mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-05 18:09:24 +08:00
add cosyvoice code
This commit is contained in:
lyuxiang.lx
67
tools/extract_embedding.py
Executable file
67
tools/extract_embedding.py
Executable file
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#!/usr/bin/env python3
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import torch
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import torchaudio
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from tqdm import tqdm
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import onnxruntime
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import torchaudio.compliance.kaldi as kaldi
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def main(args):
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utt2wav, utt2spk = {}, {}
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with open('{}/wav.scp'.format(args.dir)) as f:
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for l in f:
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l = l.replace('\n', '').split()
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utt2wav[l[0]] = l[1]
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with open('{}/utt2spk'.format(args.dir)) as f:
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for l in f:
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l = l.replace('\n', '').split()
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utt2spk[l[0]] = l[1]
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option = onnxruntime.SessionOptions()
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option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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option.intra_op_num_threads = 1
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providers = ["CPUExecutionProvider"]
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ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
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utt2embedding, spk2embedding = {}, {}
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for utt in tqdm(utt2wav.keys()):
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audio, sample_rate = torchaudio.load(utt2wav[utt])
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if sample_rate != 16000:
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audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio)
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feat = kaldi.fbank(audio,
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num_mel_bins=80,
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dither=0,
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sample_frequency=16000)
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feat = feat - feat.mean(dim=0, keepdim=True)
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embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
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utt2embedding[utt] = embedding
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spk = utt2spk[utt]
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if spk not in spk2embedding:
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spk2embedding[spk] = []
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spk2embedding[spk].append(embedding)
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torch.save(utt2embedding, '{}/utt2embedding.pt'.format(args.dir))
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torch.save(spk2embedding, '{}/spk2embedding.pt'.format(args.dir))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--dir',
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type=str)
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parser.add_argument('--onnx_path',
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type=str)
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args = parser.parse_args()
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main(args)
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61
tools/extract_speech_token.py
Executable file
61
tools/extract_speech_token.py
Executable file
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#!/usr/bin/env python3
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import torch
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from tqdm import tqdm
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import onnxruntime
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import numpy as np
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import torchaudio
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import whisper
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def main(args):
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utt2wav = {}
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with open('{}/wav.scp'.format(args.dir)) as f:
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for l in f:
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l = l.replace('\n', '').split()
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utt2wav[l[0]] = l[1]
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option = onnxruntime.SessionOptions()
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option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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option.intra_op_num_threads = 1
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providers = ["CUDAExecutionProvider"]
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ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
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utt2speech_token = {}
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for utt in tqdm(utt2wav.keys()):
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audio, sample_rate = torchaudio.load(utt2wav[utt])
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if sample_rate != 16000:
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audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio)
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if audio.shape[1] / 16000 > 30:
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logging.warning('do not support extract speech token for audio longer than 30s')
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speech_token = []
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else:
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feat = whisper.log_mel_spectrogram(audio, n_mels=128)
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speech_token = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.detach().cpu().numpy(),
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ort_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist()
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utt2speech_token[utt] = speech_token
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torch.save(utt2speech_token, '{}/utt2speech_token.pt'.format(args.dir))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--dir',
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type=str)
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parser.add_argument('--onnx_path',
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type=str)
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args = parser.parse_args()
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main(args)
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112
tools/make_parquet_list.py
Executable file
112
tools/make_parquet_list.py
Executable file
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#!/usr/bin/env python3
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import os
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import json
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from tqdm import tqdm
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import pandas as pd
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import multiprocessing
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import time
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import torch
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def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file):
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start_time = time.time()
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data_list = []
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for utt in tqdm(utt_list):
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data = open(utt2wav[utt], 'rb').read()
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data_list.append(data)
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wav_list = [utt2wav[utt] for utt in utt_list]
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text_list = [utt2text[utt] for utt in utt_list]
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spk_list = [utt2spk[utt] for utt in utt_list]
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uttembedding_list = [utt2embedding[utt] for utt in utt_list]
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spkembedding_list = [spk2embedding[utt2spk[utt]] for utt in utt_list]
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speech_token_list = [utt2speech_token[utt] for utt in utt_list]
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# 保存到parquet,utt2parquet_file,spk2parquet_file
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df = pd.DataFrame()
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df['utt'] = utt_list
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df['wav'] = wav_list
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df['audio_data'] = data_list
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df['text'] = text_list
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df['spk'] = spk_list
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df['utt_embedding'] = uttembedding_list
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df['spk_embedding'] = spkembedding_list
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df['speech_token'] = speech_token_list
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df.to_parquet(parquet_file)
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with open(utt2parquet_file, 'w') as f:
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json.dump({k: parquet_file for k in utt_list}, f, ensure_ascii=False, indent=2)
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with open(spk2parquet_file, 'w') as f:
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json.dump({k: parquet_file for k in list(set(spk_list))}, f, ensure_ascii=False, indent=2)
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logging.info('spend time {}'.format(time.time() - start_time))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--num_utts_per_parquet',
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type=int,
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default=1000,
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help='num utts per parquet')
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parser.add_argument('--num_processes',
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type=int,
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default=1,
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help='num processes for make parquets')
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parser.add_argument('--src_dir',
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type=str)
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parser.add_argument('--des_dir',
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type=str)
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args = parser.parse_args()
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utt2wav, utt2text, utt2spk = {}, {}, {}
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with open('{}/wav.scp'.format(args.src_dir)) as f:
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for l in f:
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l = l.replace('\n', '').split()
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utt2wav[l[0]] = l[1]
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with open('{}/text'.format(args.src_dir)) as f:
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for l in f:
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l = l.replace('\n', '').split()
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utt2text[l[0]] = ' '.join(l[1:])
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with open('{}/utt2spk'.format(args.src_dir)) as f:
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for l in f:
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l = l.replace('\n', '').split()
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utt2spk[l[0]] = l[1]
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utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir))
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spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir))
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utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir))
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utts = list(utt2wav.keys())
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# Using process pool to speedup
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pool = multiprocessing.Pool(processes=args.num_processes)
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parquet_list, utt2parquet_list, spk2parquet_list = [], [], []
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for i, j in enumerate(range(0, len(utts), args.num_utts_per_parquet)):
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parquet_file = os.path.join(args.des_dir, 'parquet_{:09d}.tar'.format(i))
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utt2parquet_file = os.path.join(args.des_dir, 'utt2parquet_{:09d}.json'.format(i))
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spk2parquet_file = os.path.join(args.des_dir, 'spk2parquet_{:09d}.json'.format(i))
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parquet_list.append(parquet_file)
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utt2parquet_list.append(utt2parquet_file)
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spk2parquet_list.append(spk2parquet_file)
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pool.apply_async(job, (utts[j: j + args.num_utts_per_parquet], parquet_file, utt2parquet_file, spk2parquet_file))
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pool.close()
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pool.join()
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with open('{}/data.list'.format(args.des_dir), 'w', encoding='utf8') as f1, \
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open('{}/utt2data.list'.format(args.des_dir), 'w', encoding='utf8') as f2, \
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open('{}/spk2data.list'.format(args.des_dir), 'w', encoding='utf8') as f3:
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for name in parquet_list:
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f1.write(name + '\n')
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for name in utt2parquet_list:
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f2.write(name + '\n')
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for name in spk2parquet_list:
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f3.write(name + '\n')
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