Speech is an open-source package to build end-to-end models for automatic speech recognition. Sequence-to-sequence models with attention and connectionist temporal classification are currently supported.
The goal of this software is to facilitate research in end-to-end models for speech recognition. The models are implemented in PyTorch.
The software has only been tested in Python2.7, though we plan to support both 2.7 and 3.5.
We recommend creating a virtual environment and installing the python requirements there.
virtualenv <path_to_your_env>
source <path_to_your_env>/bin/activate
pip install -r requirements.txt
Then follow the installation instructions for a version of PyTorch which works for your machine.
After all the python requirements are installed, from the top level directory, run:
make
The build process requires CMake as well as Make.
After that, source the setup.sh from the repo root.
source setup.sh
Consider adding this to your bashrc.
You can verify the install was successful by running the
tests from the tests directory.
cd tests
pytest
To train a model run
python train.py <path_to_config>
After the model is done training you can evaluate it with
python eval.py <path_to_model> <path_to_data_json>
To see the available options for each script use -h:
python {train, eval}.py -h
For examples of model configurations and datasets, visit the examples
directory. Each example dataset should have instructions and/or scripts for
downloading and preparing the data. There should also be one or more model
configurations available. The results for each configuration will documented in
each examples corresponding README.md.