### Setup
`. env/bin/activate` to activate the virtualenv.

### Data Generation
* update `OUTPUT_NAME` in *speech_samplegen.py* to create the dataset folder with the name
* `python speech_samplegen.py` generates variants of audio samples

### Data Preprocessing
* `python speech_data.py` creates the training-testing data from the generated samples.
* run `fix_csv(OUTPUT_NAME)` once to create the fixed index of the dataset generated
* run `generate_sppas_trans(OUTPUT_NAME)` once to create the SPPAS transcription(wav+txt) data
* run `$ (SPPAS_DIR)/bin/annotation.py -l eng -e csv --ipus --tok --phon --align --align -w ./outputs/OUTPUT_NAME/` once to create the phoneme alignment csv files for all variants.
* `create_seg_phonpair_tfrecords(OUTPUT_NAME)` creates the tfrecords files
 with the phoneme level pairs of right/wrong stresses

### Training
* `python speech_model.py` trains the model with the training data generated.
* `train_siamese(OUTPUT_NAME)` trains the siamese model with the generated dataset.

### Testing
* `python speech_test.py` tests the trained model with the test dataset
* `evaluate_siamese(TEST_RECORD_FILE,audio_group=OUTPUT_NAME,weights = WEIGHTS_FILE_NAME)`
  the TEST_RECORD_FILE will be under outputs directory and WEIGHTS_FILE_NAME will be under the models directory, pick the most recent weights file.