debugging

shixian.shi

2023-03-21 3258d2be0ad4944f0c7a359164cc25f2a32504c9

 funasr/bin/asr_inference_paraformer.py

@@ -43,6 +43,7 @@
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.bin.tp_inference import SpeechText2Timestamp


class Speech2Text:
@@ -540,7 +541,8 @@
        ngram_weight: float = 0.9,
        nbest: int = 1,
        num_workers: int = 1,

        timestamp_infer_config: Union[Path, str] = None,
        timestamp_model_file: Union[Path, str] = None,
        **kwargs,
):
    inference_pipeline = inference_modelscope(
@@ -604,6 +606,8 @@
        nbest: int = 1,
        num_workers: int = 1,
        output_dir: Optional[str] = None,
        timestamp_infer_config: Union[Path, str] = None,
        timestamp_model_file: Union[Path, str] = None,
        param_dict: dict = None,
        **kwargs,
):
@@ -660,6 +664,15 @@
        speech2text = Speech2TextExport(**speech2text_kwargs)
    else:
        speech2text = Speech2Text(**speech2text_kwargs)

    if timestamp_model_file is not None:
        speechtext2timestamp = SpeechText2Timestamp(
            timestamp_cmvn_file=cmvn_file,
            timestamp_model_file=timestamp_model_file,
            timestamp_infer_config=timestamp_infer_config,
        )
    else:
        speechtext2timestamp = None

    def _forward(
            data_path_and_name_and_type,
@@ -743,8 +756,16 @@

                key = keys[batch_id]
                for n, result in zip(range(1, nbest + 1), result):
                    # import pdb; pdb.set_trace()
                    text, token, token_int, hyp = result[0], result[1], result[2], result[3]
                    time_stamp = None if len(result) < 5 else result[4]
                    # conduct timestamp prediction here
                    if time_stamp is None and speechtext2timestamp:
                        ts_batch = {}
                        ts_batch['speech'] = batch['speech'][batch_id].squeeze(0)
                        ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]])
                        ts_batch['text_lengths'] = torch.tensor([len(token)])
                        import pdb; pdb.set_trace()
                    # Create a directory: outdir/{n}best_recog
                    if writer is not None:
                        ibest_writer = writer[f"{n}best_recog"]