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icefall/egs/aishell/ASR/RESULTS.md
zr_jin d76c3fe472
Migrate zipformer model to other Chinese datasets (#1216) 
added zipformer recipe for AISHELL-1
2023-10-24 16:24:46 +08:00

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## Results
### Aishell training result (Stateless Transducer)
#### Zipformer (Non-streaming)
[./zipformer](./zipformer)
It's reworked Zipformer with Pruned RNNT loss.
**Caution**: It uses `--context-size=1`.
##### normal-scaled model, number of model parameters: 73412551, i.e., 73.41 M
| | test | dev | comment |
|------------------------|------|------|-----------------------------------------|
| greedy search | 4.67 | 4.37 | --epoch 55 --avg 17 |
| modified beam search | 4.40 | 4.13 | --epoch 55 --avg 17 |
| fast beam search | 4.60 | 4.31 | --epoch 55 --avg 17 |
Command for training is:
```bash
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
--world-size 2 \
--num-epochs 60 \
--start-epoch 1 \
--use-fp16 1 \
--context-size 1 \
--enable-musan 0 \
--exp-dir zipformer/exp \
--max-duration 1000 \
--enable-musan 0 \
--base-lr 0.045 \
--lr-batches 7500 \
--lr-epochs 18 \
--spec-aug-time-warp-factor 20
```
Command for decoding is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./zipformer/decode.py \
--epoch 55 \
--avg 17 \
--exp-dir ./zipformer/exp \
--lang-dir data/lang_char \
--context-size 1 \
--decoding-method $m
done
```
Pretrained models, training logs, decoding logs, tensorboard and decoding results
are available at
<https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-2023-10-24>
##### small-scaled model, number of model parameters: 30167139, i.e., 30.17 M
| | test | dev | comment |
|------------------------|------|------|-----------------------------------------|
| greedy search | 4.97 | 4.67 | --epoch 55 --avg 21 |
| modified beam search | 4.67 | 4.40 | --epoch 55 --avg 21 |
| fast beam search | 4.85 | 4.61 | --epoch 55 --avg 21 |
Command for training is:
```bash
export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
--world-size 2 \
--num-epochs 60 \
--start-epoch 1 \
--use-fp16 1 \
--context-size 1 \
--exp-dir zipformer/exp-small \
--enable-musan 0 \
--base-lr 0.045 \
--lr-batches 7500 \
--lr-epochs 18 \
--spec-aug-time-warp-factor 20 \
--num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,768,768,768,768 \
--encoder-dim 192,256,256,256,256,256 \
--encoder-unmasked-dim 192,192,192,192,192,192 \
--max-duration 1200
```
Command for decoding is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./zipformer/decode.py \
--epoch 55 \
--avg 21 \
--exp-dir ./zipformer/exp-small \
--lang-dir data/lang_char \
--context-size 1 \
--decoding-method $m \
--num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,768,768,768,768 \
--encoder-dim 192,256,256,256,256,256 \
--encoder-unmasked-dim 192,192,192,192,192,192
done
```
Pretrained models, training logs, decoding logs, tensorboard and decoding results
are available at
<https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-small-2023-10-24/>
##### large-scaled model, number of model parameters: 157285130, i.e., 157.29 M
| | test | dev | comment |
|------------------------|------|------|-----------------------------------------|
| greedy search | 4.49 | 4.22 | --epoch 56 --avg 23 |
| modified beam search | 4.28 | 4.03 | --epoch 56 --avg 23 |
| fast beam search | 4.44 | 4.18 | --epoch 56 --avg 23 |
Command for training is:
```bash
export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
--world-size 2 \
--num-epochs 60 \
--use-fp16 1 \
--context-size 1 \
--exp-dir ./zipformer/exp-large \
--enable-musan 0 \
--lr-batches 7500 \
--lr-epochs 18 \
--spec-aug-time-warp-factor 20 \
--num-encoder-layers 2,2,4,5,4,2 \
--feedforward-dim 512,768,1536,2048,1536,768 \
--encoder-dim 192,256,512,768,512,256 \
--encoder-unmasked-dim 192,192,256,320,256,192 \
--max-duration 800
```
Command for decoding is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./zipformer/decode.py \
--epoch 56 \
--avg 23 \
--exp-dir ./zipformer/exp-large \
--lang-dir data/lang_char \
--context-size 1 \
--decoding-method $m \
--num-encoder-layers 2,2,4,5,4,2 \
--feedforward-dim 512,768,1536,2048,1536,768 \
--encoder-dim 192,256,512,768,512,256 \
--encoder-unmasked-dim 192,192,256,320,256,192
done
```
Pretrained models, training logs, decoding logs, tensorboard and decoding results
are available at
<https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-large-2023-10-24/>
#### Pruned transducer stateless 7 streaming
[./pruned_transducer_stateless7_streaming](./pruned_transducer_stateless7_streaming)
It's Streaming version of Zipformer1 with Pruned RNNT loss.
| | test | dev | comment |
|------------------------|------|------|---------------------------------------|
| greedy search | 6.95 | 6.29 | --epoch 44 --avg 15 --max-duration 600 |
| modified beam search | 6.51 | 5.90 | --epoch 44 --avg 15 --max-duration 600 |
| fast beam search | 6.73 | 6.09 | --epoch 44 --avg 15 --max-duration 600 |
Training command is:
```bash
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless7_streaming/train.py \
--world-size 2 \
--num-epochs 50 \
--use-fp16 1 \
--context-size 1 \
--max-duration 800 \
--exp-dir ./pruned_transducer_stateless7_streaming/exp \
--enable-musan 0 \
--spec-aug-time-warp-factor 20
```
**Caution**: It uses `--context-size=1`.
The decoding command is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./pruned_transducer_stateless7_streaming/decode.py \
--epoch 44 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless7_streaming/exp \
--lang-dir data/lang_char \
--context-size 1 \
--decoding-method $m
done
```
Pretrained models, training logs, decoding logs, tensorboard and decoding results
are available at
<https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-streaming-2023-10-16/>
#### Pruned transducer stateless 7
[./pruned_transducer_stateless7](./pruned_transducer_stateless7)
It's Zipformer with Pruned RNNT loss.
| | test | dev | comment |
|------------------------|------|------|---------------------------------------|
| greedy search | 5.02 | 4.61 | --epoch 42 --avg 6 --max-duration 600 |
| modified beam search | 4.81 | 4.4 | --epoch 42 --avg 6 --max-duration 600 |
| fast beam search | 4.91 | 4.52 | --epoch 42 --avg 6 --max-duration 600 |
Training command is:
```bash
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless7/train.py \
--world-size 2 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir pruned_transducer_stateless7/exp \
--context-size 1 \
--max-duration 300
```
**Caution**: It uses `--context-size=1`.
The tensorboard log is available at
<https://tensorboard.dev/experiment/MHYo3ApfQxaCdYLr38cQOQ>
The decoding command is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./pruned_transducer_stateless7/decode.py \
--epoch 42 \
--avg 6 \
--exp-dir ./pruned_transducer_stateless7/exp \
--lang-dir data/lang_char \
--max-duration 300 \
--context-size 1 \
--decoding-method $m
done
```
Pretrained models, training logs, decoding logs, and decoding results
are available at
<https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21>
#### Pruned transducer stateless 7 (zipformer)
See <https://github.com/k2-fsa/icefall/pull/986>
[./pruned_transducer_stateless7_bbpe](./pruned_transducer_stateless7_bbpe)
**Note**: The modeling units are byte level BPEs
The best results I have gotten are:
Vocab size | Greedy search(dev & test) | Modified beam search(dev & test) | Fast beam search (dev & test) | Fast beam search LG (dev & test) | comments
-- | -- | -- | -- | -- | --
500 | 4.31 & 4.59 | 4.25 & 4.54 | 4.27 & 4.55 | 4.07 & 4.38 | --epoch 48 --avg 29
The training command:
```
export CUDA_VISIBLE_DEVICES="4,5,6,7"
./pruned_transducer_stateless7_bbpe/train.py \
--world-size 4 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--max-duration 800 \
--bpe-model data/lang_bbpe_500/bbpe.model \
--exp-dir pruned_transducer_stateless7_bbpe/exp \
--lr-epochs 6 \
--master-port 12535
```
The decoding command:
```
for m in greedy_search modified_beam_search fast_beam_search fast_beam_search_LG; do
./pruned_transducer_stateless7_bbpe/decode.py \
--epoch 48 \
--avg 29 \
--exp-dir ./pruned_transducer_stateless7_bbpe/exp \
--max-sym-per-frame 1 \
--ngram-lm-scale 0.25 \
--ilme-scale 0.2 \
--bpe-model data/lang_bbpe_500/bbpe.model \
--max-duration 2000 \
--decoding-method $m
done
```
The pretrained model is available at: https://huggingface.co/pkufool/icefall_asr_aishell_pruned_transducer_stateless7_bbpe
#### Pruned transducer stateless 3
See <https://github.com/k2-fsa/icefall/pull/436>
[./pruned_transducer_stateless3](./pruned_transducer_stateless3)
It uses pruned RNN-T.
| | test | dev | comment |
|------------------------|------|------|---------------------------------------|
| greedy search | 5.39 | 5.09 | --epoch 29 --avg 5 --max-duration 600 |
| modified beam search | 5.05 | 4.79 | --epoch 29 --avg 5 --max-duration 600 |
| modified beam search + RNNLM shallow fusion | 4.73 | 4.53 | --epoch 29 --avg 5 --max-duration 600 |
| modified beam search + LODR | 4.57 | 4.37 | --epoch 29 --avg 5 --max-duration 600 |
| fast beam search | 5.13 | 4.91 | --epoch 29 --avg 5 --max-duration 600 |
Training command is:
```bash
./prepare.sh
./prepare_aidatatang_200zh.sh
export CUDA_VISIBLE_DEVICES="4,5,6,7"
./pruned_transducer_stateless3/train.py \
--exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \
--world-size 4 \
--max-duration 200 \
--datatang-prob 0.5 \
--start-epoch 1 \
--num-epochs 30 \
--use-fp16 1 \
--num-encoder-layers 12 \
--dim-feedforward 2048 \
--nhead 8 \
--encoder-dim 512 \
--context-size 1 \
--decoder-dim 512 \
--joiner-dim 512 \
--master-port 12356
```
**Caution**: It uses `--context-size=1`.
The tensorboard log is available at
<https://tensorboard.dev/experiment/OKKacljwR6ik7rbDr5gMqQ>
The decoding command is:
```bash
for epoch in 29; do
for avg in 5; do
for m in greedy_search modified_beam_search fast_beam_search; do
./pruned_transducer_stateless3/decode.py \
--exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \
--epoch $epoch \
--avg $avg \
--use-averaged-model 1 \
--max-duration 600 \
--decoding-method $m \
--num-encoder-layers 12 \
--dim-feedforward 2048 \
--nhead 8 \
--context-size 1 \
--encoder-dim 512 \
--decoder-dim 512 \
--joiner-dim 512
done
done
done
```
We provide the option of shallow fusion with a RNN language model. The pre-trained language model is
available at <https://huggingface.co/marcoyang/icefall-aishell-rnn-lm>. To decode with the language model,
please use the following command:
```bash
# download pre-trained model
git lfs install
git clone https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20
aishell_exp=icefall-aishell-pruned-transducer-stateless3-2022-06-20/
pushd ${aishell_exp}/exp
ln -s pretrained-epoch-29-avg-5-torch-1.10.0.pt epoch-99.pt
popd
# download RNN LM
git lfs install
git clone https://huggingface.co/marcoyang/icefall-aishell-rnn-lm
rnnlm_dir=icefall-aishell-rnn-lm
# RNNLM shallow fusion
for lm_scale in $(seq 0.26 0.02 0.34); do
python ./pruned_transducer_stateless3/decode.py \
--epoch 99 \
--avg 1 \
--lang-dir ${aishell_exp}/data/lang_char \
--exp-dir ${aishell_exp}/exp \
--use-averaged-model False \
--decoding-method modified_beam_search_lm_shallow_fusion \
--use-shallow-fusion 1 \
--lm-type rnn \
--lm-exp-dir ${rnnlm_dir}/exp \
--lm-epoch 99 \
--lm-scale $lm_scale \
--lm-avg 1 \
--rnn-lm-embedding-dim 2048 \
--rnn-lm-hidden-dim 2048 \
--rnn-lm-num-layers 2 \
--lm-vocab-size 4336
done
# RNNLM Low-order density ratio (LODR) with a 2-gram
cp ${rnnlm_dir}/2gram.fst.txt ${aishell_exp}/data/lang_char/2gram.fst.txt
for lm_scale in 0.48; do
for LODR_scale in -0.28; do
python ./pruned_transducer_stateless3/decode.py \
--epoch 99 \
--avg 1 \
--lang-dir ${aishell_exp}/data/lang_char \
--exp-dir ${aishell_exp}/exp \
--use-averaged-model False \
--decoding-method modified_beam_search_LODR \
--use-shallow-fusion 1 \
--lm-type rnn \
--lm-exp-dir ${rnnlm_dir}/exp \
--lm-epoch 99 \
--lm-scale $lm_scale \
--lm-avg 1 \
--rnn-lm-embedding-dim 2048 \
--rnn-lm-hidden-dim 2048 \
--rnn-lm-num-layers 2 \
--lm-vocab-size 4336 \
--tokens-ngram 2 \
--backoff-id 4336 \
--ngram-lm-scale $LODR_scale
done
done
```
Pretrained models, training logs, decoding logs, and decoding results
are available at
<https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20>
We have a tutorial in [sherpa](https://github.com/k2-fsa/sherpa) about how
to use the pre-trained model for non-streaming ASR. See
<https://k2-fsa.github.io/sherpa/offline_asr/conformer/aishell.html>
#### Pruned transducer stateless 2
See https://github.com/k2-fsa/icefall/pull/536
[./pruned_transducer_stateless2](./pruned_transducer_stateless2)
It uses pruned RNN-T.
| | test | dev | comment |
| -------------------- | ---- | ---- | -------------------------------------- |
| greedy search | 5.20 | 4.78 | --epoch 72 --avg 14 --max-duration 200 |
| modified beam search | 5.07 | 4.63 | --epoch 72 --avg 14 --max-duration 200 |
| fast beam search | 5.13 | 4.70 | --epoch 72 --avg 14 --max-duration 200 |
Training command is:
```bash
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless2/train.py \
--world-size 2 \
--num-epochs 90 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless2/exp \
--max-duration 200 \
```
The tensorboard log is available at
https://tensorboard.dev/experiment/QI3PVzrGRrebxpbWUPwmkA/
The decoding command is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./pruned_transducer_stateless2/decode.py \
--epoch 72 \
--avg 14 \
--exp-dir ./pruned_transducer_stateless2/exp \
--lang-dir data/lang_char \
--max-duration 200 \
--decoding-method $m
done
```
Pretrained models, training logs, decoding logs, and decoding results
are available at
<https://huggingface.co/teapoly/icefall-aishell-pruned-transducer-stateless2-2022-08-18>
#### 2022-03-01
[./transducer_stateless_modified-2](./transducer_stateless_modified-2)
It uses [optimized_transducer](https://github.com/csukuangfj/optimized_transducer)
for computing RNN-T loss.
Stateless transducer + modified transducer + using [aidatatang_200zh](http://www.openslr.org/62/) as extra training data.
| | test |comment |
|------------------------|------|----------------------------------------------------------------|
| greedy search | 4.94 |--epoch 89, --avg 38, --max-duration 100, --max-sym-per-frame 1 |
| modified beam search | 4.68 |--epoch 89, --avg 38, --max-duration 100 --beam-size 4 |
The training commands are:
```bash
cd egs/aishell/ASR
./prepare.sh --stop-stage 6
./prepare_aidatatang_200zh.sh
export CUDA_VISIBLE_DEVICES="0,1,2"
./transducer_stateless_modified-2/train.py \
--world-size 3 \
--num-epochs 90 \
--start-epoch 0 \
--exp-dir transducer_stateless_modified-2/exp-2 \
--max-duration 250 \
--lr-factor 2.0 \
--context-size 2 \
--modified-transducer-prob 0.25 \
--datatang-prob 0.2
```
The tensorboard log is available at
<https://tensorboard.dev/experiment/oG72ZlWaSGua6fXkcGRRjA/>
The commands for decoding are
```bash
# greedy search
for epoch in 89; do
for avg in 38; do
./transducer_stateless_modified-2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified-2/exp-2 \
--max-duration 100 \
--context-size 2 \
--decoding-method greedy_search \
--max-sym-per-frame 1
done
done
# modified beam search
for epoch in 89; do
for avg in 38; do
./transducer_stateless_modified-2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified-2/exp-2 \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
done
done
```
You can find a pre-trained model, decoding logs, and decoding results at
<https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01>
#### 2022-03-01
[./transducer_stateless_modified](./transducer_stateless_modified)
Stateless transducer + modified transducer.
| | test |comment |
|------------------------|------|----------------------------------------------------------------|
| greedy search | 5.22 |--epoch 64, --avg 33, --max-duration 100, --max-sym-per-frame 1 |
| modified beam search | 5.02 |--epoch 64, --avg 33, --max-duration 100 --beam-size 4 |
The training commands are:
```bash
cd egs/aishell/ASR
./prepare.sh --stop-stage 6
export CUDA_VISIBLE_DEVICES="0,1,2"
./transducer_stateless_modified/train.py \
--world-size 3 \
--num-epochs 90 \
--start-epoch 0 \
--exp-dir transducer_stateless_modified/exp-4 \
--max-duration 250 \
--lr-factor 2.0 \
--context-size 2 \
--modified-transducer-prob 0.25
```
The tensorboard log is available at
<https://tensorboard.dev/experiment/C27M8YxRQCa1t2XglTqlWg/>
The commands for decoding are
```bash
# greedy search
for epoch in 64; do
for avg in 33; do
./transducer_stateless_modified/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified/exp-4 \
--max-duration 100 \
--context-size 2 \
--decoding-method greedy_search \
--max-sym-per-frame 1
done
done
# modified beam search
for epoch in 64; do
for avg in 33; do
./transducer_stateless_modified/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified/exp-4 \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
done
done
```
You can find a pre-trained model, decoding logs, and decoding results at
<https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01>
#### 2022-2-19
(Duo Ma): The tensorboard log for training is available at https://tensorboard.dev/experiment/25PmX3MxSVGTdvIdhOwllw/#scalars
You can find a pretrained model by visiting https://huggingface.co/shuanguanma/icefall_aishell_transducer_stateless_context_size2_epoch60_2022_2_19
| | test |comment |
|---------------------------|------|-----------------------------------------|
| greedy search | 5.4 |--epoch 59, --avg 10, --max-duration 100 |
| beam search | 5.05|--epoch 59, --avg 10, --max-duration 100 |
You can use the following commands to reproduce our results:
```bash
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python3 ./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir exp/transducer_stateless_context_size2 \
--max-duration 100 \
--lr-factor 2.5 \
--context-size 2
lang_dir=data/lang_char
dir=exp/transducer_stateless_context_size2
python3 ./transducer_stateless/decode.py \
--epoch 59 \
--avg 10 \
--exp-dir $dir \
--lang-dir $lang_dir \
--decoding-method greedy_search \
--context-size 2 \
--max-sym-per-frame 3
lang_dir=data/lang_char
dir=exp/transducer_stateless_context_size2
python3 ./transducer_stateless/decode.py \
--epoch 59 \
--avg 10 \
--exp-dir $dir \
--lang-dir $lang_dir \
--decoding-method beam_search \
--context-size 2 \
--max-sym-per-frame 3
```
### Aishell training results (Transducer-stateless)
#### 2022-02-18
(Pingfeng Luo) : The tensorboard log for training is available at <https://tensorboard.dev/experiment/k3QL6QMhRbCwCKYKM9po9w/>
And pretrained model is available at <https://huggingface.co/pfluo/icefall-aishell-transducer-stateless-char-2021-12-29>
||test|
|--|--|
|CER| 5.05% |
You can use the following commands to reproduce our results:
```bash
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
./transducer_stateless/train.py \
--bucketing-sampler True \
--world-size 8 \
--lang-dir data/lang_char \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp_rnnt_k2 \
--max-duration 80 \
--lr-factor 3
./transducer_stateless/decode.py \
--epoch 59 \
--avg 10 \
--lang-dir data/lang_char \
--exp-dir transducer_stateless/exp_rnnt_k2 \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
### Aishell training results (Conformer-MMI)
#### 2021-12-04
(Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/140>
The tensorboard log for training is available at <https://tensorboard.dev/experiment/PSRYVbptRGynqpPRSykp1g>
And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_mmi_model>
The best decoding results (CER) are listed below, we got this results by averaging models from epoch 61 to 85, and using `attention-decoder` decoder with num_paths equals to 100.
||test|
|--|--|
|CER| 4.94% |
||lm_scale|attention_scale|
|--|--|--|
|test|1.1|0.3|
You can use the following commands to reproduce our results:
```bash
git clone https://github.com/k2-fsa/icefall
cd icefall
cd egs/aishell/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
python conformer_mmi/train.py --bucketing-sampler True \
--max-duration 200 \
--start-epoch 0 \
--num-epochs 90 \
--world-size 8
python conformer_mmi/decode.py --nbest-scale 0.5 \
--epoch 85 \
--avg 25 \
--method attention-decoder \
--max-duration 20 \
--num-paths 100
```
### Aishell training results (Conformer-CTC)
#### 2021-11-16
(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/30
Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
The best decoding results (CER) are listed below, we got this results by averaging models from epoch 60 to 84, and using `attention-decoder` decoder with num_paths equals to 100.
||test|
|--|--|
|CER| 4.26% |
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 CER above are also listed below.
||lm_scale|attention_scale|
|--|--|--|
|test|0.3|0.9|
You can use the following commands to reproduce our results:
```bash
git clone https://github.com/k2-fsa/icefall
cd icefall
cd egs/aishell/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
--max-duration 200 \
--start-epoch 0 \
--num-epochs 90 \
--world-size 4
python conformer_ctc/decode.py --nbest-scale 0.5 \
--epoch 84 \
--avg 25 \
--method attention-decoder \
--max-duration 20 \
--num-paths 100
```
### Aishell training results (Tdnn-Lstm)
#### 2021-09-13
(Wei Kang): Result of phone based Tdnn-Lstm model, https://github.com/k2-fsa/icefall/pull/30
Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc_lstm_ctc
The best decoding results (CER) are listed below, we got this results by averaging models from epoch 19 to 8, and using `1best` decoding method.
||test|
|--|--|
|CER| 10.16% |
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