readme: # Introduction
  
  ## How to clone this repo
  ```
  sudo apt-get install git-lfs
  git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
  
  
  cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
  git lfs pull
  ```
  
  **Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
  
  The model in this repo is trained using the commit `a8150021e01d34ecbd6198fe03a57eacf47a16f2`.
  
  You can use
  
  ```
  git clone https://github.com/k2-fsa/icefall
  cd icefall
  git checkout a8150021e01d34ecbd6198fe03a57eacf47a16f2
  ```
  to download `icefall`.
  
  You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/a8150021e01d34ecbd6198fe03a57eacf47a16f2/egs/librispeech/ASR/transducer_stateless/train.py#L198>.
  
  In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
  the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
  
  The decoder architecture is modified from
  [Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
  A Conv1d layer is placed right after the input embedding layer.
  
  -----
  
  ## Description
  
  This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
  using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
  and contains only an embedding layer and a Conv1d.
  
  The commands for training are:
  
  ```
  cd egs/librispeech/ASR/
  ./prepare.sh
  export CUDA_VISIBLE_DEVICES="0,1,2,3"
  ./transducer_stateless/train.py \
    --world-size 4 \
    --num-epochs 76 \
    --start-epoch 0 \
    --exp-dir transducer_stateless/exp-full \
    --full-libri 1 \
    --max-duration 300 \
    --lr-factor 5 \
    --bpe-model data/lang_bpe_500/bpe.model \
    --modified-transducer-prob 0.25
  ```
  
  The tensorboard training log can be found at
  <https://tensorboard.dev/experiment/qgvWkbF2R46FYA6ZMNmOjA/>
  
  The command for decoding is:
  ```
  epoch=63
  avg=19
  
  ## greedy search
  for sym in 1 2 3; do
    ./transducer_stateless/decode.py \
      --epoch $epoch \
      --avg $avg \
      --exp-dir transducer_stateless/exp-full \
      --bpe-model ./data/lang_bpe_500/bpe.model \
      --max-duration 100 \
      --max-sym-per-frame $sym
  done
  
  ## modified beam search
  
  ./transducer_stateless/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless/exp-full \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --max-duration 100 \
    --context-size 2 \
    --decoding-method modified_beam_search \
    --beam-size 4
  ```
  
  You can find the decoding log for the above command in this
  repo (in the folder `log`).
  
  The WERs for the test datasets are
  
  |                                     | test-clean | test-other | comment                                  |
  |-------------------------------------|------------|------------|------------------------------------------|
  | greedy search (max sym per frame 1) | 2.67       | 6.67       | --epoch 63, --avg 19, --max-duration 100 |
  | greedy search (max sym per frame 2) | 2.67       | 6.67       | --epoch 63, --avg 19, --max-duration 100 |
  | greedy search (max sym per frame 3) | 2.67       | 6.67       | --epoch 63, --avg 19, --max-duration 100 |
  | modified beam search (beam size 4)  | 2.67       | 6.57       | --epoch 63, --avg 19, --max-duration 100 |
  
  
  # File description
  
  - [log][log], this directory contains the decoding log and decoding results
  - [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
  - [data][data], this directory contains files generated by [prepare.sh][prepare]
  - [exp][exp], this directory contains only one file: `preprained.pt`
  
  `exp/pretrained.pt` is generated by the following command:
  ```
  ./transducer_stateless/export.py \
    --epoch 63 \
    --avg 19 \
    --bpe-model data/lang_bpe_500/bpe.model \
    --exp-dir transducer_stateless/exp-full
  ```
  
  **HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
  just do the following:
  ```
  cp icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/exp/pretrained.pt \
    /path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt
  ```
  and pass `--epoch 999 --avg 1` to `transducer_stateless/decode.py`.
  
  
  [icefall]: https://github.com/k2-fsa/icefall
  [prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
  [exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/exp
  [data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/data
  [test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/test_wavs
  [log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/log
  [icefall]: https://github.com/k2-fsa/icefall
  