| from ..data.utils import PadCrop | |
| from torchaudio import transforms as T | |
| def set_audio_channels(audio, target_channels): | |
| if target_channels == 1: | |
| # Convert to mono | |
| audio = audio.mean(1, keepdim=True) | |
| elif target_channels == 2: | |
| # Convert to stereo | |
| 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) | |
| # Add batch dimension | |
| 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 |