129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Chao Yang)
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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 torchaudio
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import torchaudio.compliance.kaldi as kaldi
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import wenet.dataset.kaldi_io as kaldi_io
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# The "sox" backends are deprecated and will be removed in 0.9.0 release.
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# So here we use sox_io backend
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torchaudio.set_audio_backend("sox_io")
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def parse_opts():
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parser = argparse.ArgumentParser(description='training your network')
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parser.add_argument('--num_mel_bins',
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default=80,
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type=int,
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help='Number of triangular mel-frequency bins')
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parser.add_argument('--frame_length',
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type=int,
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default=25,
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help='Frame length in milliseconds')
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parser.add_argument('--frame_shift',
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type=int,
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default=10,
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help='Frame shift in milliseconds')
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parser.add_argument('--dither',
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type=int,
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default=0.0,
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help='Dithering constant (0.0 means no dither)')
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parser.add_argument('--segments', default=None, help='segments file')
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parser.add_argument('wav_scp', help='wav scp file')
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parser.add_argument('out_ark', help='output ark file')
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parser.add_argument('out_scp', help='output scp file')
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args = parser.parse_args()
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return args
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# wav format: <key> <wav_path>
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def load_wav_scp(wav_scp_file):
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wav_list = []
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with open(wav_scp_file, 'r', encoding='utf8') as fin:
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for line in fin:
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arr = line.strip().split()
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assert len(arr) == 2
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wav_list.append((arr[0], arr[1]))
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return wav_list
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# wav format: <key> <wav_path>
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def load_wav_scp_dict(wav_scp_file):
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wav_dict = {}
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with open(wav_scp_file, 'r', encoding='utf8') as fin:
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for line in fin:
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arr = line.strip().split()
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assert len(arr) == 2
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wav_dict[arr[0]] = arr[1]
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return wav_dict
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# Segments format: <key> <wav_key> <start> <end>
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def load_wav_segments(wav_scp_file, segments_file):
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wav_dict = load_wav_scp_dict(wav_scp_file)
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audio_list = []
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with open(segments_file, 'r', encoding='utf8') as fin:
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for line in fin:
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arr = line.strip().split()
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assert len(arr) == 4
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key = arr[0]
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wav_file = wav_dict[arr[1]]
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start = float(arr[2])
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end = float(arr[3])
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audio_list.append((key, wav_file, start, end))
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return audio_list
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if __name__ == '__main__':
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args = parse_opts()
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s %(levelname)s %(message)s')
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if args.segments is None:
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audio_list = load_wav_scp(args.wav_scp)
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else:
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audio_list = load_wav_segments(args.wav_scp, args.segments)
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count = 0
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with open(args.out_ark, 'wb') as ark_fout, \
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open(args.out_scp, 'w', encoding='utf8') as scp_fout:
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for item in audio_list:
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if len(item) == 2:
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key, wav_path = item
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waveform, sample_rate = torchaudio.load_wav(wav_path)
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else:
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assert len(item) == 4
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key, wav_path, start, end = item
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sample_rate = torchaudio.info(wav_path).sample_rate
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frame_offset = int(start * sample_rate)
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num_frames = int((end - start) * sample_rate)
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waveform, sample_rate = torchaudio.load_wav(
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wav_path, frame_offset, num_frames)
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mat = kaldi.fbank(waveform,
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num_mel_bins=args.num_mel_bins,
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frame_length=args.frame_length,
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frame_shift=args.frame_shift,
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dither=args.dither,
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energy_floor=0.0,
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sample_frequency=sample_rate)
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mat = mat.detach().numpy()
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kaldi_io.write_ark_scp(key, mat, ark_fout, scp_fout)
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count += 1
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if count % 10000 == 0:
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logging.info('Progress {}/{}'.format(count, len(audio_list)))
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