游雁
2024-01-15 c6361cc2a7e99be802d7d7e81a93e874f0faf5cd
funasr/models/monotonic_aligner/model.py
@@ -41,15 +41,15 @@
        super().__init__()
        if specaug is not None:
            specaug_class = tables.specaug_classes.get(specaug.lower())
            specaug_class = tables.specaug_classes.get(specaug)
            specaug = specaug_class(**specaug_conf)
        if normalize is not None:
            normalize_class = tables.normalize_classes.get(normalize.lower())
            normalize_class = tables.normalize_classes.get(normalize)
            normalize = normalize_class(**normalize_conf)
        encoder_class = tables.encoder_classes.get(encoder.lower())
        encoder_class = tables.encoder_classes.get(encoder)
        encoder = encoder_class(input_size=input_size, **encoder_conf)
        encoder_output_size = encoder.output_size()
        predictor_class = tables.predictor_classes.get(predictor.lower())
        predictor_class = tables.predictor_classes.get(predictor)
        predictor = predictor_class(**predictor_conf)
        self.specaug = specaug
        self.normalize = normalize
@@ -166,7 +166,8 @@
        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
        meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
            
        speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
        speech = speech.to(device=kwargs["device"])
        speech_lengths = speech_lengths.to(device=kwargs["device"])
        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
@@ -188,9 +189,12 @@
            text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
            result_i = {"key": key[i], "text": text_postprocessed,
                                "timestamp": time_stamp_postprocessed,
                                }
            # ibest_writer["token"][key[i]] = " ".join(token)
            ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
            ibest_writer["timestamp_str"][key[i]] = timestamp_str
                                }
            results.append(result_i)
            if ibest_writer:
                # ibest_writer["token"][key[i]] = " ".join(token)
                ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
                ibest_writer["timestamp_str"][key[i]] = timestamp_str
        return results, meta_data