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