1.5 KiB
Results
SWBD BPE training results (Conformer-CTC)
01-17-2022
This recipe is based on LibriSpeech. Data preparation/normalization is a simplified version of the one found in Kaldi. The data is resampled to 16kHz on-the-fly -- it's not needed, but makes it easier to combine with other corpora, and likely doesn't affect the results too much. The training set was only Switchboard, minus 20 held-out conversations (dev data, ~1h of speech). This was tested only on the dev data. We didn't tune the model, hparams, or language model in any special way vs. LibriSpeech recipe. No rescoring was used (decoding method: "1best"). The model was trained on a single A100 GPU (24GB RAM) for 2 days.
WER (it includes [LAUGHTER], [NOISE], [VOCALIZED-NOISE] so the "real" WER is likely lower):
10 epochs (avg 5) : 19.58% 20 epochs (avg 10): 12.61% 30 epochs (avg 20): 11.24% 35 epochs (avg 20): 10.96% 40 epochs (avg 20): 10.94%
To reproduce the above result, use the following commands for training:
cd egs/librispeech/ASR/conformer_ctc
./prepare.sh --swbd-only true
export CUDA_VISIBLE_DEVICES="0"
./conformer_ctc/train.py \
--lr-factor 1.25 \
--max-duration 200 \
--num-workers 14 \
--lang-dir data/lang_bpe_500 \
--num-epochs 40
and the following command for decoding
python conformer_ctc/decode.py \
--epoch 40 \
--avg 20 \
--method 1best
The tensorboard log for training is available at https://tensorboard.dev/experiment/0mvXl9BYRJ62J1fVnILm0w/