| | |
| | |
| |
|
| | import glob |
| | import os |
| | import matplotlib |
| | import torch |
| | from torch.nn.utils import weight_norm |
| | matplotlib.use("Agg") |
| | import matplotlib.pylab as plt |
| | from scipy.io.wavfile import write |
| |
|
| | MAX_WAV_VALUE = 32768.0 |
| |
|
| |
|
| | 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): |
| | |
| | audio = audio * MAX_WAV_VALUE |
| | audio = audio.cpu().numpy().astype('int16') |
| | write(path, sr, audio) |