## Results
### LibriSpeech BPE training results (Transducer)
#### Conformer encoder + embedding decoder
Using commit `14c93add507982306f5a478cd144e0e32e0f970d`.
Conformer encoder + non-current decoder. The decoder
contains only an embedding layer and a Conv1d (with kernel size 2).
The WERs are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.85 | 7.30 | --epoch 29, --avg 13, --max-duration 100 |
| beam search (beam size 4) | 2.83 | 7.19 | |
The training command for reproducing is given below:
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
--max-duration 250 \
--lr-factor 3
```
The tensorboard training log can be found at
The decoding command is:
```
epoch=29
avg=13
## greedy search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
## beam search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
#### Conformer encoder + LSTM decoder
Using commit `8187d6236c2926500da5ee854f758e621df803cc`.
Conformer encoder + LSTM decoder.
The best WER is
| | test-clean | test-other |
|-----|------------|------------|
| WER | 3.07 | 7.51 |
using `--epoch 34 --avg 11` with **greedy search**.
The training command to reproduce the above WER is:
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer/train.py \
--world-size 4 \
--num-epochs 35 \
--start-epoch 0 \
--exp-dir transducer/exp-lr-2.5-full \
--full-libri 1 \
--max-duration 180 \
--lr-factor 2.5
```
The decoding command is:
```
epoch=34
avg=11
./transducer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer/exp-lr-2.5-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
```
You can find the tensorboard log at:
### LibriSpeech BPE training results (Conformer-CTC)
#### 2021-11-09
The best WER, as of 2021-11-09, for the librispeech test dataset is below
(using HLG decoding + n-gram LM rescoring + attention decoder rescoring):
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.42 | 5.73 |
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
| ngram_lm_scale | attention_scale |
|----------------|-----------------|
| 2.0 | 2.0 |
To reproduce the above result, use the following commands for training:
```
cd egs/librispeech/ASR/conformer_ctc
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./conformer_ctc/train.py \
--exp-dir conformer_ctc/exp_500_att0.8 \
--lang-dir data/lang_bpe_500 \
--att-rate 0.8 \
--full-libri 1 \
--max-duration 200 \
--concatenate-cuts 0 \
--world-size 4 \
--bucketing-sampler 1 \
--start-epoch 0 \
--num-epochs 90
# Note: It trains for 90 epochs, but the best WER is at epoch-77.pt
```
and the following command for decoding
```
./conformer_ctc/decode.py \
--exp-dir conformer_ctc/exp_500_att0.8 \
--lang-dir data/lang_bpe_500 \
--max-duration 30 \
--concatenate-cuts 0 \
--bucketing-sampler 1 \
--num-paths 1000 \
--epoch 77 \
--avg 55 \
--method attention-decoder \
--nbest-scale 0.5
```
You can find the pre-trained model by visiting
The tensorboard log for training is available at
#### 2021-08-19
(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/13
TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars
Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc
The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using `attention-decoder` decoder with num_paths equals to 100.
||test-clean|test-other|
|--|--|--|
|WER| 2.57% | 5.94% |
To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the WER above are also listed below.
||lm_scale|attention_scale|
|--|--|--|
|test-clean|1.3|1.2|
|test-other|1.2|1.1|
You can use the following commands to reproduce our results:
```bash
git clone https://github.com/k2-fsa/icefall
cd icefall
# It was using ef233486, you may not need to switch to it
# git checkout ef233486
cd egs/librispeech/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
--concatenate-cuts False \
--max-duration 200 \
--full-libri True \
--world-size 4 \
--lang-dir data/lang_bpe_5000
python conformer_ctc/decode.py --nbest-scale 0.5 \
--epoch 34 \
--avg 20 \
--method attention-decoder \
--max-duration 20 \
--num-paths 100 \
--lang-dir data/lang_bpe_5000
```
### LibriSpeech training results (Tdnn-Lstm)
#### 2021-08-24
(Wei Kang): Result of phone based Tdnn-Lstm model.
Icefall version: https://github.com/k2-fsa/icefall/commit/caa0b9e9425af27e0c6211048acb55a76ed5d315
Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using `whole-lattice-rescoring` decoding method.
||test-clean|test-other|
|--|--|--|
|WER| 6.59% | 17.69% |
We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below.
||lm_scale|
|--|--|
|test-clean|0.8|
|test-other|0.9|