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游雁

2023-02-11 0a4e3b7e64e9e095cfdcd4b3c28bde7aa58839e7

 funasr/bin/asr_inference_paraformer_timestamp.py

@@ -98,10 +98,13 @@
        logging.info("asr_train_args: {}".format(asr_train_args))
        asr_model.to(dtype=getattr(torch, dtype)).eval()

        ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
        if asr_model.ctc != None:
            ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
            scorers.update(
                ctc=ctc
            )
        token_list = asr_model.token_list
        scorers.update(
            ctc=ctc,
            length_bonus=LengthBonus(len(token_list)),
        )

@@ -169,7 +172,7 @@
        self.converter = converter
        self.tokenizer = tokenizer
        is_use_lm = lm_weight != 0.0 and lm_file is not None
        if ctc_weight == 0.0 and not is_use_lm:
        if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
            beam_search = None
        self.beam_search = beam_search
        logging.info(f"Beam_search: {self.beam_search}")
@@ -407,7 +410,7 @@
        results = speech2text(**batch)
        if len(results) < 1:
            hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
            results = [[" ", ["<space>"], [2], hyp, 10, 6]] * nbest
            results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
        time_end = time.time()
        forward_time = time_end - time_beg
        lfr_factor = results[0][-1]
@@ -433,7 +436,7 @@
                    ibest_writer["score"][key] = str(hyp.score)
    
                if text is not None:
                    text_postprocessed = postprocess_utils.sentence_postprocess(token)
                    text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                    item = {'key': key, 'value': text_postprocessed}
                    asr_result_list.append(item)
                    finish_count += 1