readme: ---
  language: "en"
  tags:
  - icefall
  - k2
  - transducer
  - aishell
  - ASR
  - stateless transducer
  - PyTorch
  license: "apache-2.0"
  datasets:
  - aishell
  metrics:
  - WER
  ---
  
  # Introduction
  
  This repo contains pre-trained model using
  <https://github.com/k2-fsa/icefall/pull/219>.
  
  It is trained on [AIShell](https://www.openslr.org/33/) dataset
  using modified transducer from [optimized_transducer](https://github.com/csukuangfj/optimized_transducer).
  
  ## How to clone this repo
  ```
  sudo apt-get install git-lfs
  git clone https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01
  
  cd icefall-aishell-transducer-stateless-modified-2022-03-01
  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 `TODO`.
  
  You can use
  
  ```
  git clone https://github.com/k2-fsa/icefall
  cd icefall
  git checkout TODO
  ```
  to download `icefall`.
  
  You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/TODO/egs/aishell/ASR/transducer_stateless_modified/train.py#L232>.
  
  
  In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
  the decoder contains a 512-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 AIShell 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:
  
  ```bash
  cd egs/aishell/ASR
  ./prepare.sh --stop-stage 6
  
  export CUDA_VISIBLE_DEVICES="0,1,2"
  
  ./transducer_stateless_modified/train.py \
    --world-size 3 \
    --num-epochs 90 \
    --start-epoch 0 \
    --exp-dir transducer_stateless_modified/exp-4 \
    --max-duration 250 \
    --lr-factor 2.0 \
    --context-size 2 \
    --modified-transducer-prob 0.25
  ```
  
  The tensorboard training log can be found at
  <https://tensorboard.dev/experiment/C27M8YxRQCa1t2XglTqlWg>
  
  The commands for decoding are
  
  ```bash
  # greedy search
  for epoch in 64; do
    for avg in 33; do
    ./transducer_stateless_modified-2/decode.py \
      --epoch $epoch \
      --avg $avg \
      --exp-dir transducer_stateless_modified/exp-4 \
      --max-duration 100 \
      --context-size 2 \
      --decoding-method greedy_search \
      --max-sym-per-frame 1
    done
  done
  
  # modified beam search
  for epoch in 64; do
    for avg in 33; do
      ./transducer_stateless_modified/decode.py \
      --epoch $epoch \
      --avg $avg \
      --exp-dir transducer_stateless_modified/exp-4 \
      --max-duration 100 \
      --context-size 2 \
      --decoding-method modified_beam_search \
      --beam-size 4
    done
  done
  ```
  
  You can find the decoding log for the above command in this
  repo (in the folder [log][log]).
  
  The WER for the test dataset is
  
  |                        | test |comment                                                         |
  |------------------------|------|----------------------------------------------------------------|
  | greedy search          | 5.22 |--epoch 64, --avg 33, --max-duration 100, --max-sym-per-frame 1 |
  | modified beam search   | 5.02 |--epoch 64, --avg 33, --max-duration 100  --beam-size 4         |
  
  # 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:
  
  ```bash
  epoch=64
  avg=33
  
  ./transducer_stateless_modified/export.py \
    --exp-dir ./transducer_stateless_modified/exp-4 \
    --lang-dir ./data/lang_char \
    --epoch $epoch \
    --avg $avg
  ```
  
  **HINT**: To use `pretrained.pt` to compute the WER for the `test` dataset,
  just do the following:
  
  ```bash
  cp icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt \
    /path/to/icefall/egs/aishell/ASR/transducer_stateless_modified/exp/epoch-999.pt
  ```
  and pass `--epoch 999 --avg 1` to `transducer_stateless_modified/decode.py`.
  
  
  [icefall]: https://github.com/k2-fsa/icefall
  [prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/aishell/ASR/prepare.sh
  [exp]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/exp
  [data]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/data
  [test_wavs]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/test_wavs
  [log]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/log
  [icefall]: https://github.c