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| import torch | |
| from torch import Tensor | |
| import torch.nn as nn | |
| import torchaudio | |
| class LinearSpectrogram(nn.Module): | |
| def __init__(self, n_fft, win_length, hop_length, pad, center, pad_mode): | |
| super().__init__() | |
| self.n_fft = n_fft | |
| self.win_length = win_length | |
| self.hop_length = hop_length | |
| self.pad = pad | |
| self.center = center | |
| self.pad_mode = pad_mode | |
| self.register_buffer("window", torch.hann_window(win_length)) | |
| def forward(self, waveform: Tensor) -> Tensor: | |
| if waveform.ndim == 3: | |
| waveform = waveform.squeeze(1) | |
| waveform = torch.nn.functional.pad(waveform.unsqueeze(1), (self.pad, self.pad), self.pad_mode).squeeze(1) | |
| spec = torch.stft(waveform, self.n_fft, self.hop_length, self.win_length, self.window, self.center, self.pad_mode, False, True, True) | |
| spec = torch.view_as_real(spec) | |
| spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
| return spec | |
| class LogMelSpectrogram(nn.Module): | |
| def __init__(self, sample_rate, n_fft, win_length, hop_length, f_min, f_max, pad, n_mels, center, pad_mode, mel_scale): | |
| super().__init__() | |
| self.sample_rate = sample_rate | |
| self.n_fft = n_fft | |
| self.win_length = win_length | |
| self.hop_length = hop_length | |
| self.f_min = f_min | |
| self.f_max = f_max | |
| self.pad = pad | |
| self.n_mels = n_mels | |
| self.center = center | |
| self.pad_mode = pad_mode | |
| self.mel_scale = mel_scale | |
| self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, pad, center, pad_mode) | |
| self.mel_scale = torchaudio.transforms.MelScale(n_mels, sample_rate, f_min, f_max, (n_fft//2)+1, mel_scale, mel_scale) | |
| def compress(self, x: Tensor) -> Tensor: | |
| return torch.log(torch.clamp(x, min=1e-5)) | |
| def decompress(self, x: Tensor) -> Tensor: | |
| return torch.exp(x) | |
| def forward(self, x: Tensor) -> Tensor: | |
| linear_spec = self.spectrogram(x) | |
| x = self.mel_scale(linear_spec) | |
| x = self.compress(x) | |
| return x | |
| def load_and_resample_audio(audio_path, target_sr, device='cpu') -> Tensor: | |
| try: | |
| y, sr = torchaudio.load(audio_path) | |
| except Exception as e: | |
| print(str(e)) | |
| return None | |
| y.to(device) | |
| # Convert to mono | |
| if y.size(0) > 1: | |
| y = y[0, :].unsqueeze(0) # shape: [2, time] -> [time] -> [1, time] | |
| # resample audio to target sample_rate | |
| if sr != target_sr: | |
| y = torchaudio.functional.resample(y, sr, target_sr) | |
| return y |