Update Zipformer-xl 700M Results on multi-hans-zh (#1694)

* add blank penalty

* update zipformer-xl results

* fix typo

egs/multi_zh-hans/ASR/RESULTS.md

@@ -43,6 +43,66 @@ Fine-tuned models, training logs, decoding logs, tensorboard and decoding result
 are available at
 <https://huggingface.co/yuekai/icefall_asr_multi-hans-zh_whisper>
 
+### Multi Chinese datasets char-based training results (streaming) on zipformer-xl model
+
+#### Streaming (with CTC head)
+
+The training command for extra-large model (num of params : ~700M):
+
+Please use the [script](https://github.com/k2-fsa/icefall/blob/master/egs/speech_llm/ASR_LLM/prepare.sh) to prepare fbank features.
+
+```
+./zipformer/train.py \
+  --world-size 8 \
+  --num-epochs 20 \
+  --use-fp16 1 \
+  --max-duration 1200 \
+  --num-workers 8 \
+  --use-ctc 1 \
+  --exp-dir zipformer/exp-xl \
+  --causal 1 \
+  --num-encoder-layers 2,3,5,6,5,3 \
+  --feedforward-dim 1536,2048,3072,4096,3072,1536 \
+  --encoder-dim 512,768,1024,1536,1024,512 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --decoder-dim 768 --joiner-dim 768 \
+  --value-head-dim 18 \
+  --query-head-dim 48 \
+  --num-heads 4,4,4,8,4,4
+
+```
+
+The decoding command for transducer greedy search:
+
+```
+./zipformer/decode.py \
+  --epoch 999 \
+  --avg 1 \
+  --causal 1 \
+  --use-averaged-model False \
+  --chunk_size -1
+  --left-context-frames -1 \
+  --use-ctc 1 \
+  --exp-dir zipformer/exp-xl \
+  --max-duration 1200 \
+  --num-encoder-layers 2,3,5,6,5,3 \
+  --feedforward-dim 1536,2048,3072,4096,3072,1536 \
+  --encoder-dim 512,768,1024,1536,1024,512 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --decoder-dim 768 --joiner-dim 768 \
+  --value-head-dim 18 \
+  --query-head-dim 48 \
+  --num-heads 4,4,4,8,4,4
+```
+
+Character Error Rates (CERs) listed below are produced by the checkpoint of the 18th epoch using BPE model ( # tokens is 2000, byte fallback enabled).
+
+| Datasets | 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   (%) |  eval | test | dev | test | dev | test | test | dev | test | dev phase1 | dev phase2 | test | dev | test meeting | test net |
+| Transducer Greedy Offline   | 21.67  | 23.43 | 1.22 | 1.31 | 3.17 | 3.27 | 14.64 | 2.42 | 1.99 | 5.00 | 2.29 | 5.98 | 5.15 | 5.85 | 6.89 |
+
+Pre-trained model can be found here : https://huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-xl
 ### Multi Chinese datasets char-based training results (streaming) on zipformer large model
 
 #### Streaming (with CTC head)
@@ -64,6 +124,7 @@ Please use the [script](https://github.com/k2-fsa/icefall/blob/master/egs/speech
   --num-encoder-layers 2,2,4,5,4,2 \
   --feedforward-dim 768,1024,1536,2048,1536,768 \
   --encoder-dim 256,384,512,768,512,256 \
+  --blank-penalty 0.7 \
   --encoder-unmasked-dim 192,192,256,320,256,192
 
 ```

egs/multi_zh-hans/ASR/zipformer/decode.py

@@ -303,6 +303,17 @@ def get_parser():
         fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
     )
 
+    parser.add_argument(
+        "--blank-penalty",
+        type=float,
+        default=0.0,
+        help="""
+        The penalty applied on blank symbol during decoding.
+        Note: It is a positive value that would be applied to logits like
+        this `logits[:, 0] -= blank_penalty` (suppose logits.shape is
+        [batch_size, vocab] and blank id is 0).
+        """,
+    )
     add_model_arguments(parser)
 
     return parser
@@ -431,6 +442,7 @@ def decode_one_batch(
             model=model,
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
+            blank_penalty=params.blank_penalty,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -455,6 +467,7 @@ def decode_one_batch(
                     model=model,
                     encoder_out=encoder_out_i,
                     max_sym_per_frame=params.max_sym_per_frame,
+                    blank_penalty=params.blank_penalty,
                 )
             elif params.decoding_method == "beam_search":
                 hyp = beam_search(
@@ -468,8 +481,9 @@ def decode_one_batch(
                 )
             hyps.append(sp.decode(hyp).split())
 
+    key = f"blank_penalty_{params.blank_penalty}"
     if params.decoding_method == "greedy_search":
-        return {"greedy_search": hyps}
+        return {"greedy_search_" + key: hyps}
     elif "fast_beam_search" in params.decoding_method:
         key = f"beam_{params.beam}_"
         key += f"max_contexts_{params.max_contexts}_"
@@ -657,6 +671,7 @@ def main():
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
 
+    params.suffix += f"-blank-penalty-{params.blank_penalty}"
     if params.use_averaged_model:
         params.suffix += "-use-averaged-model"