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| # Adapted from https://github.com/jik876/hifi-gan under the MIT license. | |
| # LICENSE is in incl_licenses directory. | |
| import glob | |
| import os | |
| import matplotlib | |
| import torch | |
| from torch.nn.utils import weight_norm | |
| matplotlib.use("Agg") | |
| import matplotlib.pylab as plt | |
| from meldataset import MAX_WAV_VALUE | |
| from scipy.io.wavfile import write | |
| def plot_spectrogram(spectrogram): | |
| fig, ax = plt.subplots(figsize=(10, 2)) | |
| im = ax.imshow(spectrogram, aspect="auto", origin="lower", | |
| interpolation='none') | |
| plt.colorbar(im, ax=ax) | |
| fig.canvas.draw() | |
| plt.close() | |
| return fig | |
| def plot_spectrogram_clipped(spectrogram, clip_max=2.): | |
| fig, ax = plt.subplots(figsize=(10, 2)) | |
| im = ax.imshow(spectrogram, aspect="auto", origin="lower", | |
| interpolation='none', vmin=1e-6, vmax=clip_max) | |
| plt.colorbar(im, ax=ax) | |
| fig.canvas.draw() | |
| plt.close() | |
| return fig | |
| def init_weights(m, mean=0.0, std=0.01): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| m.weight.data.normal_(mean, std) | |
| def apply_weight_norm(m): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| weight_norm(m) | |
| def get_padding(kernel_size, dilation=1): | |
| return int((kernel_size*dilation - dilation)/2) | |
| def load_checkpoint(filepath, device): | |
| assert os.path.isfile(filepath) | |
| print("Loading '{}'".format(filepath)) | |
| checkpoint_dict = torch.load(filepath, map_location=device) | |
| print("Complete.") | |
| return checkpoint_dict | |
| def save_checkpoint(filepath, obj): | |
| print("Saving checkpoint to {}".format(filepath)) | |
| torch.save(obj, filepath) | |
| print("Complete.") | |
| def scan_checkpoint(cp_dir, prefix): | |
| pattern = os.path.join(cp_dir, prefix + '????????') | |
| cp_list = glob.glob(pattern) | |
| if len(cp_list) == 0: | |
| return None | |
| return sorted(cp_list)[-1] | |
| def save_audio(audio, path, sr): | |
| # wav: torch with 1d shape | |
| audio = audio * MAX_WAV_VALUE | |
| audio = audio.cpu().numpy().astype('int16') | |
| write(path, sr, audio) |