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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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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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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", |
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interpolation='none') |
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plt.colorbar(im, ax=ax) |
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fig.canvas.draw() |
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plt.close() |
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return fig |
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def plot_spectrogram_clipped(spectrogram, clip_max=2.): |
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fig, ax = plt.subplots(figsize=(10, 2)) |
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im = ax.imshow(spectrogram, aspect="auto", origin="lower", |
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interpolation='none', vmin=1e-6, vmax=clip_max) |
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plt.colorbar(im, ax=ax) |
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fig.canvas.draw() |
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plt.close() |
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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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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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def get_padding(kernel_size, dilation=1): |
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return int((kernel_size*dilation - dilation)/2) |
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def load_checkpoint(filepath, device): |
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assert os.path.isfile(filepath) |
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print("Loading '{}'".format(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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def save_checkpoint(filepath, obj): |
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print("Saving checkpoint to {}".format(filepath)) |
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torch.save(obj, filepath) |
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print("Complete.") |
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def scan_checkpoint(cp_dir, prefix): |
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pattern = os.path.join(cp_dir, prefix + '????????') |
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cp_list = glob.glob(pattern) |
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if len(cp_list) == 0: |
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return None |
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return sorted(cp_list)[-1] |
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def save_audio(audio, path, sr): |
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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) |
