DeepSpeech

To run the experiments end-to-end ASR system needs to be installed. I trained the models on IU's Carbonate deep learning partition since training takes a lot of time and resources.

We need a specific branch of DeepSpeech to run the experiments:

git clone https://github.com/mozilla/DeepSpeech.git
git checkout transfer-learning2

Installation

python3 -m venv deepspeech-transfer

source $HOME/tmp/deepspeech-transfer/bin/activate

cd DeepSpeech

pip install -r requirements.txt

Note: Make sure Tensorflow GPU is installed: tensorflow-gpu == 1.13.1.

CTC Decoder Installation

cd DeepSpeech/native_client/ctcdecode
python setup.py install

Additionaly you will need to download checkpoints for a pretrained English language model of version 0.5.0.

Training data and files

Training data is stored in data/ inside the project submission directory. It includes:

• Audio clips/ directory
• Inside this directory we have: train.csv, dev.csv and test.csv files. In the same directory .sh scripts for server jobs store files batch.csv for training the pipeline. It is important so that the batches are saved in the clips/ directory.
• Language model file tt_lm.binary
• Alphabet file tt_alphabet.txt
• Trie file tt_trie

Scripts

The scripts/ directory contains all the bash scripts to run DeepSpeech jobs either on the server or locally. Unfortunately there is no other way but change all of the absolute directories that DeepSpeech uses inside the .sh scripts since it is just a research code and there was not enough time to wrap it all into the proper util functions.

For example in tt_init_1.sh:

--train_files='/N/slate/anakuzne/tatar/clips/batch.csv'

should be changed to

--train_files='/data/clips/batch.csv'

Source model and checkpoint directories also should be adjusted to the machine where you run the code.

Checkpoint directories are the arguments for DeepSpeech.py.

--export_dir path to the directory where the model is stored.

--checkpoint_dir path to the directory where the checkpoints for the current model will be stored.

Important:

--source_model_checkpoint_dir is the path to the pretrained English model's checkpoints.

Running experiments

To run the experiments go to the code/ directory. There are two data files: tt_train_with_scores.csv for ranking the data according to compression ration and train.csv (same as in the data/clips/ directory, it is here for convenience).

There are three modes in which we can run main.py UCB1, EXP3 and LinUCB. Below is the example command to run the code with UCB1 algorithm with tunable parameters.

python main.py --mode='UCB1' --df_path='tt_train_with_scores.csv' --num_tasks=3 --csv='train.csv' --num_episodes=15 --batch_size=64 --gain_type='SPG' --c 0.01

For more argument specification run

python main.py --help

Training results are stored in .pickle files for each algorithm:

• sc_reward_hist_{algo name}.pickle for the history of scaled rewards;
• loss_hist_{algo name}.pickle for saving losses;
• action_hist_{algo name}.pickle for action history.

Additionally, there are running log prints in every algorithm where the current Q-function or weights are printed.

NOTE: there could be issues with the DeepSpeech installation. If something emerges, do not hesitate to message me anakuzne@iu.edu.