From 2cbebbb33435dc490b6a092f2e5903c7f0e3e33c Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 31 一月 2023 17:54:02 +0800
Subject: [PATCH] Merge pull request #52 from alibaba-damo-academy/dev

---
 funasr/bin/asr_inference_paraformer.py |    8 +++++---
 1 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py
index a50e038..cb140eb 100644
--- a/funasr/bin/asr_inference_paraformer.py
+++ b/funasr/bin/asr_inference_paraformer.py
@@ -169,7 +169,7 @@
         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}")
@@ -227,6 +227,8 @@
         pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
                                                                         predictor_outs[2], predictor_outs[3]
         pre_token_length = pre_token_length.round().long()
+        if torch.max(pre_token_length) < 1:
+            return []
         decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
         decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
@@ -394,7 +396,7 @@
 #         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]
@@ -621,7 +623,7 @@
             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]

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