mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-08-09 18:12:19 +00:00
Code Issues Packages Projects Releases Wiki Activity
icefall/egs/librispeech/ASR/RESULTS.md
Fangjun Kuang 3607c516d6
Update results for torchaudio RNN-T. (#322)
2022-04-20 11:15:10 +08:00

669 lines
24 KiB
Markdown
Raw Blame History

## Results
### LibriSpeech BPE training results (Pruned Transducer 2)
[pruned_transducer_stateless2](./pruned_transducer_stateless2)
This is with a reworked version of the conformer encoder, with many changes.
#### Training on fulll librispeech
Using commit `34aad74a2c849542dd5f6359c9e6b527e8782fd6`.
See <https://github.com/k2-fsa/icefall/pull/288>
The WERs are:
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-------------------------------------------------------------------------------|
| greedy search (max sym per frame 1) | 2.62 | 6.37 | --epoch 25 --avg 8 --max-duration 600 |
| fast beam search | 2.61 | 6.17 | --epoch 25 --avg 8 --max-duration 600 --decoding-method fast_beam_search |
| modified beam search | 2.59 | 6.19 | --epoch 25 --avg 8 --max-duration 600 --decoding-method modified_beam_search |
| greedy search (max sym per frame 1) | 2.70 | 6.04 | --epoch 34 --avg 10 --max-duration 600 |
| fast beam search | 2.66 | 6.00 | --epoch 34 --avg 10 --max-duration 600 --decoding-method fast_beam_search |
| greedy search (max sym per frame 1) | 2.62 | 6.03 | --epoch 38 --avg 10 --max-duration 600 |
| fast beam search | 2.57 | 5.95 | --epoch 38 --avg 10 --max-duration 600 --decoding-method fast_beam_search |
The train and decode commands are:
`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp --world-size 8 --num-epochs 26 --full-libri 1 --max-duration 300`
and:
`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp --epoch 25 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600`
The Tensorboard log is at <https://tensorboard.dev/experiment/Xoz0oABMTWewo1slNFXkyA> (apologies, log starts
only from epoch 3).
#### Training on train-clean-100:
Trained with 1 job:
`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws1 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 300`
and decoded with:
`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws1 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600`.
The Tensorboard log is at <https://tensorboard.dev/experiment/AhnhooUBRPqTnaggoqo7lg> (learning rate
schedule is not visible due to a since-fixed bug).
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-------------------------------------------------------|
| greedy search (max sym per frame 1) | 7.12 | 18.42 | --epoch 19 --avg 8 |
| greedy search (max sym per frame 1) | 6.71 | 17.77 | --epoch 29 --avg 8 |
| greedy search (max sym per frame 1) | 6.64 | 17.19 | --epoch 39 --avg 10 |
| fast beam search | 6.58 | 17.27 | --epoch 29 --avg 8 --decoding-method fast_beam_search |
| fast beam search | 6.53 | 16.82 | --epoch 39 --avg 10 --decoding-method fast_beam_search |
Trained with 2 jobs:
`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws2 --world-size 2 --num-epochs 40 --full-libri 0 --max-duration 300`
and decoded with:
`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws2 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600`.
The Tensorboard log is at <https://tensorboard.dev/experiment/dvOC9wsrSdWrAIdsebJILg/>
(learning rate schedule is not visible due to a since-fixed bug).
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-----------------------|
| greedy search (max sym per frame 1) | 7.05 | 18.77 | --epoch 19 --avg 8 |
| greedy search (max sym per frame 1) | 6.82 | 18.14 | --epoch 29 --avg 8 |
| greedy search (max sym per frame 1) | 6.81 | 17.66 | --epoch 30 --avg 10 |
Trained with 4 jobs:
`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws4 --world-size 4 --num-epochs 40 --full-libri 0 --max-duration 300`
and decoded with:
`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws4 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600`.
The Tensorboard log is at <https://tensorboard.dev/experiment/a3T0TyC0R5aLj5bmFbRErA/>
(learning rate schedule is not visible due to a since-fixed bug).
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-----------------------|
| greedy search (max sym per frame 1) | 7.31 | 19.55 | --epoch 19 --avg 8 |
| greedy search (max sym per frame 1) | 7.08 | 18.59 | --epoch 29 --avg 8 |
| greedy search (max sym per frame 1) | 6.86 | 18.29 | --epoch 30 --avg 10 |
Trained with 1 job, with --use-fp16=True --max-duration=300 i.e. with half-precision
floats (but without increasing max-duration), after merging <https://github.com/k2-fsa/icefall/pull/305>.
Train command was
`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 300 --use-fp16 True`
The Tensorboard log is at <https://tensorboard.dev/experiment/DAtGG9lpQJCROUDwPNxwpA>
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-----------------------|
| greedy search (max sym per frame 1) | 7.10 | 18.57 | --epoch 19 --avg 8 |
| greedy search (max sym per frame 1) | 6.81 | 17.84 | --epoch 29 --avg 8 |
| greedy search (max sym per frame 1) | 6.63 | 17.39 | --epoch 30 --avg 10 |
Trained with 1 job, with --use-fp16=True --max-duration=500, i.e. with half-precision
floats and max-duration increased from 300 to 500, after merging <https://github.com/k2-fsa/icefall/pull/305>.
Train command was
`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 500 --use-fp16 True`
The Tensorboard log is at <https://tensorboard.dev/experiment/Km7QBHYnSLWs4qQnAJWsaA>
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-----------------------|
| greedy search (max sym per frame 1) | 7.10 | 18.79 | --epoch 19 --avg 8 |
| greedy search (max sym per frame 1) | 6.92 | 18.16 | --epoch 29 --avg 8 |
| greedy search (max sym per frame 1) | 6.89 | 17.75 | --epoch 30 --avg 10 |
### LibriSpeech BPE training results (Pruned Transducer)
Conformer encoder + non-current decoder. The decoder
contains only an embedding layer, a Conv1d (with kernel size 2) and a linear
layer (to transform tensor dim).
#### 2022-03-12
[pruned_transducer_stateless](./pruned_transducer_stateless)
Using commit `1603744469d167d848e074f2ea98c587153205fa`.
See <https://github.com/k2-fsa/icefall/pull/248>
The WERs are:
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 2.62 | 6.37 | --epoch 42 --avg 11 --max-duration 100 |
| greedy search (max sym per frame 2) | 2.62 | 6.37 | --epoch 42 --avg 11 --max-duration 100 |
| greedy search (max sym per frame 3) | 2.62 | 6.37 | --epoch 42 --avg 11 --max-duration 100 |
| modified beam search (beam size 4) | 2.56 | 6.27 | --epoch 42 --avg 11 --max-duration 100 |
| beam search (beam size 4) | 2.57 | 6.27 | --epoch 42 --avg 11 --max-duration 100 |
The decoding time for `test-clean` and `test-other` is given below:
(A V100 GPU with 32 GB RAM is used for decoding. Note: Not all GPU RAM is used during decoding.)
| decoding method | test-clean (seconds) | test-other (seconds)|
|---|---:|---:|
| greedy search (--max-sym-per-frame=1) | 160 | 159 |
| greedy search (--max-sym-per-frame=2) | 184 | 177 |
| greedy search (--max-sym-per-frame=3) | 210 | 213 |
| modified beam search (--beam-size 4)| 273 | 269 |
|beam search (--beam-size 4) | 2741 | 2221 |
We recommend you to use `modified_beam_search`.
Training command:
```bash
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
. path.sh
./pruned_transducer_stateless/train.py \
--world-size 8 \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless/exp \
--full-libri 1 \
--max-duration 300 \
--prune-range 5 \
--lr-factor 5 \
--lm-scale 0.25
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/WKRFY5fYSzaVBHahenpNlA/>
The command for decoding is:
```bash
epoch=42
avg=11
sym=1
# greedy search
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless/exp \
--max-duration 100 \
--decoding-method greedy_search \
--beam-size 4 \
--max-sym-per-frame $sym
# modified beam search
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless/exp \
--max-duration 100 \
--decoding-method modified_beam_search \
--beam-size 4
# beam search
# (not recommended)
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless/exp \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
You can find a pre-trained model, decoding logs, and decoding results at
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12>
#### 2022-02-18
[pruned_transducer_stateless](./pruned_transducer_stateless)
The WERs are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.85 | 6.98 | --epoch 28 --avg 15 --max-duration 100 |
The training command for reproducing is given below:
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless/exp \
--full-libri 1 \
--max-duration 300 \
--prune-range 5 \
--lr-factor 5 \
--lm-scale 0.25 \
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/ejG7VpakRYePNNj6AbDEUw/#scalars>
The decoding command is:
```
epoch=28
avg=15
## greedy search
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir pruned_transducer_stateless/exp \
--max-duration 100
```
### LibriSpeech BPE training results (Transducer)
#### Conformer encoder + embedding decoder
Conformer encoder + non-recurrent decoder. The decoder
contains only an embedding layer and a Conv1d (with kernel size 2).
See
- [./transducer_stateless](./transducer_stateless)
- [./transducer_stateless_multi_datasets](./transducer_stateless_multi_datasets)
##### 2022-03-01
Using commit `2332ba312d7ce72f08c7bac1e3312f7e3dd722dc`.
It uses [GigaSpeech](https://github.com/SpeechColab/GigaSpeech)
as extra training data. 20% of the time it selects a batch from L subset of
GigaSpeech and 80% of the time it selects a batch from LibriSpeech.
The WERs are
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 2.64 | 6.55 | --epoch 39 --avg 15 --max-duration 100 |
| modified beam search (beam size 4) | 2.61 | 6.46 | --epoch 39 --avg 15 --max-duration 100 |
The training command for reproducing is given below:
```bash
cd egs/librispeech/ASR/
./prepare.sh
./prepare_giga_speech.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless_multi_datasets/train.py \
--world-size 4 \
--num-epochs 40 \
--start-epoch 0 \
--exp-dir transducer_stateless_multi_datasets/exp-full-2 \
--full-libri 1 \
--max-duration 300 \
--lr-factor 5 \
--bpe-model data/lang_bpe_500/bpe.model \
--modified-transducer-prob 0.25 \
--giga-prob 0.2
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/xmo5oCgrRVelH9dCeOkYBg/>
The decoding command is:
```bash
epoch=39
avg=15
sym=1
# greedy search
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-full-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--max-sym-per-frame $sym
# modified beam search
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-full-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
```
You can find a pretrained model by visiting
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01>
##### 2022-04-19
[transducer_stateless2](./transducer_stateless2)
This version uses torchaudio's RNN-T loss.
Using commit `fce7f3cd9a486405ee008bcbe4999264f27774a3`.
See <https://github.com/k2-fsa/icefall/pull/316>
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|--------------------------------------------------------------------------------|
| greedy search (max sym per frame 1) | 2.65 | 6.30 | --epoch 59 --avg 10 --max-duration 600 |
| greedy search (max sym per frame 2) | 2.62 | 6.23 | --epoch 59 --avg 10 --max-duration 100 |
| greedy search (max sym per frame 3) | 2.62 | 6.23 | --epoch 59 --avg 10 --max-duration 100 |
| modified beam search | 2.63 | 6.15 | --epoch 59 --avg 10 --max-duration 100 --decoding-method modified_beam_search |
| beam search | 2.59 | 6.15 | --epoch 59 --avg 10 --max-duration 100 --decoding-method beam_search |
**Note**: This model is trained with standard RNN-T loss. Neither modified transducer nor pruned RNN-T is used.
You can see that there is a performance degradation in WER when we limit the max symbol per frame to 1.
The number of active paths in `modified_beam_search` and `beam_search` is 4.
The training and decoding commands are:
```bash
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./transducer_stateless2/train.py \
--world-size 8 \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir transducer_stateless2/exp-2 \
--full-libri 1 \
--max-duration 300 \
--lr-factor 5
epoch=59
avg=10
# greedy search
./transducer_stateless2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./transducer_stateless2/exp-2 \
--max-duration 600 \
--decoding-method greedy_search \
--max-sym-per-frame 1
# modified beam search
./transducer_stateless2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./transducer_stateless2/exp-2 \
--max-duration 100 \
--decoding-method modified_beam_search \
# beam search
./transducer_stateless2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./transducer_stateless2/exp-2 \
--max-duration 100 \
--decoding-method beam_search \
```
The tensorboard log is at <https://tensorboard.dev/experiment/oAlle3dxQD2EY8ePwjIGuw/>.
You can find a pre-trained model, decoding logs, and decoding results at
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless2-torchaudio-2022-04-19>
##### 2022-02-07
Using commit `a8150021e01d34ecbd6198fe03a57eacf47a16f2`.
The WERs are
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 2.67 | 6.67 | --epoch 63 --avg 19 --max-duration 100 |
| greedy search (max sym per frame 2) | 2.67 | 6.67 | --epoch 63 --avg 19 --max-duration 100 |
| greedy search (max sym per frame 3) | 2.67 | 6.67 | --epoch 63 --avg 19 --max-duration 100 |
| modified beam search (beam size 4) | 2.67 | 6.57 | --epoch 63 --avg 19 --max-duration 100 |
The training command for reproducing is given below:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 76 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
--max-duration 300 \
--lr-factor 5 \
--bpe-model data/lang_bpe_500/bpe.model \
--modified-transducer-prob 0.25
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/qgvWkbF2R46FYA6ZMNmOjA/#scalars>
The decoding command is:
```
epoch=63
avg=19
## greedy search
for sym in 1 2 3; do
./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 \
--max-sym-per-frame $sym
done
## modified 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 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
```
You can find a pretrained model by visiting
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07>
#### 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: <https://tensorboard.dev/experiment/D7NQc3xqTpyVmWi5FnWjrA>
### 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
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09>
The tensorboard log for training is available at
<https://tensorboard.dev/experiment/hZDWrZfaSqOMqtW0NEfXKg/#scalars>
#### 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|
Reference in New Issue View Git Blame Copy Permalink