This is an in progress reimplementation of the Jasper speech-to-text network on Intel optimized TensorFlow using the included Keras API.
In order to run this network you will first need to download and extract the appropraite training data. To do this, set this master folder as your working directory and run the bash script, download_librispeech.sh, located in the datagen_utils folder. This will download the datasets listed in the librispeech.csv file. Currently, only the 100 hour clean training set is listed, with the other sets listed in the extra.csv file. If you wish to download all of the training files, copy-paste the lines in the extra.csv file into the librispeech.csv file, then run the download_librispeech.sh script. Warning: This method takes a long time to complete. It may be faster for you to manually download the appropriate folders from the librispeech website and extract them using tar -xvf.
After the files have been downloaded and extracted, you will then need to run the covert_json.sh script also located in the datagen_utils folder to convert the files from .flac to .wav and construct a .json file containing pertinent information on the audio files. The convert_json.sh script may need to be edited in order to select exactly which datasets to convert. It is currently set up to convert the 100 hour clean training set.
Following the conversion of the .flac files and the creation of the .json file, you can then run the train.py script which will initialize and train a 5x3 Jasper model on the dataset(s) of your choosing. Should the training set you wish to use is different than the 100 hour clean set from LibriSpeech speech, you will need to update model.compile() with the appropriate max lengths of computed spectrogram and normalized sentence.
This project has been developed on Intel Devcloud and Intel NUC.
Package requirements:
- TensorFlow 1.14
- Scipy
- Numpy
- Matplotlib
- Unidecode
- Inflect
To date, I have not had the compute resources necessary to fully train, test, and debug the network. I am still working on securing the necessary resources.