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Running
on
Zero
| # Copyright 2025 ByteDance and/or its affiliates. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| 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, figsize=(12, 6)): | |
| if isinstance(spec, torch.Tensor): | |
| spec = spec.cpu().numpy() | |
| H = spec.shape[1] // 2 | |
| fig = plt.figure(figsize=figsize) | |
| 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') # blue is gt | |
| 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') # red is pred | |
| plt.xlim(0, max(dur_gt[-1], dur_pred[-1])) | |
| if f0s is not None: | |
| ax = plt.gca() | |
| ax2 = ax.twinx() | |
| # ax.set_xticks() | |
| 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') # blue is gt | |
| # plt.xlim(0, dur_gt[-1]) | |
| return fig | |