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import torch
import numpy as np
import torchaudio
def get_mel_from_wav(audio, _stft):
audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
audio = torch.autograd.Variable(audio, requires_grad=False)
melspec, log_magnitudes_stft, energy = _stft.mel_spectrogram(audio)
melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
log_magnitudes_stft = (
torch.squeeze(log_magnitudes_stft, 0).numpy().astype(np.float32)
)
energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
return melspec, log_magnitudes_stft, energy
def _pad_spec(fbank, target_length=1024):
n_frames = fbank.shape[0]
p = target_length - n_frames
# cut and pad
if p > 0:
m = torch.nn.ZeroPad2d((0, 0, 0, p))
fbank = m(fbank)
elif p < 0:
fbank = fbank[0:target_length, :]
if fbank.size(-1) % 2 != 0:
fbank = fbank[..., :-1]
return fbank
def pad_wav(waveform, segment_length):
waveform_length = waveform.shape[-1]
assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
if segment_length is None or waveform_length == segment_length:
return waveform
elif waveform_length > segment_length:
return waveform[:segment_length]
elif waveform_length < segment_length:
temp_wav = np.zeros((1, segment_length))
temp_wav[:, :waveform_length] = waveform
return temp_wav
def normalize_wav(waveform):
waveform = waveform - np.mean(waveform)
waveform = waveform / (np.max(np.abs(waveform)) + 1e-8)
return waveform * 0.5
def read_wav_file(filename, segment_length):
# waveform, sr = librosa.load(filename, sr=None, mono=True) # 4 times slower
waveform, sr = torchaudio.load(filename) # Faster!!!
waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)
waveform = waveform.numpy()[0, ...]
waveform = normalize_wav(waveform)
waveform = waveform[None, ...]
waveform = pad_wav(waveform, segment_length)
waveform = waveform / np.max(np.abs(waveform))
waveform = 0.5 * waveform
return waveform
def wav_to_fbank(filename, target_length=1024, fn_STFT=None):
assert fn_STFT is not None
# mixup
waveform = read_wav_file(filename, target_length * 160) # hop size is 160
waveform = waveform[0, ...]
waveform = torch.FloatTensor(waveform)
fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
fbank = torch.FloatTensor(fbank.T)
log_magnitudes_stft = torch.FloatTensor(log_magnitudes_stft.T)
fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
log_magnitudes_stft, target_length
)
return fbank, log_magnitudes_stft, waveform
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