## Results

### GigaSpeech BPE training results (Conformer-CTC)

#### 2022-04-06

The best WER, as of 2022-04-06, for the gigaspeech is below
(using HLG decoding + n-gram LM rescoring + attention decoder rescoring):

|     |  Dev  | Test  |
|-----|-------|-------|
| WER | 11.93 | 11.86 |

Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
| ngram_lm_scale | attention_scale |
|----------------|-----------------|
|      0.3       |        1.5      |


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

```
cd egs/gigaspeech/ASR/conformer_ctc
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./conformer_ctc/train.py \
  --max-duration 120 \
  --num-workers 1 \
  --world-size 8 \
  --exp-dir conformer_ctc/exp_500 \
  --lang-dir data/lang_bpe_500
```

and the following command for decoding

```
./conformer_ctc/decode.py \
  --epoch 19 \
  --avg 8 \
  --method attention-decoder \
  --num-paths 1000 \
  --exp-dir conformer_ctc/exp_500 \
  --lang-dir data/lang_bpe_500 \
  --max-duration 20 \
  --num-workers 1
```

The tensorboard log for training is available at
<https://tensorboard.dev/experiment/rz63cmJXSK2fV9GceJtZXQ/>