A PyTorch implementation of punctuation prediction system using LSTM/BLSTM [1][2][3], which automatically adds suitable punctation into text without punctuation.

• PyTorch 0.4+

egs/toy/run.sh provides an example usage.

# Set PATH and PYTHONPATH
$ cd egs/toy/; . ./path.sh
# Train
$ train.py -h
# Add punctuation
$ add_punctuation.py -h
# Analyze metrics
$ analyer.py -h

If you want to visualize your loss, you can use visdom to do that:

1. Open a new terminal in your remote server (recommend tmux) and run $ visdom.
2. Open a new terminal and run $ train.py ... --visdom 1 --vidsdom_id "<any-string>".
3. Open your browser and type <your-remote-server-ip>:8097, egs, 127.0.0.1:8097.
4. In visdom website, chose <any-string> in Environment to see your loss.

$ train.py --continue_from <model-path>

Use comma separated gpu-id sequence, such as:

$ CUDA_VISIBLE_DEVICES="0,1" train.py

• [1] Kaituo Xu, Lei Xie, and Kaisheng Yao. "Investigating LSTM for punctuation prediction" in ISCSLP 2016
• [2] Ottokar Tilk and Tanel Alumae. "Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration" in Interspeech 2016
• [3] Ottokar Tilk and Tanel Alumae. "LSTM for Punctuation Restoration in Speech Transcripts" in Interspeech 2015