| import matplotlib |
|
|
| matplotlib.use('Agg') |
| 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, title=''): |
| if isinstance(spec, torch.Tensor): |
| spec = spec.cpu().numpy() |
| fig = plt.figure(figsize=(12, 6)) |
| plt.title(title) |
| 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, mels=None, vmin=-5.5, vmax=1): |
| dur_gt = dur_gt.cpu().numpy() |
| dur_pred = dur_pred.cpu().numpy() |
| dur_gt = np.cumsum(dur_gt).astype(int) |
| dur_pred = np.cumsum(dur_pred).astype(int) |
| fig = plt.figure(figsize=(12, 6)) |
| for i in range(len(dur_gt)): |
| shift = (i % 8) + 1 |
| plt.text(dur_gt[i], shift * 4, txt[i]) |
| plt.text(dur_pred[i], 40 + shift * 4, txt[i]) |
| plt.vlines(dur_gt[i], 0, 40, colors='b') |
| plt.vlines(dur_pred[i], 40, 80, colors='r') |
| plt.xlim(0, max(dur_gt[-1], dur_pred[-1])) |
| if mels is not None: |
| mels = mels.cpu().numpy() |
| plt.pcolor(mels.T, vmin=vmin, vmax=vmax) |
| return fig |
|
|
|
|
| def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None): |
| fig = plt.figure(figsize=(12, 8)) |
| 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 |
|
|
