This is a simplified version of https://github.com/parlance/ctcdecode.

For the detail of the ctcdecode, please checkout https://github.com/parlance/ctcdecode .

The main difference is;

• Remove KenLM support
• Remove dependencies
  • Boost
  • utf8
  • ThreadPool
• Clean-up library
• Use TorchScript for bind the C++
• Use Torch's at::parallel_for in place of ThreadPool.
• Remove unused functions
• Rename the module and decoder class (simple_ctc.BeamSearchDecoder)
• Moved the original decode method to forward and replace decode method with high level API that performs label conversion as well.

• Add language model back.
  • Which LM should we use?
    • KenLM (as in the original)
    • SRILM (non-commerial use only without huge fee)
    • IRSTLM
    • MITLM
• Fix timestep bug.

• OpenFST (statically built/linked when installing)
• PyTorch

See requirements.txt for the Python package requirements.

NOTE The build process downloads and compiles OpenFST, so it takes a while.

pip install git+https://github.com/mthrok/ctcdecode

For development

git clone https://github.com/mthrok/ctcdecode
cd ctcdecode
python setup.py develop

NOTE Currently, timesteps is not correctly computed. so an empty list is returned.

from simple_ctc import BeamSearchDecoder

decoder = BeamSearchDecoder(
    labels,
    beam_size=100,
    cutoff_top_n=40,
    cutoff_prob=1.0,
    blank_id=0,
    is_nll=False,
    num_processes=4,
)
result = decoder.decode(prob_seqs, seq_lens)

print(result.labels[batch][beam][:])  # Resulting label sequences. 3D list.
print(result.scores[batch][beam])  # Scores of the sequences. 2D list.
print(result.timesteps[batch][beam][:])  # Timesteps of each label peak probabilities. 3D list.

This decoder supports TorchScript. You should be able to deploy the dumped object in non-Python environment by loading the libctcdecode.so in your application.

import torch

path = 'decoder.zip'
torch.jit.save(decoder, path)
decoder = torch.jit.load(path)

result = decoder.decoder(prob_seqs, seq_lens)