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|
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| import glob
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| import os
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| import matplotlib
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| import torch
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| from torch.nn.utils import weight_norm
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|
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| matplotlib.use("Agg")
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| import matplotlib.pylab as plt
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| from .meldataset import MAX_WAV_VALUE
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| from scipy.io.wavfile import write
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|
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| def plot_spectrogram(spectrogram):
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| fig, ax = plt.subplots(figsize=(10, 2))
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| im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
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| plt.colorbar(im, ax=ax)
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|
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| fig.canvas.draw()
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| plt.close()
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|
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| return fig
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|
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|
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| def plot_spectrogram_clipped(spectrogram, clip_max=2.0):
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| fig, ax = plt.subplots(figsize=(10, 2))
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| im = ax.imshow(
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| spectrogram,
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| aspect="auto",
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| origin="lower",
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| interpolation="none",
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| vmin=1e-6,
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| vmax=clip_max,
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| )
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| plt.colorbar(im, ax=ax)
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|
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| fig.canvas.draw()
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| plt.close()
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|
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| return fig
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|
|
|
|
| def init_weights(m, mean=0.0, std=0.01):
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| classname = m.__class__.__name__
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| if classname.find("Conv") != -1:
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| m.weight.data.normal_(mean, std)
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|
|
|
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| def apply_weight_norm(m):
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| classname = m.__class__.__name__
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| if classname.find("Conv") != -1:
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| weight_norm(m)
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|
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| def get_padding(kernel_size, dilation=1):
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| return int((kernel_size * dilation - dilation) / 2)
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|
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| def load_checkpoint(filepath, device):
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| assert os.path.isfile(filepath)
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| print(f"Loading '{filepath}'")
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| checkpoint_dict = torch.load(filepath, map_location=device)
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| print("Complete.")
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| return checkpoint_dict
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|
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|
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| def save_checkpoint(filepath, obj):
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| print(f"Saving checkpoint to {filepath}")
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| torch.save(obj, filepath)
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| print("Complete.")
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|
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| def scan_checkpoint(cp_dir, prefix, renamed_file=None):
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| pattern = os.path.join(cp_dir, prefix + "????????")
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| cp_list = glob.glob(pattern)
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|
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| if len(cp_list) > 0:
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| last_checkpoint_path = sorted(cp_list)[-1]
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| print(f"[INFO] Resuming from checkpoint: '{last_checkpoint_path}'")
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| return last_checkpoint_path
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|
|
|
|
| if renamed_file:
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| renamed_path = os.path.join(cp_dir, renamed_file)
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| if os.path.isfile(renamed_path):
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| print(f"[INFO] Resuming from renamed checkpoint: '{renamed_file}'")
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| return renamed_path
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|
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| return None
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|
|
|
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| def save_audio(audio, path, sr):
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|
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| audio = audio * MAX_WAV_VALUE
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| audio = audio.cpu().numpy().astype("int16")
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| write(path, sr, audio)
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|
