| import torch
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| import torchaudio
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| import random
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| import itertools
|
| import numpy as np
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|
|
|
|
|
|
| def normalize_wav(waveform):
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| waveform = waveform - torch.mean(waveform)
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| waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
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| return waveform * 0.5
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|
|
|
|
| def pad_wav(waveform, segment_length):
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| waveform_length = len(waveform)
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|
|
| if segment_length is None or waveform_length == segment_length:
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| return waveform
|
| elif waveform_length > segment_length:
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| return waveform[:segment_length]
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| else:
|
| pad_wav = torch.zeros(segment_length - waveform_length).to(waveform.device)
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| waveform = torch.cat([waveform, pad_wav])
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| return waveform
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|
|
|
|
| def _pad_spec(fbank, target_length=1024):
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| batch, n_frames, channels = fbank.shape
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| p = target_length - n_frames
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| if p > 0:
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| pad = torch.zeros(batch, p, channels).to(fbank.device)
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| fbank = torch.cat([fbank, pad], 1)
|
| elif p < 0:
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| fbank = fbank[:, :target_length, :]
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|
|
| if channels % 2 != 0:
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| fbank = fbank[:, :, :-1]
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|
|
| return fbank
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|
|
|
|
| def read_wav_file(filename, segment_length):
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| waveform, sr = torchaudio.load(filename)
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| waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)[0]
|
| try:
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| waveform = normalize_wav(waveform)
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| except:
|
| print ("Exception normalizing:", filename)
|
| waveform = torch.ones(160000)
|
| waveform = pad_wav(waveform, segment_length).unsqueeze(0)
|
| waveform = waveform / torch.max(torch.abs(waveform))
|
| waveform = 0.5 * waveform
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| return waveform
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|
|
|
|
| def get_mel_from_wav(audio, _stft):
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| audio = torch.nan_to_num(torch.clip(audio, -1, 1))
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| audio = torch.autograd.Variable(audio, requires_grad=False)
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| melspec, log_magnitudes_stft, energy = _stft.mel_spectrogram(audio)
|
| return melspec, log_magnitudes_stft, energy
|
|
|
|
|
| def wav_to_fbank(paths, target_length=1024, fn_STFT=None):
|
| assert fn_STFT is not None
|
|
|
| waveform = torch.cat([read_wav_file(path, target_length * 160) for path in paths], 0)
|
|
|
| fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
|
| fbank = fbank.transpose(1, 2)
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| log_magnitudes_stft = log_magnitudes_stft.transpose(1, 2)
|
|
|
| fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
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| log_magnitudes_stft, target_length
|
| )
|
|
|
| return fbank, log_magnitudes_stft, waveform
|
|
|
| def wav_to_fbank2(waveform, target_length=-1, fn_STFT=None):
|
| assert fn_STFT is not None
|
|
|
| fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
|
| fbank = fbank.transpose(1, 2)
|
| log_magnitudes_stft = log_magnitudes_stft.transpose(1, 2)
|
|
|
|
|
| if(target_length>0):
|
| fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
|
| log_magnitudes_stft, target_length
|
| )
|
|
|
| return fbank, log_magnitudes_stft, waveform
|
|
|
|
|
| def uncapitalize(s):
|
| if s:
|
| return s[:1].lower() + s[1:]
|
| else:
|
| return ""
|
|
|
|
|
