mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
68 lines
2.6 KiB
Python
Executable File
68 lines
2.6 KiB
Python
Executable File
#!/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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