# Introduction

## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17

cd icefall-asr-librispeech-transducer-bpe-500-2021-12-17
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 `cb04c8a7509425ab45fae888b0ca71bbbd23f0de`.

You can use

```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout cb04c8a7509425ab45fae888b0ca71bbbd23f0de
```
to download `icefall`.

You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/cb04c8a7509425ab45fae888b0ca71bbbd23f0de/egs/librispeech/ASR/transducer/train.py#L196>

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 4-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 30 \
  --start-epoch 0 \
  --exp-dir transducer/exp-lr-2.5-full \
  --full-libri 1 \
  --max-duration 250 \
  --lr-factor 2.5
```

The command for decoding is:
```
epoch=26
avg=12

./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 this
repo: [log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04](log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04).

The best WER using greedy search is:

|     | test-clean | test-other |
|-----|------------|------------|
| WER | 3.16       | 7.71       |

# 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 26 \
  --avg 12 \
  --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-500-2021-12-17/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-500-2021-12-17/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/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 'cb04c8a7509425ab45fae888b0ca71bbbd23f0de'.


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 4-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 this
repo: log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04.


The best WER using greedy search is:


test-clean: WER, test-other: 3.16


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 
