70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
#!/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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from concurrent.futures import ThreadPoolExecutor, as_completed
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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 single_job(utt):
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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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return utt, speech_token
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def main(args):
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all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()]
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utt2speech_token = {}
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for future in tqdm(as_completed(all_task)):
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utt, speech_token = future.result()
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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", type=str)
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parser.add_argument("--onnx_path", type=str)
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parser.add_argument("--num_thread", type=int, default=8)
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args = parser.parse_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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executor = ThreadPoolExecutor(max_workers=args.num_thread)
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main(args)
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