| | 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 |
