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| #!/usr/bin/env python3 | |
| # Copyright 2023 (authors: Feiteng Li) | |
| # | |
| # See ../../../../LICENSE for clarification regarding multiple authors | |
| # | |
| # 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. | |
| from typing import Dict, List, Tuple, Union | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import torch | |
| def visualize( | |
| predicts: Tuple[torch.Tensor], | |
| batch: Dict[str, Union[List, torch.Tensor]], | |
| output_dir: str, | |
| limit: int = 4, | |
| ) -> None: | |
| text_tokens = batch["text_tokens"].to("cpu").detach().numpy() | |
| text_tokens_lens = batch["text_tokens_lens"].to("cpu").detach().numpy() | |
| audio_features = batch["audio_features"].to("cpu").detach().numpy() | |
| audio_features_lens = ( | |
| batch["audio_features_lens"].to("cpu").detach().numpy() | |
| ) | |
| assert text_tokens.ndim == 2 | |
| utt_ids, texts = batch["utt_id"], batch["text"] | |
| encoder_outputs = predicts[0].to("cpu").type(torch.float32).detach().numpy() | |
| decoder_outputs = predicts[1] | |
| if isinstance(decoder_outputs, list): | |
| decoder_outputs = decoder_outputs[-1] | |
| decoder_outputs = ( | |
| decoder_outputs.to("cpu").type(torch.float32).detach().numpy() | |
| ) | |
| vmin, vmax = 0, 1024 # Encodec | |
| if decoder_outputs.dtype == np.float32: | |
| vmin, vmax = -6, 0 # Fbank | |
| num_figures = 3 | |
| for b, (utt_id, text) in enumerate(zip(utt_ids[:limit], texts[:limit])): | |
| _ = plt.figure(figsize=(14, 8 * num_figures)) | |
| S = text_tokens_lens[b] | |
| T = audio_features_lens[b] | |
| # encoder | |
| plt.subplot(num_figures, 1, 1) | |
| plt.title(f"Text: {text}") | |
| plt.imshow( | |
| X=np.transpose(encoder_outputs[b]), | |
| cmap=plt.get_cmap("jet"), | |
| aspect="auto", | |
| interpolation="nearest", | |
| ) | |
| plt.gca().invert_yaxis() | |
| plt.axvline(x=S - 0.4, linewidth=2, color="r") | |
| plt.xlabel("Encoder Output") | |
| plt.colorbar() | |
| # decoder | |
| plt.subplot(num_figures, 1, 2) | |
| plt.imshow( | |
| X=np.transpose(decoder_outputs[b]), | |
| cmap=plt.get_cmap("jet"), | |
| aspect="auto", | |
| interpolation="nearest", | |
| vmin=vmin, | |
| vmax=vmax, | |
| ) | |
| plt.gca().invert_yaxis() | |
| plt.axvline(x=T - 0.4, linewidth=2, color="r") | |
| plt.xlabel("Decoder Output") | |
| plt.colorbar() | |
| # target | |
| plt.subplot(num_figures, 1, 3) | |
| plt.imshow( | |
| X=np.transpose(audio_features[b]), | |
| cmap=plt.get_cmap("jet"), | |
| aspect="auto", | |
| interpolation="nearest", | |
| vmin=vmin, | |
| vmax=vmax, | |
| ) | |
| plt.gca().invert_yaxis() | |
| plt.axvline(x=T - 0.4, linewidth=2, color="r") | |
| plt.xlabel("Decoder Target") | |
| plt.colorbar() | |
| plt.savefig(f"{output_dir}/{utt_id}.png") | |
| plt.close() | |