mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-13 19:14:20 +00:00
102 lines
2.4 KiB
Markdown
102 lines
2.4 KiB
Markdown
## Results
|
|
### Switchboard BPE training results (Conformer-CTC)
|
|
|
|
#### 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:
|
|
|
|
```bash
|
|
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:
|
|
|
|
```bash
|
|
./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:
|
|
|
|
```bash
|
|
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:
|
|
|
|
```bash
|
|
./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 |
|