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# Introduction
This recipe includes scripts for training Zipformer model using both English and Chinese datasets.
# Included Training Sets
1. LibriSpeech (English)
2. AiShell-2 (Chinese)
3. TAL-CSASR (Code-Switching, Chinese and English)
|Datset| Number of hours| URL|
|---|---:|---|
|**TOTAL**|2,547|---|
|LibriSpeech|960|https://www.openslr.org/12/|
|AiShell-2|1,000|http://www.aishelltech.com/aishell_2|
|TAL-CSASR|587|https://ai.100tal.com/openData/voice|

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## Results
### Zh-En datasets bpe-based training results (Non-streaming) on Zipformer model
This is the [pull request #1238](https://github.com/k2-fsa/icefall/pull/1265) in icefall.
#### Non-streaming (Byte-Level BPE vocab_size=2000)
Best results (num of params : ~69M):
The training command:
```
./zipformer/train.py \
--world-size 4 \
--num-epochs 35 \
--use-fp16 1 \
--max-duration 1000 \
--num-workers 8
```
The decoding command:
```
for method in greedy_search modified_beam_search fast_beam_search; do
./zipformer/decode.py \
--epoch 34 \
--avg 19 \
--decoding_method $method
done
```
Word Error Rates (WERs) listed below are produced by the checkpoint of the 20th epoch using greedy search and BPE model (# tokens is 2000).
| Datasets | TAL-CSASR | TAL-CSASR |
|----------------------|-----------|-----------|
| Zipformer WER (%) | dev | test |
| greedy_search | 6.65 | 6.69 |
| modified_beam_search | 6.46 | 6.51 |
| fast_beam_search | 6.57 | 6.68 |
Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-zipformer-multi-zh-en-2023-11-22, which is trained on LibriSpeech 960-hour training set (with speed perturbation), TAL-CSASR training set (with speed perturbation) and AiShell-2 (w/o speed perturbation).

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. shared/parse_options.sh || exit 1
vocab_sizes=(
500
2000
)