We currently provide recipe for the baseline model on TIMIT used in the paper.
At first, prepare data for training (set the paths instead of [...], [DATA_DST] and [MODEL_DST])
python prepare.py \
--src [...]/timit \
--data_dst [DATA_DST] \
--model_dst [MODEL_DST] \
--sph2pipe [...]/sph2pipe_v2.5/sph2pipe
Besides TIMIT data the auxiliary files for acoustic model training/evaluation will be generated:
cd $MODEL_DST
tree -L 2
.
├── am
│ ├── lexicon.txt
│ └── tokens.txt
To train the baseline model run (Set the full path to wav2letter for [...]).
[...]/wav2letter/build/Train train --flagsfile train_baseline_conv_relu.cfg --minloglevel=0 --logtostderr=1
@inproceedings{zeghidour2018learning,
title={Learning filterbanks from raw speech for phone recognition},
author={Zeghidour, Neil and Usunier, Nicolas and Kokkinos, Iasonas and Schaiz, Thomas and Synnaeve, Gabriel and Dupoux, Emmanuel},
booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={5509--5513},
year={2018},
organization={IEEE}
}