• Multi-gpu support, how do we get around passing hidden,cell states for data parallel?
• Support LibriSpeech via multi-processed scripts
• Sometimes gradients go boom, add inf check to prevent meter from measuring this (and going boom as well)
• Similar convergence as to torch7 version on an4

Implementation of Baidu Warp-CTC using pytorch. Creates a network based on the DeepSpeech2 architecture, trained with the CTC activation function.

Several libraries are needed to be installed for training to work. I will assume that everything is being installed in an Anaconda installation on Ubuntu.

The dataloader relies on Neon, as a result Neon has to be installed. Installation instructions can be seen here Instructions to install Neon for Anaconda/Ubuntu below:

sudo apt-get install libcurl4-openssl-dev clang libopencv-dev libsox-dev
sudo apt-get install ffmpeg
git clone https://github.com/NervanaSystems/neon.git
cd neon && make sysinstall

Install pytorch if you haven't already:

conda install pytorch -c https://conda.anaconda.org/t/6N-MsQ4WZ7jo/soumith

Install this fork for Warp-CTC bindings:

git clone https://github.com/SeanNaren/ctc.git
cd ctc
mkdir build
cd build
cmake ..
make
cd ../python
sudo /home/<your.name>/anaconda2/bin/python setup.py install # e.g sudo /home/sean.narenthiran/anaconda2/bin/python setup.py install

Finally:

pip install python-levenshtein

Currently only supports an4. To download and setup the an4 dataset run below command in the root folder of the repo:

python get_an4.py
python create_dataset_manifest.py --root_path dataset/

This will generate csv manifests files used to load the data for training.

Optionally, a noise dataset can be used to inject noise artifically into the training data. Just fill a folder with noise wav files you want to inject (a source of noise files is the musan dataset) and run the below command:

python create_noise_manifest.py --root_path noise/ # or whatever you've named your noise folder

You need to find the maximum duration of the training and testing samples. The command below will iterate through the current folder and find the longest duration:

find . -type f -name "*.wav" | xargs soxi -D | sort | tail -n 1

Afterwards you can run the training script.

python main.py --max_duration 6.4 # This is the default max duration (for an4)