# Introduction

## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23

cd icefall-asr-librispeech-transducer-bpe-500-2021-12-23
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 `5b6699a8354b70b23b252b371c612a35ed186ec2`.

You can use

```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 5b6699a8354b70b23b252b371c612a35ed186ec2
```
to download `icefall`.

You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/5b6699a8354b70b23b252b371c612a35ed186ec2/egs/librispeech/ASR/transducer/train.py#L191>

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, plus a 2-layer LSTM with hidden size 512.

-----

## Description

This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall][icefall].

The commands for training are:

```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer/train.py \
  --world-size 4 \
  --num-epochs 35 \
  --start-epoch 0 \
  --exp-dir transducer/exp-lr-2.5-full \
  --full-libri 1 \
  --max-duration 180 \
  --lr-factor 2.5
```

The command for decoding is:
```
epoch=34
avg=11

./transducer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer/exp-lr-2.5-full \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100
```

You can find the decoding log for the above command in the `log` folder
of this repo.

The best WER using greedy search is:

|     | test-clean | test-other |
|-----|------------|------------|
| WER | 3.07       | 7.51       |

# 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/export.py \
  --epoch 34 \
  --avg 11 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --exp-dir transducer/exp-lr-2.5-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-bpe-500-2021-12-23/exp/pretrained.pt \
  /path/to/icefall/egs/librispeech/ASR/transducer/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer/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-bpe-500-2021-12-23/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall

{}
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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 '5b6699a8354b70b23b252b371c612a35ed186ec2'.


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, plus a 2-layer LSTM with hidden size 512.




---


Description
-----------


This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall](URL).


The commands for training are:


The command for decoding is:


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


The best WER using greedy search is:


test-clean: WER, test-other: 3.07


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/URL'.



TAGS
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passage: TAGS
#region-us 
