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Running on Zero
Running on Zero
| from __future__ import annotations | |
| import torch | |
| import torchaudio | |
| class TorchMel(torch.nn.Module): | |
| def __init__(self, | |
| n_fft=640, | |
| n_mel_channels=64, | |
| target_sample_rate=16000, | |
| hop_length=160, | |
| win_length=640): | |
| super().__init__() | |
| self.mel_stft = torchaudio.transforms.MelSpectrogram( | |
| sample_rate=target_sample_rate, | |
| n_fft=n_fft, | |
| win_length=win_length, | |
| hop_length=hop_length, | |
| n_mels=n_mel_channels, | |
| power=1, | |
| center=True, | |
| normalized=False, | |
| norm=None, | |
| ) | |
| def forward(self, waveform): | |
| if len(waveform.shape) == 3: | |
| waveform = waveform.squeeze(1) # 'b 1 nw -> b nw' | |
| assert len(waveform.shape) == 2 | |
| mel = self.mel_stft(waveform) | |
| mel = mel.clamp(min=1e-5).log() | |
| return mel[:,:,:-1].transpose(1,2) | |