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