mirror of
https://github.com/AIGC-Audio/AudioGPT.git
synced 2026-07-10 20:40:45 +02:00
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import matplotlib
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matplotlib.use('Agg')
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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', 'orange', 'k', 'cyan', 'm', 'b', 'lime', 'g', 'brown', 'navy']
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def spec_to_figure(spec, vmin=None, vmax=None, title='', f0s=None, dur_info=None):
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if isinstance(spec, torch.Tensor):
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spec = spec.cpu().numpy()
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H = spec.shape[1] // 2
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fig = plt.figure(figsize=(12, 6))
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plt.title(title)
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plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
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if dur_info is not None:
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assert isinstance(dur_info, dict)
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txt = dur_info['txt']
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dur_gt = dur_info['dur_gt']
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if isinstance(dur_gt, torch.Tensor):
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dur_gt = dur_gt.cpu().numpy()
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dur_gt = np.cumsum(dur_gt).astype(int)
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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 * 4, txt[i])
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plt.vlines(dur_gt[i], 0, H // 2, colors='b') # blue is gt
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plt.xlim(0, dur_gt[-1])
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if 'dur_pred' in dur_info:
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dur_pred = dur_info['dur_pred']
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if isinstance(dur_pred, torch.Tensor):
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dur_pred = dur_pred.cpu().numpy()
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dur_pred = np.cumsum(dur_pred).astype(int)
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for i in range(len(dur_pred)):
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shift = (i % 8) + 1
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plt.text(dur_pred[i], H + shift * 4, txt[i])
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plt.vlines(dur_pred[i], H, H * 1.5, colors='r') # red is pred
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plt.xlim(0, max(dur_gt[-1], dur_pred[-1]))
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if f0s is not None:
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ax = plt.gca()
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ax2 = ax.twinx()
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if not isinstance(f0s, dict):
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f0s = {'f0': f0s}
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for i, (k, f0) in enumerate(f0s.items()):
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if isinstance(f0, torch.Tensor):
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f0 = f0.cpu().numpy()
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ax2.plot(f0, label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.5)
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ax2.set_ylim(0, 1000)
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ax2.legend()
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return fig
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