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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=2.0):
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(f"Loading '{filepath}'")
checkpoint_dict = torch.load(filepath, map_location=device)
print("Complete.")
return checkpoint_dict
def save_checkpoint(filepath, obj):
print(f"Saving checkpoint to {filepath}")
torch.save(obj, filepath)
print("Complete.")
def scan_checkpoint(cp_dir, prefix, renamed_file=None):
# Fallback to original scanning logic first
pattern = os.path.join(cp_dir, prefix + "????????")
cp_list = glob.glob(pattern)
if len(cp_list) > 0:
last_checkpoint_path = sorted(cp_list)[-1]
print(f"[INFO] Resuming from checkpoint: '{last_checkpoint_path}'")
return last_checkpoint_path
# If no pattern-based checkpoints are found, check for renamed file
if renamed_file:
renamed_path = os.path.join(cp_dir, renamed_file)
if os.path.isfile(renamed_path):
print(f"[INFO] Resuming from renamed checkpoint: '{renamed_file}'")
return renamed_path
return None
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)
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