Delete dreamvoice/src/utils/.ipynb_checkpoints

dreamvoice/src/utils/.ipynb_checkpoints/__init__-checkpoint.py

@@ -1 +0,0 @@
-from .utils import *

dreamvoice/src/utils/.ipynb_checkpoints/utils-checkpoint.py

@@ -1,76 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.io import wavfile
-import torch
-
-
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
-    """
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-
-def scale_shift(x, scale, shift):
-    return (x+shift) * scale
-
-
-def scale_shift_re(x, scale, shift):
-    return (x/scale) - shift
-
-
-def align_seq(source, target_length, mapping_method='hard'):
-    source_len = source.shape[1]
-    if mapping_method == 'hard':
-        mapping_idx = np.round(np.arange(target_length) * source_len / target_length)
-        output = source[:, mapping_idx]
-    else:
-        # TBD
-        raise NotImplementedError
-
-    return output
-
-
-def save_plot(tensor, savepath):
-    tensor = tensor.squeeze().cpu()
-    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.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
-
-
-if __name__ == "__main__":
-
-    a = torch.rand(2, 10)
-    target_len = 15
-
-    b = align_seq(a, target_len)