Train new model from start conditioning on 9 speakers.
As a result one can understand the spoken language but disturbances are too obvious.
Additionally speaker 12: D17 had originally too high noise level that was not cleaned up automaticcaly, thus has to be replaced with another speaker.
Experiment evaluation results
Following utterances are generated using evaluate.py script. First raw represents predicted utternace and the second is ground truth
target.
Speaker 7 utterance 0
Speaker 12 utterance 2
Speaker 4 utterance 3
Speaker 11 utterance 4
Speaker 3 utterance 5
Speaker 15 utterance 10
Speaker 17 utterance 17
Speaker 1 utterance 23
Speakers
The speaker indeces correspond to speker directories in Liepa database in order specified in dataset (See var. available_speakers). Notice that only speakers with sentence utterances were considered.