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Running
on
Zero
| 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 | |