---
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-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall