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| 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') # blue is gt | |
| plt.vlines(dur_pred[i], 10, 20, colors='r') # red is pred | |
| 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 | |