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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.io import wavfile
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import torch
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from torch.nn import functional as F
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def repeat_expand_2d(content, target_len):
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src_len = content.shape[-1]
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target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
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temp = torch.arange(src_len+1) * target_len / src_len
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current_pos = 0
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for i in range(target_len):
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if i < temp[current_pos+1]:
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target[:, i] = content[:, current_pos]
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else:
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current_pos += 1
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target[:, i] = content[:, current_pos]
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return target
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def save_plot(tensor, savepath):
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plt.style.use('default')
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fig, ax = plt.subplots(figsize=(12, 3))
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im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none')
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plt.colorbar(im, ax=ax)
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plt.tight_layout()
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fig.canvas.draw()
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plt.savefig(savepath)
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plt.close()
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def save_audio(file_path, sampling_rate, audio):
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audio = np.clip(audio.detach().cpu().squeeze().numpy(), -0.999, 0.999)
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wavfile.write(file_path, sampling_rate, (audio * 32767).astype("int16"))
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def minmax_norm_diff(tensor: torch.Tensor, vmax: float = 2.5, vmin: float = -12) -> torch.Tensor:
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tensor = torch.clip(tensor, vmin, vmax)
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tensor = 2 * (tensor - vmin) / (vmax - vmin) - 1
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return tensor
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def reverse_minmax_norm_diff(tensor: torch.Tensor, vmax: float = 2.5, vmin: float = -12) -> torch.Tensor:
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tensor = torch.clip(tensor, -1.0, 1.0)
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tensor = (tensor + 1) / 2
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tensor = tensor * (vmax - vmin) + vmin
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return tensor |
