dataset fixes
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@ -53,7 +53,7 @@ class WavAugmentor:
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if __name__ == '__main__':
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sample, _ = load_wav_to_torch('obama1.wav')
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sample = sample.permute(1,0) / 32768.0
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sample = sample / 32768.0
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aug = WavAugmentor()
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for j in range(10):
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out = aug.augment(sample, 24000)
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@ -40,7 +40,7 @@ class WavfileDataset(torch.utils.data.Dataset):
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if sampling_rate != self.sampling_rate:
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raise ValueError(f"Input sampling rate does not match specified rate {self.sampling_rate}")
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audio_norm = audio / self.max_wav_value
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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audio_norm = audio_norm.unsqueeze(0)
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return audio_norm, audiopath
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def __getitem__(self, index):
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@ -49,22 +49,22 @@ class WavfileDataset(torch.utils.data.Dataset):
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while clip1 is None and clip2 is None:
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# Split audio_norm into two tensors of equal size.
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audio_norm, filename = self.get_audio_for_index(index)
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if audio_norm.shape[0] < self.window * 2:
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if audio_norm.shape[1] < self.window * 2:
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# Try next index. This adds a bit of bias and ideally we'd filter the dataset rather than do this.
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index = (index + 1) % len(self)
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continue
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j = random.randint(0, audio_norm.shape[0] - self.window)
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clip1 = audio_norm[j:j+self.window]
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j = random.randint(0, audio_norm.shape[1] - self.window)
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clip1 = audio_norm[:, j:j+self.window]
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if self.augment:
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clip1 = self.augmentor.augment(clip1, self.sampling_rate)
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j = random.randint(0, audio_norm.shape[0]-self.window)
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clip2 = audio_norm[j:j+self.window]
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j = random.randint(0, audio_norm.shape[1]-self.window)
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clip2 = audio_norm[:, j:j+self.window]
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if self.augment:
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clip2 = self.augmentor.augment(clip2, self.sampling_rate)
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return {
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'clip1': clip1.unsqueeze(0),
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'clip2': clip2.unsqueeze(0),
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'clip1': clip1,
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'clip2': clip2,
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'path': filename,
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}
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