| import matplotlib.pyplot as plt
|
| import numpy as np
|
| import torch
|
|
|
| LINE_COLORS = ['w', 'r', 'y', 'cyan', 'm', 'b', 'lime']
|
|
|
|
|
| def spec_to_figure(spec, vmin=None, vmax=None):
|
| if isinstance(spec, torch.Tensor):
|
| spec = spec.cpu().numpy()
|
| fig = plt.figure(figsize=(12, 6))
|
| plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
|
| return fig
|
|
|
|
|
| def spec_f0_to_figure(spec, f0s, figsize=None):
|
| max_y = spec.shape[1]
|
| if isinstance(spec, torch.Tensor):
|
| spec = spec.detach().cpu().numpy()
|
| f0s = {k: f0.detach().cpu().numpy() for k, f0 in f0s.items()}
|
| f0s = {k: f0 / 10 for k, f0 in f0s.items()}
|
| fig = plt.figure(figsize=(12, 6) if figsize is None else figsize)
|
| plt.pcolor(spec.T)
|
| for i, (k, f0) in enumerate(f0s.items()):
|
| plt.plot(f0.clip(0, max_y), label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.8)
|
| plt.legend()
|
| return fig
|
|
|
|
|
| def dur_to_figure(dur_gt, dur_pred, txt):
|
| dur_gt = dur_gt.long().cpu().numpy()
|
| dur_pred = dur_pred.long().cpu().numpy()
|
| dur_gt = np.cumsum(dur_gt)
|
| dur_pred = np.cumsum(dur_pred)
|
| fig = plt.figure(figsize=(12, 6))
|
| for i in range(len(dur_gt)):
|
| shift = (i % 8) + 1
|
| plt.text(dur_gt[i], shift, txt[i])
|
| plt.text(dur_pred[i], 10 + shift, txt[i])
|
| plt.vlines(dur_gt[i], 0, 10, colors='b')
|
| plt.vlines(dur_pred[i], 10, 20, colors='r')
|
| return fig
|
|
|
|
|
| def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None):
|
| fig = plt.figure()
|
| f0_gt = f0_gt.cpu().numpy()
|
| plt.plot(f0_gt, color='r', label='gt')
|
| if f0_cwt is not None:
|
| f0_cwt = f0_cwt.cpu().numpy()
|
| plt.plot(f0_cwt, color='b', label='cwt')
|
| if f0_pred is not None:
|
| f0_pred = f0_pred.cpu().numpy()
|
| plt.plot(f0_pred, color='green', label='pred')
|
| plt.legend()
|
| return fig
|
|
|
