File size: 3,141 Bytes
8337fa0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import torch
import torchaudio
import random
import itertools
import numpy as np
def normalize_wav(waveform):
waveform = waveform - torch.mean(waveform)
waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
return waveform * 0.5
def pad_wav(waveform, segment_length):
waveform_length = len(waveform)
if segment_length is None or waveform_length == segment_length:
return waveform
elif waveform_length > segment_length:
return waveform[:segment_length]
else:
pad_wav = torch.zeros(segment_length - waveform_length).to(waveform.device)
waveform = torch.cat([waveform, pad_wav])
return waveform
def _pad_spec(fbank, target_length=1024):
batch, n_frames, channels = fbank.shape
p = target_length - n_frames
if p > 0:
pad = torch.zeros(batch, p, channels).to(fbank.device)
fbank = torch.cat([fbank, pad], 1)
elif p < 0:
fbank = fbank[:, :target_length, :]
if channels % 2 != 0:
fbank = fbank[:, :, :-1]
return fbank
def read_wav_file(filename, segment_length):
waveform, sr = torchaudio.load(filename) # Faster!!!
waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)[0]
try:
waveform = normalize_wav(waveform)
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
return waveform
def get_mel_from_wav(audio, _stft):
audio = torch.nan_to_num(torch.clip(audio, -1, 1))
audio = torch.autograd.Variable(audio, requires_grad=False)
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) # hop size is 160
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)
fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
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)
# print(fbank.shape, log_magnitudes_stft.shape)
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 ""
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