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Results

Switchboard BPE training results (Conformer-CTC)

2023-09-04

The best WER, as of 2023-09-04, for the Switchboard is below

Results using attention decoder are given as:

eval2000-swbd eval2000-callhome eval2000-avg
conformer_ctc 9.48 17.73 13.67

Decoding results and models can be found here: https://huggingface.co/zrjin/icefall-asr-swbd-conformer-ctc-2023-8-26

2023-06-27

The best WER, as of 2023-06-27, for the Switchboard is below

Results using HLG decoding + n-gram LM rescoring + attention decoder rescoring:

eval2000 rt03
conformer_ctc 30.80 32.29

Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:

eval2000
ngram_lm_scale attention_scale
0.9 1.1
rt03
ngram_lm_scale attention_scale
0.9 1.9

To reproduce the above result, use the following commands for training:

cd egs/swbd/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./conformer_ctc/train.py \
  --max-duration 120 \
  --num-workers 8 \
  --enable-musan False \
  --world-size 2 \
  --num-epochs 100

and the following command for decoding:

./conformer_ctc/decode.py \
  --epoch 99 \
  --avg 10 \
  --max-duration 50

2023-06-26

The best WER, as of 2023-06-26, for the Switchboard is below

Results using HLG decoding + n-gram LM rescoring + attention decoder rescoring:

eval2000 rt03
conformer_ctc 33.37 35.06

Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:

eval2000
ngram_lm_scale attention_scale
0.3 2.5
rt03
ngram_lm_scale attention_scale
0.7 1.3

To reproduce the above result, use the following commands for training:

cd egs/swbd/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./conformer_ctc/train.py \
  --max-duration 120 \
  --num-workers 8 \
  --enable-musan False \
  --world-size 2 \

and the following command for decoding:

./conformer_ctc/decode.py \
  --epoch 55 \
  --avg 1 \
  --max-duration 50

For your reference, the nbest oracle WERs are:

eval2000 rt03
conformer_ctc 25.64 26.84