# Introduction

## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22


cd icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
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 `fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32`.

You can use

```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32
```
to download `icefall`.

You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32/egs/librispeech/ASR/transducer_stateless/train.py#L195>.

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
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 0 \
  --exp-dir transducer_stateless/exp-full \
  --full-libri 1 \
  --max-duration 250 \
  --lr-factor 3
```

The tensorboard training log can be found at
<https://tensorboard.dev/experiment/PsJ3LgkEQfOmzedAlYfVeg/#scalars&_smoothingWeight=0>

The command for decoding is:
```
epoch=20
avg=10

## greedy search
./transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless/exp-full \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100

## beam search
./transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless/exp-full \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --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             | 2.99       | 7.52       | --epoch 20, --avg 10, --max-duration 100 |
| beam search (beam size 2) | 2.95       | 7.43       |                                          |
| beam search (beam size 3) | 2.94       | 7.37       |                                          |
| beam search (beam size 4) | 2.92       | 7.37       |                                          |
| beam search (beam size 5) | 2.93       | 7.38       |                                          |
| beam search (beam size 8) | 2.92       | 7.38       |                                          |


# 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:
```
./transducer_stateless/export.py \
  --epoch 20 \
  --avg 10 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --exp-dir transducer_stateless/exp-full
```

**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/exp/pretrained.pt \
  /path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `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-transducer-stateless-bpe-500-2021-12-22/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall

{}
region:us
TAGS
#region-us 

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


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


Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.


The model in this repo is trained using the commit 'fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32'.


You can use


to download 'icefall'.


You can find the model information by visiting <URL


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.
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 'transducer\_stateless/URL'.



TAGS
#region-us 

TAGS
#region-us 
