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transducer_stateless

README.md

Introduction

The decoder, i.e., the prediction network, is from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419 (Rnn-Transducer with Stateless Prediction Network)

You can use the following command to start the training:

cd egs/librispeech/ASR

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-libri 1 \
  --max-duration 250 \
  --lr-factor 2.5

How to get framewise token alignment

Assume that you already have a trained model. If not, you can either train one by yourself or download a pre-trained model from hugging face: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01

Caution: If you are going to use your own trained model, remember to set --modified-transducer-prob to a nonzero value since the force alignment code assumes that --max-sym-per-frame is 1.

The following shows how to get framewise token alignment using the above pre-trained model.

git clone https://github.com/k2-fsa/icefall
cd icefall/egs/librispeech/ASR
mkdir tmp
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01 ./tmp/

ln -s $PWD/tmp/exp/pretrained.pt $PWD/tmp/epoch-999.pt

./transducer_stateless/compute_ali.py \
        --exp-dir ./tmp/exp \
        --bpe-model ./tmp/data/lang_bpe_500/bpe.model \
        --epoch 999 \
        --avg 1 \
        --max-duration 100 \
        --dataset dev-clean \
        --out-dir data/ali

After running the above commands, you will find the following two files in the folder ./data/ali:

-rw-r--r-- 1 xxx xxx 412K Mar  7 15:45 cuts_dev-clean.json.gz
-rw-r--r-- 1 xxx xxx 2.9M Mar  7 15:45 token_ali_dev-clean.h5

You can find usage examples in ./test_compute_ali.py about extracting framewise token alignment information from the above two files.

How to get word starting time from framewise token alignment

Assume you have run the above commands to get framewise token alignment using a pre-trained model from tmp/exp/epoch-999.pt. You can use the following commands to obtain word starting time.

./transducer_stateless/test_compute_ali.py \
        --bpe-model ./tmp/data/lang_bpe_500/bpe.model \
        --ali-dir data/ali \
        --dataset dev-clean

Caution: Since the frame shift is 10ms and the subsampling factor of the model is 4, the time resolution is 0.04 second.

Note: The script test_compute_ali.py is for illustration only and it processes only one batch and then exits.

You will get the following output:

5694-64029-0022-1998-0
[('THE', '0.20'), ('LEADEN', '0.36'), ('HAIL', '0.72'), ('STORM', '1.00'), ('SWEPT', '1.48'), ('THEM', '1.88'), ('OFF', '2.00'), ('THE', '2.24'), ('FIELD', '2.36'), ('THEY', '3.20'), ('FELL', '3.36'), ('BACK', '3.64'), ('AND', '3.92'), ('RE', '4.04'), ('FORMED', '4.20')]

3081-166546-0040-308-0
[('IN', '0.32'), ('OLDEN', '0.60'), ('DAYS', '1.00'), ('THEY', '1.40'), ('WOULD', '1.56'), ('HAVE', '1.76'), ('SAID', '1.92'), ('STRUCK', '2.60'), ('BY', '3.16'), ('A', '3.36'), ('BOLT', '3.44'), ('FROM', '3.84'), ('HEAVEN', '4.04')]

2035-147960-0016-1283-0
[('A', '0.44'), ('SNAKE', '0.52'), ('OF', '0.84'), ('HIS', '0.96'), ('SIZE', '1.12'), ('IN', '1.60'), ('FIGHTING', '1.72'), ('TRIM', '2.12'), ('WOULD', '2.56'), ('BE', '2.76'), ('MORE', '2.88'), ('THAN', '3.08'), ('ANY', '3.28'), ('BOY', '3.56'), ('COULD', '3.88'), ('HANDLE', '4.04')]

2428-83699-0020-1734-0
[('WHEN', '0.28'), ('THE', '0.48'), ('TRAP', '0.60'), ('DID', '0.88'), ('APPEAR', '1.08'), ('IT', '1.80'), ('LOOKED', '1.96'), ('TO',
'2.24'), ('ME', '2.36'), ('UNCOMMONLY', '2.52'), ('LIKE', '3.16'), ('AN', '3.40'), ('OPEN', '3.56'), ('SPRING', '3.92'), ('CART', '4.28')]

8297-275154-0026-2108-0
[('LET', '0.44'), ('ME', '0.72'), ('REST', '0.92'), ('A', '1.32'), ('LITTLE', '1.40'), ('HE', '1.80'), ('PLEADED', '2.00'), ('IF', '3.04'), ("I'M", '3.28'), ('NOT', '3.52'), ('IN', '3.76'), ('THE', '3.88'), ('WAY', '4.00')]

652-129742-0007-1002-0
[('SURROUND', '0.28'), ('WITH', '0.80'), ('A', '0.92'), ('GARNISH', '1.00'), ('OF', '1.44'), ('COOKED', '1.56'), ('AND', '1.88'), ('DICED', '4.16'), ('CARROTS', '4.28'), ('TURNIPS', '4.44'), ('GREEN', '4.60'), ('PEAS', '4.72')]

For the row:

5694-64029-0022-1998-0
[('THE', '0.20'), ('LEADEN', '0.36'), ('HAIL', '0.72'), ('STORM', '1.00'), ('SWEPT', '1.48'),
('THEM', '1.88'), ('OFF', '2.00'), ('THE', '2.24'), ('FIELD', '2.36'), ('THEY', '3.20'), ('FELL', '3.36'),
('BACK', '3.64'), ('AND', '3.92'), ('RE', '4.04'), ('FORMED', '4.20')]
  • 5694-64029-0022-1998-0 is the cut ID.
  • ('THE', '0.20') means the word THE starts at 0.20 second.
  • ('LEADEN', '0.36') means the word LEADEN starts at 0.36 second.

You can compare the above word starting time with the one from https://github.com/CorentinJ/librispeech-alignments

5694-64029-0022 ",THE,LEADEN,HAIL,STORM,SWEPT,THEM,OFF,THE,FIELD,,THEY,FELL,BACK,AND,RE,FORMED," "0.230,0.360,0.670,1.010,1.440,1.860,1.990,2.230,2.350,2.870,3.230,3.390,3.660,3.960,4.060,4.160,4.850,4.9"

We reformat it below for readability:

5694-64029-0022 ",THE,LEADEN,HAIL,STORM,SWEPT,THEM,OFF,THE,FIELD,,THEY,FELL,BACK,AND,RE,FORMED,"
"0.230,0.360,0.670,1.010,1.440,1.860,1.990,2.230,2.350,2.870,3.230,3.390,3.660,3.960,4.060,4.160,4.850,4.9"
  the  leaden hail storm swept them  off   the   field  sil   they  fell  back  and   re   formed  sil