mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
153 lines
4.0 KiB
Markdown
153 lines
4.0 KiB
Markdown
## Results
|
|
### GigaSpeech BPE training results (Pruned Transducer 2)
|
|
|
|
#### 2022-05-12
|
|
|
|
#### Conformer encoder + embedding decoder
|
|
|
|
Conformer encoder + non-recurrent decoder. The encoder is a
|
|
reworked version of the conformer encoder, with many changes. The
|
|
decoder contains only an embedding layer, a Conv1d (with kernel
|
|
size 2) and a linear layer (to transform tensor dim). k2 pruned
|
|
RNN-T loss is used.
|
|
|
|
The best WER, as of 2022-05-12, for the gigaspeech is below
|
|
|
|
Results are:
|
|
|
|
| | Dev | Test |
|
|
|----------------------|-------|-------|
|
|
| greedy search | 10.51 | 10.73 |
|
|
| fast beam search | 10.50 | 10.69 |
|
|
| modified beam search | 10.40 | 10.51 |
|
|
|
|
To reproduce the above result, use the following commands for training:
|
|
|
|
```bash
|
|
cd egs/gigaspeech/ASR
|
|
./prepare.sh
|
|
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
|
|
./pruned_transducer_stateless2/train.py \
|
|
--max-duration 120 \
|
|
--num-workers 1 \
|
|
--world-size 8 \
|
|
--exp-dir pruned_transducer_stateless2/exp \
|
|
--bpe-model data/lang_bpe_500/bpe.model \
|
|
--use-fp16 True
|
|
```
|
|
|
|
and the following commands for decoding:
|
|
|
|
```bash
|
|
# greedy search
|
|
./pruned_transducer_stateless2/decode.py \
|
|
--iter 3488000 \
|
|
--avg 20 \
|
|
--decoding-method greedy_search \
|
|
--exp-dir pruned_transducer_stateless2/exp \
|
|
--bpe-model data/lang_bpe_500/bpe.model \
|
|
--max-duration 600
|
|
|
|
# fast beam search
|
|
./pruned_transducer_stateless2/decode.py \
|
|
--iter 3488000 \
|
|
--avg 20 \
|
|
--decoding-method fast_beam_search \
|
|
--exp-dir pruned_transducer_stateless2/exp \
|
|
--bpe-model data/lang_bpe_500/bpe.model \
|
|
--max-duration 600
|
|
|
|
# modified beam search
|
|
./pruned_transducer_stateless2/decode.py \
|
|
--iter 3488000 \
|
|
--avg 15 \
|
|
--decoding-method modified_beam_search \
|
|
--exp-dir pruned_transducer_stateless2/exp \
|
|
--bpe-model data/lang_bpe_500/bpe.model \
|
|
--max-duration 600
|
|
```
|
|
|
|
Pretrained model is available at
|
|
<https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2>
|
|
|
|
The tensorboard log for training is available at
|
|
<https://tensorboard.dev/experiment/zmmM0MLASnG1N2RmJ4MZBw/>
|
|
|
|
### GigaSpeech BPE training results (Conformer-CTC)
|
|
|
|
#### 2022-04-06
|
|
|
|
The best WER, as of 2022-04-06, for the gigaspeech is below
|
|
|
|
Results using HLG decoding + n-gram LM rescoring + attention decoder rescoring:
|
|
|
|
| | Dev | Test |
|
|
|-----|-------|-------|
|
|
| WER | 10.47 | 10.58 |
|
|
|
|
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
|
|
| ngram_lm_scale | attention_scale |
|
|
|----------------|-----------------|
|
|
| 0.5 | 1.3 |
|
|
|
|
|
|
To reproduce the above result, use the following commands for training:
|
|
|
|
```bash
|
|
cd egs/gigaspeech/ASR
|
|
./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:
|
|
|
|
```bash
|
|
./conformer_ctc/decode.py \
|
|
--epoch 18 \
|
|
--avg 6 \
|
|
--method attention-decoder \
|
|
--num-paths 1000 \
|
|
--exp-dir conformer_ctc/exp_500 \
|
|
--lang-dir data/lang_bpe_500 \
|
|
--max-duration 20 \
|
|
--num-workers 1
|
|
```
|
|
|
|
Results using HLG decoding + whole lattice rescoring:
|
|
|
|
| | Dev | Test |
|
|
|-----|-------|-------|
|
|
| WER | 10.51 | 10.62 |
|
|
|
|
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
|
|
| lm_scale |
|
|
|----------|
|
|
| 0.2 |
|
|
|
|
To reproduce the above result, use the training commands above, and the following command for decoding:
|
|
|
|
```bash
|
|
./conformer_ctc/decode.py \
|
|
--epoch 18 \
|
|
--avg 6 \
|
|
--method whole-lattice-rescoring \
|
|
--num-paths 1000 \
|
|
--exp-dir conformer_ctc/exp_500 \
|
|
--lang-dir data/lang_bpe_500 \
|
|
--max-duration 20 \
|
|
--num-workers 1
|
|
```
|
|
Note: the `whole-lattice-rescoring` method is about twice as fast as the `attention-decoder` method, with slightly worse WER.
|
|
|
|
Pretrained model is available at
|
|
<https://huggingface.co/wgb14/icefall-asr-gigaspeech-conformer-ctc>
|
|
|
|
The tensorboard log for training is available at
|
|
<https://tensorboard.dev/experiment/rz63cmJXSK2fV9GceJtZXQ/>
|