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__init__.py
WIP: Begin to add BPE decoding
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asr_datamodule.py
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conformer.py
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decode.py
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pretrained.py
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README.md
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README.md

How to use a pre-trained model to transcribe a sound file or multiple sound files

(See the bottom of this document for the link to a colab notebook.)

You need to prepare 4 files:

  • a model checkpoint file, e.g., epoch-20.pt
  • HLG.pt, the decoding graph
  • words.txt, the word symbol table
  • a sound file, whose sampling rate has to be 16 kHz. Supported formats are those supported by torchaudio.load(), e.g., wav and flac.

Also, you need to install kaldifeat. Please refer to https://github.com/csukuangfj/kaldifeat for installation.

./conformer_ctc/pretrained.py --help

displays the help information.

HLG decoding

Once you have the above files ready and have kaldifeat installed, you can run:

./conformer_ctc/pretrained.py \
  --checkpoint /path/to/your/checkpoint.pt \
  --words-file /path/to/words.txt \
  --HLG /path/to/HLG.pt \
  /path/to/your/sound.wav

and you will see the transcribed result.

If you want to transcribe multiple files at the same time, you can use:

./conformer_ctc/pretrained.py \
  --checkpoint /path/to/your/checkpoint.pt \
  --words-file /path/to/words.txt \
  --HLG /path/to/HLG.pt \
  /path/to/your/sound1.wav \
  /path/to/your/sound2.wav \
  /path/to/your/sound3.wav

Note: This is the fastest decoding method.

HLG decoding + LM rescoring

./conformer_ctc/pretrained.py also supports whole lattice LM rescoring and attention decoder rescoring.

To use whole lattice LM rescoring, you also need the following files:

  • G.pt, e.g., data/lm/G_4_gram.pt if you have run ./prepare.sh

The command to run decoding with LM rescoring is:

./conformer_ctc/pretrained.py \
  --checkpoint /path/to/your/checkpoint.pt \
  --words-file /path/to/words.txt \
  --HLG /path/to/HLG.pt \
  --method whole-lattice-rescoring \
  --G data/lm/G_4_gram.pt \
  --ngram-lm-scale 0.8 \
  /path/to/your/sound1.wav \
  /path/to/your/sound2.wav \
  /path/to/your/sound3.wav

HLG Decoding + LM rescoring + attention decoder rescoring

To use attention decoder for rescoring, you need the following extra information:

  • sos token ID
  • eos token ID

The command to run decoding with attention decoder rescoring is:

./conformer_ctc/pretrained.py \
  --checkpoint /path/to/your/checkpoint.pt \
  --words-file /path/to/words.txt \
  --HLG /path/to/HLG.pt \
  --method attention-decoder \
  --G data/lm/G_4_gram.pt \
  --ngram-lm-scale 1.3 \
  --attention-decoder-scale 1.2 \
  --lattice-score-scale 0.5 \
  --num-paths 100 \
  --sos-id 1 \
  --eos-id 1 \
  /path/to/your/sound1.wav \
  /path/to/your/sound2.wav \
  /path/to/your/sound3.wav

Decoding with a pre-trained model in action

We have uploaded a pre-trained model to https://huggingface.co/pkufool/conformer_ctc

The following shows the steps about the usage of the provided pre-trained model.

(1) Download the pre-trained model

sudo apt-get install git-lfs
cd /path/to/icefall/egs/librispeech/ASR
git lfs install
mkdir tmp
cd tmp
git clone https://huggingface.co/pkufool/conformer_ctc

CAUTION: You have to install git-lfst to download the pre-trained model.

You will find the following files:

tmp
`-- conformer_ctc
    |-- README.md
    |-- data
    |   |-- lang_bpe
    |   |   |-- HLG.pt
    |   |   |-- bpe.model
    |   |   |-- tokens.txt
    |   |   `-- words.txt
    |   `-- lm
    |       `-- G_4_gram.pt
    |-- exp
    |   `-- pretraind.pt
    `-- test_wavs
        |-- 1089-134686-0001.flac
        |-- 1221-135766-0001.flac
        |-- 1221-135766-0002.flac
        `-- trans.txt

6 directories, 11 files

File descriptions:

  • data/lang_bpe/HLG.pt

    It is the decoding graph.

  • data/lang_bpe/bpe.model

    It is a sentencepiece model. You can use it to reproduce our results.

  • data/lang_bpe/tokens.txt

    It contains tokens and their IDs, generated from bpe.model. Provided only for convienice so that you can look up the SOS/EOS ID easily.

  • data/lang_bpe/words.txt

    It contains words and their IDs.

  • data/lm/G_4_gram.pt

    It is a 4-gram LM, useful for LM rescoring.

  • exp/pretrained.pt

    It contains pre-trained model parameters, obtained by averaging checkpoints from epoch-15.pt to epoch-34.pt. Note: We have removed optimizer state_dict to reduce file size.

  • test_waves/*.flac

    It contains some test sound files from LibriSpeech test-clean dataset.

  • test_waves/trans.txt

    It contains the reference transcripts for the sound files in test_waves/.

The information of the test sound files is listed below:

$ soxi tmp/conformer_ctc/test_wavs/*.flac

Input File     : 'tmp/conformer_ctc/test_wavs/1089-134686-0001.flac'
Channels       : 1
Sample Rate    : 16000
Precision      : 16-bit
Duration       : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors
File Size      : 116k
Bit Rate       : 140k
Sample Encoding: 16-bit FLAC

Input File     : 'tmp/conformer_ctc/test_wavs/1221-135766-0001.flac'
Channels       : 1
Sample Rate    : 16000
Precision      : 16-bit
Duration       : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors
File Size      : 343k
Bit Rate       : 164k
Sample Encoding: 16-bit FLAC

Input File     : 'tmp/conformer_ctc/test_wavs/1221-135766-0002.flac'
Channels       : 1
Sample Rate    : 16000
Precision      : 16-bit
Duration       : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors
File Size      : 105k
Bit Rate       : 174k
Sample Encoding: 16-bit FLAC

Total Duration of 3 files: 00:00:28.16

(2) Use HLG decoding

cd /path/to/icefall/egs/librispeech/ASR

./conformer_ctc/pretrained.py \
  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac

The output is given below:

2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0
2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model
2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer
2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started
2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding
2021-08-20 11:03:19,149 INFO [pretrained.py:339]
./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS

./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN

./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION


2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done

(3) Use HLG decoding + LM rescoring

./conformer_ctc/pretrained.py \
  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
  --method whole-lattice-rescoring \
  --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \
  --ngram-lm-scale 0.8 \
  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac

The output is:

2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0
2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model
2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt
2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer
2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started
2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring
2021-08-20 11:13:11,736 INFO [pretrained.py:339]
./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS

./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN

./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION


2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done

(4) Use HLG decoding + LM rescoring + attention decoder rescoring

./conformer_ctc/pretrained.py \
  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
  --method attention-decoder \
  --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \
  --ngram-lm-scale 1.3 \
  --attention-decoder-scale 1.2 \
  --lattice-score-scale 0.5 \
  --num-paths 100 \
  --sos-id 1 \
  --eos-id 1 \
  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac

The output is:

2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0
2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model
2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt
2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer
2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started
2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring
2021-08-20 11:20:05,805 INFO [pretrained.py:339]
./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS

./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN

./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION


2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done

NOTE: We provide a colab notebook for demonstration. Open In Colab

Due to limited memory provided by Colab, you have to upgrade to Colab Pro to run HLG decoding + LM rescoring and HLG decoding + LM rescoring + attention decoder rescoring. Otherwise, you can only run HLG decoding with Colab.