

# 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

{"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "librispeech", "ASR", "stateless transducer", "PyTorch", "RNN-T", "pruned RNN-T", "speech recognition"], "datasets": ["librispeech"], "metrics": ["WER"]}
k2 icefall transducer librispeech ASR stateless transducer PyTorch RNN-T pruned RNN-T speech recognition en dataset:librispeech license:apache-2.0 region:us
TAGS
#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #pruned RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us 

Introduction
============


This repo contains pre-trained model using
<URL


It is trained on full LibriSpeech dataset using pruned RNN-T loss from k2.


How to clone this repo
----------------------


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


to download 'icefall'.


The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
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](URL). 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:


The tensorboard training log can be found at
<URL


The command for decoding is:


You can find the decoding log for the above command in this
repo (in the folder 'log').


The WERs for the test datasets are



File description
================


* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'


'exp/URL' is generated by the following command:


HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:


and pass '--epoch 999 --avg 1' to 'pruned\_transducer\_stateless/URL'.



TAGS
#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #pruned RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us 

passage: TAGS
#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #pruned RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us 
