ba-dls-deepspeech

Train your own CTC model!

Table of Contents

1. Dependencies
2. Data
3. Running an example

Dependencies

You will need the following packages installed before you can train a model using this code. You may have to change PYTHONPATH to include the directories of your new packages.

theano
The underlying deep learning Python library. We suggest downloading the latest release from https://github.com/Theano/Theano/releases.

$tar xf <downloaded_tar_file>
$cd theano-*
$python setup.py install --user

keras
This is a wrapper over Theano that provides nice functions for building networks. Download the latest release from https://github.com/fchollet/keras/releases
Make sure you install it with support for hdf5 - we make use of that to save models.

$tar xf <downloaded_tar_file>
$cd keras-*
$python setup.py install --user

lasagne

$pip install lasagne <--user>

warp-ctc
This contains the main implementation of the CTC cost function.
git clone https://github.com/baidu-research/warp-ctc
To install it, follow the instructions on https://github.com/baidu-research/warp-ctc

theano-warp-ctc
This is a theano wrapper over warp-ctc.
git clone https://github.com/sherjilozair/ctc
Follow the instructions on https://github.com/sherjilozair/ctc for installation.

Others
You may require some additional packages. Install Python requirements through pip as:
pip install soundfile
On Ubuntu, avconv (used here for audio format conversions) requires libav-tools.
sudo apt-get install libav-tools

Data

We will make use of the LibriSpeech ASR corpus to train our models. Use the download.sh script to download this corpus (~65GB). Use flac_to_wav.sh to convert any flac files to wav.
We make use of a JSON file that aggregates all data for training, validation and testing. Once you have a corpus, create a description file that is a json-line file in the following format:

{"duration": 15.685, "text": "spoken text label", "key": "/home/username/LibriSpeech/train-clean-360/5672/88367/5672-88367-0031.wav"}
{"duration": 14.32, "text": "ground truth text", "key": "/home/username/LibriSpeech/train-other-500/8678/280914/8678-280914-0009.wav"}

You can create such a file using create_desc_file.py. Each line is a JSON. We will make use of the durations to construct a curriculum in the first epoch (shorter utterances are easier).
You can query the duration of a file using: soxi -D filename.

Running an example

Finally, let's train a model!
python train.py train_corpus.json validation_corpus.json ./save_my_model_here
This will checkpoint a model every few iterations into the directory you specify.