LoganLiu66

Hi, I have the same problem, what should I do to fix this bug, thanks!

> I suggest to use the lastest recipe `zipformer` instead. In the old recipe `pruned_transducer_stateless7_streaming`, there might be some issues when doing the chunk-wise forward for the first chunks, since...

> > > I suggest to use the lastest recipe `zipformer` instead. In the old recipe `pruned_transducer_stateless7_streaming`, there might be some issues when doing the chunk-wise forward for the first...

``` python ./pruned_transducer_stateless7_streaming/decode.py \ --epoch 999 \ --avg 1 \ --use-averaged-model 0 \ --beam-size 4 \ --exp-dir ${exp_dir} \ --lang-dir ${lang_dir} \ --max-duration 600 \ --decode-chunk-len 32 \ --decoding-method greedy_search...

It seems to be an overall deterioration in performance. decode.py ``` %WER = 5.48 Errors: 46 insertions, 99 deletions, 231 substitutions, over 6862 reference words (6532 correct) ``` streaming_decode.py ```...

> please have a look at the errs-* file and see if there are any error patterns. It doesn't seem to have an error pattern.

> @LoganLiu66 Did you retrain the model when changing the bpe model? Please check that if the tokens.txt files for two bpe models are the same? I train two models...

After reducing `--beam` from 20.0 to 2.0 and `--max-contexts` from 8 to 2, it seems to be better(WER from 12.29 to 7.81) , but still higher than `greedy_search`

I first train a unigram subword model by using spm tool with setting `model_type="unigram"`, and use this model to tokenize input. But I find many words are recognized to \,...

The vocab size is set to 5000. I set `character_coverage=0.98` in unigram because RuntimeError occurs when training `[Vocabulary size is smaller than required_chars. 5000 vs 5092. Increase vocab_size or decrease...