Data preparation
If you want to ./prepare.sh to download everything for you,
you can just run
./prepare.sh
If you have pre-downloaded the LibriSpeech dataset, please
read ./prepare.sh and modify it to point to the location
of your dataset so that it won't re-download it. After modification,
please run
./prepare.sh
The script ./prepare.sh prepare features, lexicon, LMs, etc.
All generated files are saved in the folder ./data.
HINT: ./prepare.sh support options --stage and --stop-stage.
TDNN-LSTM CTC training
The folder tdnn_lstm_ctc contains scripts for CTC training
with TDNN-LSTM models.
Pre-configured parameters for training and decoding are set in the function
get_params() within tdnn_lstm_ctc/train.py
and tdnn_lstm_ctc/decode.py.
Parameters that can be passed from commandlin can be found by
./tdnn_lstm_ctc/train.py --help
./tdnn_lstm_ctc/decode.py --help
If you have 4 GPUs on a machine and want to use GPU 0, 2, 3 for mutli-GPU training, you can run
export CUDA_VISIBLE_DEVICES="0,2,3"
./tdnn_lstm_ctc/train.py \
--master-port 12345 \
--world-size 3
If you want to decode by averaging checkpoints epoch-8.pt,
epoch-9.pt and epoch-10.pt, you can run
./tdnn_lstm_ctc/decode.py \
--epoch 10 \
--avg 3
Conformer CTC training
The folder conformer-ctc contains scripts for CTC training
with conformer models. The steps of running the training and
decoding are similar as tdnn_lstm_ctc.