csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07

Introduction

How to clone this repo

sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07


cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
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 a8150021e01d34ecbd6198fe03a57eacf47a16f2.

You can use

git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout a8150021e01d34ecbd6198fe03a57eacf47a16f2

to download icefall.

You can find the model information by visiting https://github.com/k2-fsa/icefall/blob/a8150021e01d34ecbd6198fe03a57eacf47a16f2/egs/librispeech/ASR/transducer_stateless/train.py#L198.

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. 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 76 \
  --start-epoch 0 \
  --exp-dir transducer_stateless/exp-full \
  --full-libri 1 \
  --max-duration 300 \
  --lr-factor 5 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --modified-transducer-prob 0.25

The tensorboard training log can be found at https://tensorboard.dev/experiment/qgvWkbF2R46FYA6ZMNmOjA/

The command for decoding is:

epoch=63
avg=19

## greedy search
for sym in 1 2 3; do
  ./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 \
    --max-sym-per-frame $sym
done

## modified 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 \
  --context-size 2 \
  --decoding-method modified_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)	2.67	6.67	--epoch 63, --avg 19, --max-duration 100
greedy search (max sym per frame 2)	2.67	6.67	--epoch 63, --avg 19, --max-duration 100
greedy search (max sym per frame 3)	2.67	6.67	--epoch 63, --avg 19, --max-duration 100
modified beam search (beam size 4)	2.67	6.57	--epoch 63, --avg 19, --max-duration 100

File description

• log, this directory contains the decoding log and decoding results
• test_wavs, this directory contains wave files for testing the pre-trained model
• data, this directory contains files generated by prepare.sh
• exp, this directory contains only one file: preprained.pt

exp/pretrained.pt is generated by the following command:

./transducer_stateless/export.py \
  --epoch 63 \
  --avg 19 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --exp-dir transducer_stateless/exp-full

HINT: To use pretrained.pt to compute the WER for test-clean and test-other, just do the following:

cp icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/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.