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@ -757,7 +757,6 @@ class SeperateMDXC(SeperateAttributes):
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mix_shape = mix.shape[1]
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pad_size = hop_size - (mix_shape - chunk_size) % hop_size
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mix = torch.cat([torch.zeros(2, chunk_size - hop_size), mix, torch.zeros(2, pad_size + chunk_size - hop_size)], 1)
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mix = mix.to(self.device)
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chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)
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batches = [chunks[i : i + batch_size] for i in range(0, len(chunks), batch_size)]
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@ -768,7 +767,7 @@ class SeperateMDXC(SeperateAttributes):
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cnt = 0
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for batch in batches:
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self.running_inference_progress_bar(len(batches))
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x = model(batch)
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x = model(batch.to(self.device))
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for w in x:
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X[..., cnt * hop_size : cnt * hop_size + chunk_size] += w
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