readme: # Introduction
  
  This repo contains pre-trained model using
  <https://github.com/k2-fsa/icefall/pull/213>.
  
  It is trained on train-clean-100 subset of the LibriSpeech dataset.
  Also, it uses the `S` subset from GigaSpeech as extra training data.
  
  ## How to clone this repo
  ```
  sudo apt-get install git-lfs
  git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
  
  cd icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
  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 `2332ba312d7ce72f08c7bac1e3312f7e3dd722dc`.
  
  You can use
  
  ```
  git clone https://github.com/k2-fsa/icefall
  cd icefall
  git checkout 2332ba312d7ce72f08c7bac1e3312f7e3dd722dc
  ```
  to download `icefall`.
  
  You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/2332ba312d7ce72f08c7bac1e3312f7e3dd722dc/egs/librispeech/ASR/transducer_stateless_multi_datasets/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
  ./prepare_giga_speech.sh
  
  export CUDA_VISIBLE_DEVICES="0,1"
  
  ./transducer_stateless_multi_datasets/train.py \
    --world-size 2 \
    --num-epochs 60 \
    --start-epoch 0 \
    --exp-dir transducer_stateless_multi_datasets/exp-100-2 \
    --full-libri 0 \
    --max-duration 300 \
    --lr-factor 1 \
    --bpe-model data/lang_bpe_500/bpe.model \
    --modified-transducer-prob 0.25
    --giga-prob 0.2
  ```
  
  The tensorboard training log can be found at
  <https://tensorboard.dev/experiment/qUEKzMnrTZmOz1EXPda9RA/>
  
  The command for decoding is:
  ```
  epoch=57
  avg=17
  
  ## greedy search
  for epoch in 57; do
    for avg in 17; do
      for sym in 1 2 3; do
      ./transducer_stateless_multi_datasets/decode.py \
        --epoch $epoch \
        --avg $avg \
        --exp-dir transducer_stateless_multi_datasets/exp-100-2 \
        --bpe-model ./data/lang_bpe_500/bpe.model \
        --max-duration 100 \
        --context-size 2 \
        --max-sym-per-frame $sym
      done
    done
  done
  
  ## modified beam search
  
  epoch=57
  avg=17
  ./transducer_stateless_multi_datasets/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_multi_datasets/exp-100-2 \
    --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) | 6.34       | 16.7       | --epoch 57, --avg 17, --max-duration 100 |
  | greedy search (max sym per frame 2) | 6.34       | 16.7       | --epoch 57, --avg 17, --max-duration 100 |
  | greedy search (max sym per frame 3) | 6.34       | 16.7       | --epoch 57, --avg 17, --max-duration 100 |
  | modified beam search (beam size 4)  | 6.31       | 16.3       | --epoch 57, --avg 17, --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
  ./transducer_stateless_multi_datasets/export.py \
    --epoch 57 \
    --avg 17 \
    --bpe-model data/lang_bpe_500/bpe.model \
    --exp-dir transducer_stateless_multi_datasets/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-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
    /path/to/icefall/egs/librispeech/ASR/transducer_stateless_multi_datasets/exp/epoch-999.pt
  ```
  and pass `--epoch 999 --avg 1` to `transducer_stateless_multi_datasets/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://huggi