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# 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.
```bash
./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:
```bash
./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:
```bash
./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:
```bash
./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:
```bash
./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
```bash
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
```bash
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
```bash
./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
```bash
./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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
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.
Please visit
<https://icefall.readthedocs.io/en/latest/recipes/librispeech/conformer_ctc.html>
for how to run this recipe.

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## Yesno recipe
You can run the recipe with **CPU**.
This is the simplest ASR recipe in `icefall`.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
The above Colab notebook finishes the training using **CPU**
within two minutes (50 epochs in total).
The WER is
It can be run on CPU and takes less than 30 seconds to
get the following WER:
```
[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
```
Please refer to
<https://icefal1.readthedocs.io/en/latest/recipes/yesno.html>
for detailed instructions.

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## How to run this recipe
You can find detailed instructions by visiting
<https://icefal1.readthedocs.io/en/latest/recipes/yesno.html>
It describes how to run this recipe and how to use
a pre-trained model with `./pretrained.py`.