| import torch |
| import torch.nn as nn |
| from torchaudio import transforms as T |
|
|
|
|
| class PadCrop(nn.Module): |
| def __init__(self, n_samples, randomize=True): |
| super().__init__() |
| self.n_samples = n_samples |
| self.randomize = randomize |
|
|
| def __call__(self, signal): |
| n, s = signal.shape |
| start = 0 if (not self.randomize) else torch.randint(0, max(0, s - self.n_samples) + 1, []).item() |
| end = start + self.n_samples |
| output = signal.new_zeros([n, self.n_samples]) |
| output[:, :min(s, self.n_samples)] = signal[:, start:end] |
| return output |
|
|
|
|
| def set_audio_channels(audio, target_channels): |
| if target_channels == 1: |
| |
| audio = audio.mean(1, keepdim=True) |
| elif target_channels == 2: |
| |
| if audio.shape[1] == 1: |
| audio = audio.repeat(1, 2, 1) |
| elif audio.shape[1] > 2: |
| audio = audio[:, :2, :] |
| return audio |
|
|
| def prepare_audio(audio, in_sr, target_sr, target_length, target_channels, device): |
| |
| audio = audio.to(device) |
|
|
| if in_sr != target_sr: |
| resample_tf = T.Resample(in_sr, target_sr).to(device) |
| audio = resample_tf(audio) |
|
|
| audio = PadCrop(target_length, randomize=False)(audio) |
|
|
| |
| if audio.dim() == 1: |
| audio = audio.unsqueeze(0).unsqueeze(0) |
| elif audio.dim() == 2: |
| audio = audio.unsqueeze(0) |
|
|
| audio = set_audio_channels(audio, target_channels) |
|
|
| return audio |
