| | 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 |
| |
|
