| | from ..data.utils import PadCrop |
| |
|
| | from torchaudio import transforms as T |
| |
|
| | 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 |
