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+## Results
+
+### Zipformer PromptASR (zipformer + PromptASR + BERT text encoder)
+
+#### [zipformer_prompt_asr](./zipformer_prompt_asr)
+
+See <https://github.com/k2-fsa/icefall/pull/1250> for commit history and
+our paper <https://arxiv.org/abs/2309.07414> for more details.
+
+
+
+##### Training on the medium subset, with content & style prompt, no context list
+
+You can find a pre-trained model, training logs, decoding logs, and decoding results at: <>
+
+Number of model parameters:
+
+| decoding method      | lh-test-clean | lh-test-other | comment             |
+|----------------------|---------------|---------------|---------------------|
+| modified_beam_search        |   2.64        |    5.55       |    --pre-text-transform mixed-punc --style-text-transform mixed-punc       |
+| modified_beam_search        |   2.82        |    6.03       |    --pre-text-transform upper-no-punc --style-text-transform upper-no-punc       |
+| modified_beam_search        |   2.64        |    5.55       |    --pre-text-transform mixed-punc --style-text-transform mixed-punc       |
+
+
+The training command is:
+
+```bash
+causal=0
+subset=medium
+memory_dropout_rate=0.05
+text_encoder_type=BERT
+
+python ./zipformer_prompt_asr/train_bert_encoder.py \
+    --world-size 4 \
+    --start-epoch 1 \
+    --num-epochs 60 \
+    --exp-dir ./zipformer_prompt_asr/exp \
+    --use-fp16 True \
+    --memory-dropout-rate $memory_dropout_rate \
+    --causal $causal \
+    --subset $subset \
+    --manifest-dir data/fbank \
+    --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+    --max-duration 1000 \
+    --text-encoder-type $text_encoder_type \
+    --use-context-list 0 \
+    --top-k $top_k \
+    --use-style-prompt 1
+```
+
+##### Training on the medium subset, with content & style prompt, with context list
+
+You can find a pre-trained model, training logs, decoding logs, and decoding results at: <>
+
+Number of model parameters:
+
+*Utterance-level biasing:*
+
+| decoding method      | lh-test-clean | lh-test-other | comment             |
+|----------------------|---------------|---------------|---------------------|
+| modified_beam_search        |   3.11        |    6.79       |    --use-pre-text 0 --use-style-prompt 0    |
+| modified_beam_search        |   2.82        |    6.03       |    --pre-text-transform upper-no-punc --style-text-transform upper-no-punc       |
+| modified_beam_search        |   2.64        |    5.55       |    --pre-text-transform mixed-punc --style-text-transform mixed-punc       |
+
+*Word-level biasing:*
+
+The results are reported on LibriSpeech test-sets using the biasing list provided from <https://arxiv.org/abs/2104.02194>. You need to set `--use-ls-test-set 1` for the following table.
+
+
+| decoding method      | ls-test-clean | ls-test-other | comment             |
+|----------------------|---------------|---------------|---------------------|
+| modified_beam_search        |   2.69        |    5.28       |    --use-pre-text 0 --use-style-prompt 0    |
+| modified_beam_search        |   2.32        |    4.77       |    --use-ls-context-list 1 --pre-text-transform mixed-punc --style-text-transform mixed-punc --ls-distractors 0       |
+| modified_beam_search        |   2.36        |    4.91       |    --use-ls-context-list 1 --pre-text-transform mixed-punc --style-text-transform mixed-punc --ls-distractors 100       |
+
+
+
+Note that to train this model, please first run `prepare_prompt_asr.sh` to prepare a
+manifest containing context words.
+
+The training command is:
+
+```bash
+
+causal=0
+subset=medium
+memory_dropout_rate=0.05
+text_encoder_type=BERT
+
+# prepare the required data for context biasing training & decoding
+./prepare_prompt_asr.sh --stage 0 --stop_stage 1
+
+python ./zipformer_prompt_asr/train_bert_encoder.py \
+    --world-size 4 \
+    --start-epoch 1 \
+    --num-epochs 60 \
+    --exp-dir ./zipformer_prompt_asr/exp \
+    --use-fp16 True \
+    --memory-dropout-rate $memory_dropout_rate \
+    --causal $causal \
+    --subset $subset \
+    --manifest-dir data/fbank \
+    --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+    --max-duration 1000 \
+    --text-encoder-type $text_encoder_type \
+    --use-context-list 1 \
+    --top-k 10000 \
+    --use-style-prompt 1
+```