diff --git a/egs/multi_zh-hans/ASR/RESULTS.md b/egs/multi_zh-hans/ASR/RESULTS.md index 5133229a7c..15e7896047 100644 --- 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 index a7cd7ce434..5143f945d8 100755 --- 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)