########## BASELINE FOR ZEROSPEECH 2019##### This baseline works as two independant systems. First it trains BEER (https://github.com/beer-asr/beer) an acoustic unit discovery algorithm. From a dataset of audio files, it learns an unsupervised set of acoustic units. These phones are then fed into OSSIAN (https://github.com/CSTR-Edinburgh/Ossian), a text-to-speech pipeline. From sentences, written with units from BEER, OSSIAN learns to synthesize speech. ##########HOW TO TRAIN THE BASELINE AND BUILD A SUBMISSION########## # All dependancies and systems are pre installed in the docker. You can check what was installed by reading set up scripts at: vim $HOME/baseline/set_up_baseline.sh vim $HOME/eval/set_up_eval.sh # Train on toy dataset: cd $HOME/baseline/training bash train_baseline.sh /home/zs2019/baseline /home/zs2019/databases/english_small/ 1 # Train on full dataset bash train_baseline.sh /home/zs2019/baseline /home/zs2019/databases/english/ 1 # Decode sentences and make a submission from the baseline bash decode_and_make_submission.sh /home/zs2019 submission ##########HOW TO EVALUATE A SUBMISSION############################## # A submission for the english database should be structured like this : $HOME/submission: metadata code/* english/test/*.txt and *.wav # You need to validate your submission before evaluating it cd $HOME/tools/ bash validate.sh $HOME/baseline/training/submission/zip english # You can then run the evaluation script (note that the evaluation script will also do the validation by default) cd $HOME/tools/eval bash evaluate_submission.sh /home/zs2019/ /home/zs2019/submission