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import torch
import torch.nn.functional as F
import matplotlib
import torchaudio.transforms as transforms
matplotlib.use("Agg")
import matplotlib.pylab as plt
class Metric:
def __init__(self):
self.steps = 0
self.value = 0
def update(self, value):
self.steps += 1
self.value += (value - self.value) / self.steps
return self.value
def reset(self):
self.steps = 0
self.value = 0
class LogMelSpectrogram(torch.nn.Module):
def __init__(self):
super().__init__()
self.melspctrogram = transforms.MelSpectrogram(
sample_rate=16000,
n_fft=1024,
win_length=1024,
hop_length=160,
center=False,
power=1.0,
norm="slaney",
onesided=True,
n_mels=128,
mel_scale="slaney",
)
def forward(self, wav):
padding = (1024 - 160) // 2
wav = F.pad(wav, (padding, padding), "reflect")
mel = self.melspctrogram(wav)
logmel = torch.log(torch.clamp(mel, min=1e-5))
return logmel
def save_checkpoint(
checkpoint_dir,
acoustic,
optimizer,
step,
loss,
best,
logger,
):
state = {
"acoustic-model": acoustic.state_dict(),
"optimizer": optimizer.state_dict(),
"step": step,
"loss": loss,
}
checkpoint_dir.mkdir(exist_ok=True, parents=True)
checkpoint_path = checkpoint_dir / f"model-{step}.pt"
torch.save(state, checkpoint_path)
if best:
best_path = checkpoint_dir / "model-best.pt"
torch.save(state, best_path)
logger.info(f"Saved checkpoint: {checkpoint_path.stem}")
def load_checkpoint(
load_path,
acoustic,
optimizer,
rank,
logger,
):
logger.info(f"Loading checkpoint from {load_path}")
checkpoint = torch.load(load_path, map_location={"cuda:0": f"cuda:{rank}"})
acoustic.load_state_dict(checkpoint["acoustic-model"])
if "optimizer" in checkpoint:
optimizer.load_state_dict(checkpoint["optimizer"])
step = checkpoint.get("step", 0)
loss = checkpoint.get("loss", float("inf"))
return step, loss
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
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