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| from kaldiio import WriteHelper
|
| import argparse
| import numpy as np
| import json
| import torch
| import torchaudio
| import torchaudio.compliance.kaldi as kaldi
|
|
| def compute_fbank(wav_file,
| num_mel_bins=80,
| frame_length=25,
| frame_shift=10,
| dither=0.0,
| resample_rate=16000,
| speed=1.0,
| window_type="hamming"):
|
| waveform, sample_rate = torchaudio.load(wav_file)
| if resample_rate != sample_rate:
| waveform = torchaudio.transforms.Resample(orig_freq=sample_rate,
| new_freq=resample_rate)(waveform)
| if speed != 1.0:
| waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
| waveform, resample_rate,
| [['speed', str(speed)], ['rate', str(resample_rate)]]
| )
|
| waveform = waveform * (1 << 15)
| mat = kaldi.fbank(waveform,
| num_mel_bins=num_mel_bins,
| frame_length=frame_length,
| frame_shift=frame_shift,
| dither=dither,
| energy_floor=0.0,
| window_type=window_type,
| sample_frequency=resample_rate)
|
| return mat.numpy()
|
|
| def get_parser():
| parser = argparse.ArgumentParser(
| description="computer features",
| formatter_class=argparse.ArgumentDefaultsHelpFormatter,
| )
| parser.add_argument(
| "--wav-lists",
| "-w",
| default=False,
| required=True,
| type=str,
| help="input wav lists",
| )
| parser.add_argument(
| "--text-files",
| "-t",
| default=False,
| required=True,
| type=str,
| help="input text files",
| )
| parser.add_argument(
| "--dims",
| "-d",
| default=80,
| type=int,
| help="feature dims",
| )
| parser.add_argument(
| "--max-lengths",
| "-m",
| default=1500,
| type=int,
| help="max frame numbers",
| )
| parser.add_argument(
| "--sample-frequency",
| "-s",
| default=16000,
| type=int,
| help="sample frequency",
| )
| parser.add_argument(
| "--speed-perturb",
| "-p",
| default="1.0",
| type=str,
| help="speed perturb",
| )
| parser.add_argument(
| "--ark-index",
| "-a",
| default=1,
| required=True,
| type=int,
| help="ark index",
| )
| parser.add_argument(
| "--output-dir",
| "-o",
| default=False,
| required=True,
| type=str,
| help="output dir",
| )
| parser.add_argument(
| "--window-type",
| default="hamming",
| required=False,
| type=str,
| help="window type"
| )
| return parser
|
|
| def main():
| parser = get_parser()
| args = parser.parse_args()
|
| ark_file = args.output_dir + "/ark/feats." + str(args.ark_index) + ".ark"
| scp_file = args.output_dir + "/ark/feats." + str(args.ark_index) + ".scp"
| text_file = args.output_dir + "/txt/text." + str(args.ark_index) + ".txt"
| feats_shape_file = args.output_dir + "/ark/len." + str(args.ark_index)
| text_shape_file = args.output_dir + "/txt/len." + str(args.ark_index)
|
| ark_writer = WriteHelper('ark,scp:{},{}'.format(ark_file, scp_file))
| text_writer = open(text_file, 'w')
| feats_shape_writer = open(feats_shape_file, 'w')
| text_shape_writer = open(text_shape_file, 'w')
|
| speed_perturb_list = args.speed_perturb.split(',')
|
| for speed in speed_perturb_list:
| with open(args.wav_lists, 'r', encoding='utf-8') as wavfile:
| with open(args.text_files, 'r', encoding='utf-8') as textfile:
| for wav, text in zip(wavfile, textfile):
| s_w = wav.strip().split()
| wav_id = s_w[0]
| wav_file = s_w[1]
|
| s_t = text.strip().split()
| text_id = s_t[0]
| txt = s_t[1:]
| fbank = compute_fbank(wav_file,
| num_mel_bins=args.dims,
| resample_rate=args.sample_frequency,
| speed=float(speed),
| window_type=args.window_type
| )
| feats_dims = fbank.shape[1]
| feats_lens = fbank.shape[0]
| if feats_lens >= args.max_lengths:
| continue
| txt_lens = len(txt)
| if speed == "1.0":
| wav_id_sp = wav_id
| else:
| wav_id_sp = wav_id + "_sp" + speed
|
| feats_shape_writer.write(wav_id_sp + " " + str(feats_lens) + "," + str(feats_dims) + '\n')
| text_shape_writer.write(wav_id_sp + " " + str(txt_lens) + '\n')
|
| text_writer.write(wav_id_sp + " " + " ".join(txt) + '\n')
| ark_writer(wav_id_sp, fbank)
|
|
| if __name__ == '__main__':
| main()
|
|
