| import matplotlib |
|
|
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| import numpy as np |
| import torch |
|
|
| LINE_COLORS = ['w', 'r', 'orange', 'k', 'cyan', 'm', 'b', 'lime', 'g', 'brown', 'navy'] |
|
|
|
|
| def spec_to_figure(spec, vmin=None, vmax=None, title='', f0s=None, dur_info=None): |
| if isinstance(spec, torch.Tensor): |
| spec = spec.cpu().numpy() |
| H = spec.shape[1] // 2 |
| fig = plt.figure(figsize=(12, 6)) |
| plt.title(title) |
| plt.pcolor(spec.T, vmin=vmin, vmax=vmax) |
|
|
| if dur_info is not None: |
| assert isinstance(dur_info, dict) |
| txt = dur_info['txt'] |
| dur_gt = dur_info['dur_gt'] |
| if isinstance(dur_gt, torch.Tensor): |
| dur_gt = dur_gt.cpu().numpy() |
| dur_gt = np.cumsum(dur_gt).astype(int) |
| for i in range(len(dur_gt)): |
| shift = (i % 8) + 1 |
| plt.text(dur_gt[i], shift * 4, txt[i]) |
| plt.vlines(dur_gt[i], 0, H // 2, colors='b') |
| plt.xlim(0, dur_gt[-1]) |
| if 'dur_pred' in dur_info: |
| dur_pred = dur_info['dur_pred'] |
| if isinstance(dur_pred, torch.Tensor): |
| dur_pred = dur_pred.cpu().numpy() |
| dur_pred = np.cumsum(dur_pred).astype(int) |
| for i in range(len(dur_pred)): |
| shift = (i % 8) + 1 |
| plt.text(dur_pred[i], H + shift * 4, txt[i]) |
| plt.vlines(dur_pred[i], H, H * 1.5, colors='r') |
| plt.xlim(0, max(dur_gt[-1], dur_pred[-1])) |
| if f0s is not None: |
| ax = plt.gca() |
| ax2 = ax.twinx() |
| |
|
|
| if not isinstance(f0s, dict): |
| f0s = {'f0': f0s} |
| for i, (k, f0) in enumerate(f0s.items()): |
| if f0 is not None: |
| if isinstance(f0, torch.Tensor): |
| f0 = f0.cpu().numpy() |
| ax2.plot( |
| np.arange(len(f0)) + 0.5, f0, label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.5) |
| ax2.set_ylim(0, 1000) |
| ax2.legend() |
| return fig |
|
|
|
|
| def align_to_figure(align, dur_info): |
| if isinstance(align, torch.Tensor): |
| align = align.cpu().numpy() |
| H = align.shape[1] |
| fig = plt.figure(figsize=(12, 6)) |
| plt.pcolor(align.T, vmin=0, vmax=1) |
| if dur_info is not None: |
| assert isinstance(dur_info, dict) |
| txt = dur_info['txt'] |
| dur_gt = dur_info['dur_gt'] |
| if isinstance(dur_gt, torch.Tensor): |
| dur_gt = dur_gt.cpu().numpy() |
| dur_gt = np.cumsum(dur_gt).astype(int) // 2 |
| for i in range(len(dur_gt)): |
| plt.text(dur_gt[i], i, txt[i], color='red') |
| plt.vlines(dur_gt[i], 0, H, colors='b') |
| |
| return fig |
|
|
