readme: ---
  language: "en"
  tags:
  - icefall
  - k2
  - transducer
  - librispeech
  - ASR
  - stateless transducer
  - PyTorch
  - RNN-T
  - pruned RNN-T
  - speech recognition
  license: "apache-2.0"
  datasets:
  - librispeech
  metrics:
  - WER
  ---
  
  
  # Introduction
  
  This repo contains pre-trained model using
  <https://github.com/k2-fsa/icefall/pull/248>.
  
  It is trained on full LibriSpeech dataset using pruned RNN-T loss from [k2](https://github.com/k2-fsa/k2).
  
  ## How to clone this repo
  ```
  sudo apt-get install git-lfs
  git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
  
  cd icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
  git lfs pull
  ```
  
  **Caution**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
  
  The model in this repo is trained using the commit `1603744469d167d848e074f2ea98c587153205fa`.
  
  You can use
  
  ```
  git clone https://github.com/k2-fsa/icefall
  cd icefall
  git checkout 1603744469d167d848e074f2ea98c587153205fa
  ```
  to download `icefall`.
  
  
  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="0,1,2,3,4,5,6,7"
  
  . path.sh
  
  ./pruned_transducer_stateless/train.py \
    --world-size 8 \
    --num-epochs 60 \
    --start-epoch 0 \
    --exp-dir pruned_transducer_stateless/exp \
    --full-libri 1 \
    --max-duration 300 \
    --prune-range 5 \
    --lr-factor 5 \
    --lm-scale 0.25
  ```
  
  The tensorboard training log can be found at
  <https://tensorboard.dev/experiment/WKRFY5fYSzaVBHahenpNlA/>
  
  The command for decoding is:
  
  ```bash
  epoch=42
  avg=11
  sym=1
  
  # greedy search
  
  ./pruned_transducer_stateless/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir ./pruned_transducer_stateless/exp \
    --max-duration 100 \
    --decoding-method greedy_search \
    --beam-size 4 \
    --max-sym-per-frame $sym
  
  # modified beam search
  ./pruned_transducer_stateless/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir ./pruned_transducer_stateless/exp \
    --max-duration 100 \
    --decoding-method modified_beam_search \
    --beam-size 4
  
  # beam search
  # (not recommended)
  ./pruned_transducer_stateless/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir ./pruned_transducer_stateless/exp \
    --max-duration 100 \
    --decoding-method 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) | 2.62       | 6.37       | --epoch 42, --avg 11, --max-duration 100 |
  | greedy search (max sym per frame 2) | 2.62       | 6.37       | --epoch 42, --avg 11, --max-duration 100 |
  | greedy search (max sym per frame 3) | 2.62       | 6.37       | --epoch 42, --avg 11, --max-duration 100 |
  | modified beam search (beam size 4)  | 2.56       | 6.27       | --epoch 42, --avg 11, --max-duration 100 |
  | beam search (beam size 4)           | 2.57       | 6.27       | --epoch 42, --avg 11, --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:
  
  ```bash
  epoch=42
  avg=11
  
  ./pruned_transducer_stateless/export.py \
    --exp-dir ./pruned_transducer_stateless/exp \
    --bpe-model data/lang_bpe_500/bpe.model \
    --epoch $epoch \
    --avg $avg
  ```
  
  **HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
  just do the following:
  ```
  cp icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/exp/pretrained.pt \
    /path/to/icefall/egs/librispeech/ASR/pruned_transducer_stateless/exp/epoch-999.pt
  ```
  and pass `--epoch 999 --avg 1` to `pruned_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-pruned-transducer-stateless-2022-03-12/tree/main/exp
  [data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/data
  [test_wavs]: https://huggingface.co/csukuangfj/icefall-