138 lines
4.9 KiB
Python
138 lines
4.9 KiB
Python
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#!/usr/bin/env python3
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# encoding: utf-8
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import sys
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import argparse
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import json
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import codecs
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import yaml
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import torch
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import torchaudio
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import torchaudio.compliance.kaldi as kaldi
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from torch.utils.data import Dataset, DataLoader
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torchaudio.set_audio_backend("sox_io")
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class CollateFunc(object):
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''' Collate function for AudioDataset
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'''
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def __init__(self, feat_dim, resample_rate):
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self.feat_dim = feat_dim
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self.resample_rate = resample_rate
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pass
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def __call__(self, batch):
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mean_stat = torch.zeros(self.feat_dim)
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var_stat = torch.zeros(self.feat_dim)
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number = 0
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for item in batch:
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value = item[1].strip().split(",")
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assert len(value) == 3 or len(value) == 1
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wav_path = value[0]
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sample_rate = torchaudio.backend.sox_io_backend.info(wav_path).sample_rate
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resample_rate = sample_rate
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# len(value) == 3 means segmented wav.scp,
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# len(value) == 1 means original wav.scp
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if len(value) == 3:
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start_frame = int(float(value[1]) * sample_rate)
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end_frame = int(float(value[2]) * sample_rate)
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waveform, sample_rate = torchaudio.backend.sox_io_backend.load(
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filepath=wav_path,
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num_frames=end_frame - start_frame,
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frame_offset=start_frame)
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else:
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waveform, sample_rate = torchaudio.load(item[1])
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waveform = waveform * (1 << 15)
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if self.resample_rate != 0 and self.resample_rate != sample_rate:
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resample_rate = self.resample_rate
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waveform = torchaudio.transforms.Resample(
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orig_freq=sample_rate, new_freq=resample_rate)(waveform)
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mat = kaldi.fbank(waveform,
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num_mel_bins=self.feat_dim,
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dither=0.0,
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energy_floor=0.0,
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sample_frequency=resample_rate)
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mean_stat += torch.sum(mat, axis=0)
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var_stat += torch.sum(torch.square(mat), axis=0)
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number += mat.shape[0]
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return number, mean_stat, var_stat
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class AudioDataset(Dataset):
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def __init__(self, data_file):
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self.items = []
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with codecs.open(data_file, 'r', encoding='utf-8') as f:
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for line in f:
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arr = line.strip().split()
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self.items.append((arr[0], arr[1]))
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def __len__(self):
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return len(self.items)
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def __getitem__(self, idx):
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return self.items[idx]
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='extract CMVN stats')
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parser.add_argument('--num_workers',
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default=0,
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type=int,
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help='num of subprocess workers for processing')
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parser.add_argument('--train_config',
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default='',
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help='training yaml conf')
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parser.add_argument('--in_scp', default=None, help='wav scp file')
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parser.add_argument('--out_cmvn',
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default='global_cmvn',
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help='global cmvn file')
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args = parser.parse_args()
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with open(args.train_config, 'r') as fin:
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configs = yaml.load(fin, Loader=yaml.FullLoader)
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feat_dim = configs['collate_conf']['feature_extraction_conf']['mel_bins']
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resample_rate = 0
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if 'resample' in configs['collate_conf']['feature_extraction_conf']:
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resample_rate = configs['collate_conf']['feature_extraction_conf']['resample']
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print('using resample and new sample rate is {}'.format(resample_rate))
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collate_func = CollateFunc(feat_dim, resample_rate)
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dataset = AudioDataset(args.in_scp)
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batch_size = 20
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data_loader = DataLoader(dataset,
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batch_size=batch_size,
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shuffle=True,
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sampler=None,
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num_workers=args.num_workers,
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collate_fn=collate_func)
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with torch.no_grad():
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all_number = 0
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all_mean_stat = torch.zeros(feat_dim)
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all_var_stat = torch.zeros(feat_dim)
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wav_number = 0
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for i, batch in enumerate(data_loader):
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number, mean_stat, var_stat = batch
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all_mean_stat += mean_stat
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all_var_stat += var_stat
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all_number += number
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wav_number += batch_size
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if wav_number % 1000 == 0:
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print(f'processed {wav_number} wavs, {all_number} frames',
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file=sys.stderr,
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flush=True)
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cmvn_info = {
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'mean_stat': list(all_mean_stat.tolist()),
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'var_stat': list(all_var_stat.tolist()),
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'frame_num': all_number
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}
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with open(args.out_cmvn, 'w') as fout:
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fout.write(json.dumps(cmvn_info))
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