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