| | |
|---|
| | | 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}") |
|---|
| | |
|---|
| | | 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] |
|---|
