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Running
on
Zero
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from torchmetrics import CharErrorRate | |
| def extract_cer( | |
| model, | |
| **kwargs, | |
| ): | |
| """Compute Character Error Rate (CER) between the predicted and the ground truth audio. | |
| content_gt: the ground truth content. | |
| audio_ref: path to the ground truth audio. | |
| audio_deg: path to the predicted audio. | |
| mode: "gt_content" computes the CER between the predicted content obtained from the whisper model and the ground truth content. | |
| both content_gt and audio_deg are needed. | |
| "gt_audio" computes the CER between the extracted ground truth and predicted contents obtained from the whisper model. | |
| both audio_ref and audio_deg are needed. | |
| """ | |
| kwargs = kwargs["kwargs"] | |
| mode = kwargs["intelligibility_mode"] | |
| language = kwargs["language"] | |
| cer = CharErrorRate() | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| cer = cer.to(device) | |
| # Get ground truth content | |
| if mode == "gt_content": | |
| content_gt = kwargs["content_gt"] | |
| audio_deg = kwargs["audio_deg"] | |
| if language == "chinese": | |
| prompt = "以下是普通话的句子" | |
| result_deg = model.transcribe( | |
| audio_deg, language="zh", verbose=True, initial_prompt=prompt | |
| ) | |
| else: | |
| result_deg = model.transcribe(audio_deg, verbose=True) | |
| elif mode == "gt_audio": | |
| audio_ref = kwargs["audio_ref"] | |
| audio_deg = kwargs["audio_deg"] | |
| if language == "chinese": | |
| prompt = "以下是普通话的句子" | |
| result_ref = model.transcribe( | |
| audio_ref, language="zh", verbose=True, initial_prompt=prompt | |
| ) | |
| result_deg = model.transcribe( | |
| audio_deg, language="zh", verbose=True, initial_prompt=prompt | |
| ) | |
| else: | |
| result_ref = model.transcribe(audio_deg, verbose=True) | |
| result_deg = model.transcribe(audio_deg, verbose=True) | |
| content_gt = result_ref["text"] | |
| content_gt = content_gt.replace(" ", "") | |
| content_gt = content_gt.replace(".", "") | |
| content_gt = content_gt.replace("'", "") | |
| content_gt = content_gt.replace("-", "") | |
| content_gt = content_gt.replace(",", "") | |
| content_gt = content_gt.replace("!", "") | |
| content_gt = content_gt.lower() | |
| # Get predicted truth content | |
| content_pred = result_deg["text"] | |
| content_pred = content_pred.replace(" ", "") | |
| content_pred = content_pred.replace(".", "") | |
| content_pred = content_pred.replace("'", "") | |
| content_pred = content_pred.replace("-", "") | |
| content_pred = content_pred.replace(",", "") | |
| content_pred = content_pred.replace("!", "") | |
| content_pred = content_pred.lower() | |
| return cer(content_pred, content_gt).detach().cpu().numpy().tolist() | |