minor fixes in the CTC decoding code (#1338)

k2-fsa · Oct 25, 2023 · 770c495 · 770c495
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diff --git a/egs/multi_zh-hans/ASR/RESULTS.md b/egs/multi_zh-hans/ASR/RESULTS.md
@@ -33,8 +33,8 @@ Character Error Rates (CERs) listed below are produced by the checkpoint of the
 
 | Datasets | aidatatang _200zh | aidatatang _200zh | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech | WenetSpeech | WenetSpeech |
 |--------------------------------|------------------------------|-------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|--------------------|-------------------------|---------------------|
-|  Zipformer  CER   (%) | dev | test | eval| test | dev | test | dev| test | test | dev| test | dev phase1 | dev phase2 | test | dev | test meeting | test net |
-| CTC Decoding | 14.57 | 15.26 | 72.85 | 69.70 | 12.87 | 13.76 | 23.56 | 25.55 | 71.75 | 22.35 | 19.34 | 42.38 | 26.90 | 48.71 | 64.88 | 67.29 | 54.24 |
+|  Zipformer  CER   (%) | dev | test | eval | test | dev | test | dev | test | test | dev | test | dev phase1 | dev phase2 | test | dev | test meeting | test net |
+| CTC Decoding | 2.86 | 3.36 | 22.93 | 24.28 | 2.05 | 2.27 | 3.33 | 3.82 | 15.45 | 3.49 | 2.77 | 6.90 | 2.85 | 8.29 | 9.41 | 6.92 | 8.57 |
 | Greedy Search   | 3.36 | 3.83 | 23.90 | 25.18 | 2.77 | 3.08 | 3.70 | 4.04 | 16.13 | 3.77 | 3.15 | 6.88 | 3.14 | 8.08 | 9.04 | 7.19 | 8.17 |
 
 Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-ctc-2023-10-24/

diff --git a/egs/multi_zh-hans/ASR/zipformer/ctc_decode.py b/egs/multi_zh-hans/ASR/zipformer/ctc_decode.py
@@ -379,7 +379,8 @@ def decode_dataset(
             this_batch = []
             assert len(hyps) == len(texts)
             for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
-                ref_words = ref_text.split()
+                ref_words = list(ref_text.replace(" ", ""))
+                hyp_words = list("".join(hyp_words))
                 this_batch.append((cut_id, ref_words, hyp_words))
 
             results[name].extend(this_batch)