v + # if isinstance(model.feature_extractor, EncodecFeatures): + # encodec_parameters = { + # "feature_extractor.encodec." + key: value + # for key, value in model.feature_extractor.encodec.state_dict().items() + # } + # state_dict.update(encodec_parameters) + model.load_state_dict(state_dict) + model.eval() + return model + + @torch.inference_mode() + def forward(self, audio_input: torch.Tensor, **kwargs: Any) -> torch.Tensor: + """ + Method to run a copy-synthesis from audio waveform. The feature extractor first processes the audio input, + which is then passed through the backbone and the head to reconstruct the audio output. + + Args: + audio_input (Tensor): The input tensor representing the audio waveform of shape (B, T), + where B is the batch size and L is the waveform length. + + + Returns: + Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T). + """ + features, _, _ = self.feature_extractor(audio_input, **kwargs) # 0818 + audio_output = self.decode(features, **kwargs) + return audio_output + + + # 0818 + @torch.inference_mode() + def encode(self, audio_input: torch.Tensor, **kwargs: Any) -> torch.Tensor: + features, _, _ = self.feature_extractor(audio_input, **kwargs) + return features + + + @torch.inference_mode() + def decode(self, features_input: torch.Tensor, **kwargs: Any) -> torch.Tensor: + """ + Method to decode audio waveform from already calculated features. The features input is passed through + the backbone and the head to reconstruct the audio output. + + Args: + features_input (Tensor): The input tensor of features of shape (B, C, L), where B is the batch size, + C denotes the feature dimension, and L is the sequence length. + + Returns: + Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T). + """ + x = self.backbone(features_input, **kwargs) + audio_output = self.head(x) + return audio_output + + @torch.inference_mode() + def codes_to_features(self, codes: torch.Tensor) -> torch.Tensor: + """ + Transforms an input sequence of discrete tokens (codes) into feature embeddings using the feature extractor's + codebook weights. + + Args: + codes (Tensor): The input tensor. Expected shape is (K, L) or (K, B, L), + where K is the number of codebooks, B is the batch size and L is the sequence length. + + Returns: + Tensor: Features of shape (B, C, L), where B is the batch size, C denotes the feature dimension, + and L is the sequence length. + """ + assert isinstance( + self.feature_extractor, EncodecFeatures + ), "Feature extractor should be an instance of EncodecFeatures" + + if codes.dim() == 2: + codes = codes.unsqueeze(1) + + n_bins = self.feature_extractor.encodec.quantizer.bins + offsets = torch.arange(0, n_bins * len(codes), n_bins, device=codes.device) + embeddings_idxs = codes + offsets.view(-1, 1, 1) + features = torch.nn.functional.embedding(embeddings_idxs, self.feature_extractor.codebook_weights).sum(dim=0) + features = features.transpose(1, 2) + + return features diff --git a/vocos/spectral_ops.py b/vocos/spectral_ops.py new file mode 100644 index 0000000..a8eda1c --- /dev/null +++ b/vocos/spectral_ops.py @@ -0,0 +1,192 @@ +import numpy as np +import scipy +import torch +from torch import nn, view_as_real, view_as_complex + + +class ISTFT(nn.Module): + """ + Custom implementation of ISTFT since torch.istft doesn't allow custom padding (other than `center=True`) with + windowing. This is because the NOLA (Nonzero Overlap Add) check fails at the edges. + See issue: https://github.com/pytorch/pytorch/issues/62323 + Specifically, in the context of neural vocoding we are interested in "same" padding analogous to CNNs. + The NOLA constraint is met as we trim padded samples anyway. + + Args: + n_fft (int): Size of Fourier transform. + hop_length (int): The distance between neighboring sliding window frames. + win_length (int): The size of window frame and STFT filter. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + """ + + def __init__(self, n_fft: int, hop_length: int, win_length: int, padding: str = "same"): + super().__init__() + if padding not in ["center", "same"]: + raise ValueError("Padding must be 'center' or 'same'.") + self.padding = padding + self.n_fft = n_fft + self.hop_length = hop_length + self.win_length = win_length + window = torch.hann_window(win_length) + self.register_buffer("window", window) + + def forward(self, spec: torch.Tensor) -> torch.Tensor: + """ + Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram. + + Args: + spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size, + N is the number of frequency bins, and T is the number of time frames. + + Returns: + Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal. + """ + if self.padding == "center": + # Fallback to pytorch native implementation + return torch.istft(spec, self.n_fft, self.hop_length, self.win_length, self.window, center=True) + elif self.padding == "same": + pad = (self.win_length - self.hop_length) // 2 + else: + raise ValueError("Padding must be 'center' or 'same'.") + + assert spec.dim() == 3, "Expected a 3D tensor as input" + B, N, T = spec.shape + + # Inverse FFT + ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward") + ifft = ifft * self.window[None, :, None] + + # Overlap and Add + output_size = (T - 1) * self.hop_length + self.win_length + y = torch.nn.functional.fold( + ifft, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length), + )[:, 0, 0, pad:-pad] + + # Window envelope + window_sq = self.window.square().expand(1, T, -1).transpose(1, 2) + window_envelope = torch.nn.functional.fold( + window_sq, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length), + ).squeeze()[pad:-pad] + + # Normalize + assert (window_envelope > 1e-11).all() + y = y / window_envelope + + return y + + +class MDCT(nn.Module): + """ + Modified Discrete Cosine Transform (MDCT) module. + + Args: + frame_len (int): Length of the MDCT frame. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + """ + + def __init__(self, frame_len: int, padding: str = "same"): + super().__init__() + if padding not in ["center", "same"]: + raise ValueError("Padding must be 'center' or 'same'.") + self.padding = padding + self.frame_len = frame_len + N = frame_len // 2 + n0 = (N + 1) / 2 + window = torch.from_numpy(scipy.signal.cosine(frame_len)).float() + self.register_buffer("window", window) + + pre_twiddle = torch.exp(-1j * torch.pi * torch.arange(frame_len) / frame_len) + post_twiddle = torch.exp(-1j * torch.pi * n0 * (torch.arange(N) + 0.5) / N) + # view_as_real: NCCL Backend does not support ComplexFloat data type + # https://github.com/pytorch/pytorch/issues/71613 + self.register_buffer("pre_twiddle", view_as_real(pre_twiddle)) + self.register_buffer("post_twiddle", view_as_real(post_twiddle)) + + def forward(self, audio: torch.Tensor) -> torch.Tensor: + """ + Apply the Modified Discrete Cosine Transform (MDCT) to the input audio. + + Args: + audio (Tensor): Input audio waveform of shape (B, T), where B is the batch size + and T is the length of the audio. + + Returns: + Tensor: MDCT coefficients of shape (B, L, N), where L is the number of output frames + and N is the number of frequency bins. + """ + if self.padding == "center": + audio = torch.nn.functional.pad(audio, (self.frame_len // 2, self.frame_len // 2)) + elif self.padding == "same": + # hop_length is 1/2 frame_len + audio = torch.nn.functional.pad(audio, (self.frame_len // 4, self.frame_len // 4)) + else: + raise ValueError("Padding must be 'center' or 'same'.") + + x = audio.unfold(-1, self.frame_len, self.frame_len // 2) + N = self.frame_len // 2 + x = x * self.window.expand(x.shape) + X = torch.fft.fft(x * view_as_complex(self.pre_twiddle).expand(x.shape), dim=-1)[..., :N] + res = X * view_as_complex(self.post_twiddle).expand(X.shape) * np.sqrt(1 / N) + return torch.real(res) * np.sqrt(2) + + +class IMDCT(nn.Module): + """ + Inverse Modified Discrete Cosine Transform (IMDCT) module. + + Args: + frame_len (int): Length of the MDCT frame. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + """ + + def __init__(self, frame_len: int, padding: str = "same"): + super().__init__() + if padding not in ["center", "same"]: + raise ValueError("Padding must be 'center' or 'same'.") + self.padding = padding + self.frame_len = frame_len + N = frame_len // 2 + n0 = (N + 1) / 2 + window = torch.from_numpy(scipy.signal.cosine(frame_len)).float() + self.register_buffer("window", window) + + pre_twiddle = torch.exp(1j * torch.pi * n0 * torch.arange(N * 2) / N) + post_twiddle = torch.exp(1j * torch.pi * (torch.arange(N * 2) + n0) / (N * 2)) + self.register_buffer("pre_twiddle", view_as_real(pre_twiddle)) + self.register_buffer("post_twiddle", view_as_real(post_twiddle)) + + def forward(self, X: torch.Tensor) -> torch.Tensor: + """ + Apply the Inverse Modified Discrete Cosine Transform (IMDCT) to the input MDCT coefficients. + + Args: + X (Tensor): Input MDCT coefficients of shape (B, L, N), where B is the batch size, + L is the number of frames, and N is the number of frequency bins. + + Returns: + Tensor: Reconstructed audio waveform of shape (B, T), where T is the length of the audio. + """ + B, L, N = X.shape + Y = torch.zeros((B, L, N * 2), dtype=X.dtype, device=X.device) + Y[..., :N] = X + Y[..., N:] = -1 * torch.conj(torch.flip(X, dims=(-1,))) + y = torch.fft.ifft(Y * view_as_complex(self.pre_twiddle).expand(Y.shape), dim=-1) + y = torch.real(y * view_as_complex(self.post_twiddle).expand(y.shape)) * np.sqrt(N) * np.sqrt(2) + result = y * self.window.expand(y.shape) + output_size = (1, (L + 1) * N) + audio = torch.nn.functional.fold( + result.transpose(1, 2), + output_size=output_size, + kernel_size=(1, self.frame_len), + stride=(1, self.frame_len // 2), + )[:, 0, 0, :] + + if self.padding == "center": + pad = self.frame_len // 2 + elif self.padding == "same": + pad = self.frame_len // 4 + else: + raise ValueError("Padding must be 'center' or 'same'.") + + audio = audio[:, pad:-pad] + return audio