tilayealemu/MelaNet

Amharic speech recognition using Deep Learning Deep learning speech recognition model for Amharic, and potentially other Ethiopian languages too. ## Overview The best documentation so far is Deep Learning for Amharic speech recognition. Here is an overview. ## Quick start To get an idea of how models are setup and investigated, take a look at the notebooks for Model 1 and Model 2. If you are interested in running or updating any of the source code, you need a host with Python, Tensorflow, Keras and librosa, Jupyter. A docker image is available with all pre-requisites installed. Here is how you use it ``` git clone git@github.com:tilayealemu/MelaNet cd MelaNet/docker docker-compose up ``` This should start Jupyter server on port 8888. Go to localhost to connect to it. I strongly recommend you use the docker approach as you can waste quite a lot of time installing packages on your own computer. ## Getting data You need data if you want to train your own models. It's 1.2 GB when compressed, and 2.3 GB uncompressed. Download it from MelaNetData and copy it to your clone of this repo like so: ``` git clone git@github.com:tilayealemu/MelaNetData cd MelaNetData/data cat data.tar.gz.* > data.tar.gz tar xzf data.tar.gz mv -r data/* /data ``` You should now have all .wav files and transcriptions. ## Structure ``` ├── docker docker files ├── models pre-trained models ├── src python source files ├── *.ipynb Jupyter notebooks for visualization and experimentation ``` ## Questions If you face any issues please raise a ticket.