README.md

---
language: "en"
tags:
- icefall
- k2
- transducer
- aishell
- ASR
- stateless transducer
- PyTorch
license: "apache-2.0"
datasets:
- aishell
- aidatatang_200zh
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).
Also, it uses [aidatatang_200zh](http://www.openslr.org/62/) as extra training data.

## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01

cd icefall-aishell-transducer-stateless-modified-2-2022-03-01
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-2/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
./prepare_aidatatang_200zh.sh

export CUDA_VISIBLE_DEVICES="0,1,2"

./transducer_stateless_modified-2/train.py \
  --world-size 3 \
  --num-epochs 90 \
  --start-epoch 0 \
  --exp-dir transducer_stateless_modified-2/exp-2 \
  --max-duration 250 \
  --lr-factor 2.0 \
  --context-size 2 \
  --modified-transducer-prob 0.25 \
  --datatang-prob 0.2
```

The tensorboard training log can be found at
<https://tensorboard.dev/experiment/oG72ZlWaSGua6fXkcGRRjA/>

The commands for decoding are

```bash
# greedy search
for epoch in 89; do
  for avg in 38; do
  ./transducer_stateless_modified-2/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_modified-2/exp-2 \
    --max-duration 100 \
    --context-size 2 \
    --decoding-method greedy_search \
    --max-sym-per-frame 1
  done
done

# modified beam search
for epoch in 89; do
  for avg in 38; do
    ./transducer_stateless_modified-2/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_modified-2/exp-2 \
    --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          | 4.94 |--epoch 89, --avg 38, --max-duration 100, --max-sym-per-frame 1 |
| modified beam search   | 4.68 |--epoch 89, --avg 38, --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=89
avg=38

./transducer_stateless_modified-2/export.py \
  --exp-dir ./transducer_stateless_modified-2/exp-2 \
  --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-2-2022-03-01/exp/pretrained.pt \
  /path/to/icefall/egs/aishell/ASR/transducer_stateless_modified-2/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless_modified-2/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-2-2022-03-01/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall