icefall/egs/librispeech/ASR/conformer_ctc/README.md

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

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