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* `pruned_transudcer_stateless7_streaming` for AISHELL-1 * Update train.py * Update train2.py * Update decode.py * Update RESULTS.md
679 lines
20 KiB
Markdown
679 lines
20 KiB
Markdown
## Results
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### Aishell training result(Stateless Transducer)
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#### Pruned transducer stateless 7 streaming
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[./pruned_transducer_stateless7_streaming](./pruned_transducer_stateless7_streaming)
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It's Streaming version of Zipformer1 with Pruned RNNT loss.
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| | test | dev | comment |
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|------------------------|------|------|---------------------------------------|
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| greedy search | 6.95 | 6.29 | --epoch 44 --avg 15 --max-duration 600 |
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| modified beam search | 6.51 | 5.90 | --epoch 44 --avg 15 --max-duration 600 |
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| fast beam search | 6.73 | 6.09 | --epoch 44 --avg 15 --max-duration 600 |
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Training command is:
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```bash
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1"
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./pruned_transducer_stateless7_streaming/train.py \
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--world-size 2 \
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--num-epochs 50 \
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--use-fp16 1 \
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--context-size 1 \
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--max-duration 800 \
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--exp-dir ./pruned_transducer_stateless7_streaming/exp \
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--enable-musan 0 \
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--spec-aug-time-warp-factor 20
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```
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**Caution**: It uses `--context-size=1`.
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The decoding command is:
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```bash
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for m in greedy_search modified_beam_search fast_beam_search ; do
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./pruned_transducer_stateless7_streaming/decode.py \
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--epoch 44 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless7_streaming/exp \
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--lang-dir data/lang_char \
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--context-size 1 \
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--decoding-method $m
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done
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```
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Pretrained models, training logs, decoding logs, tensorboard and decoding results
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are available at
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<https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-streaming-2023-10-16/>
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#### Pruned transducer stateless 7
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[./pruned_transducer_stateless7](./pruned_transducer_stateless7)
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It's Zipformer with Pruned RNNT loss.
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| | test | dev | comment |
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|------------------------|------|------|---------------------------------------|
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| greedy search | 5.02 | 4.61 | --epoch 42 --avg 6 --max-duration 600 |
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| modified beam search | 4.81 | 4.4 | --epoch 42 --avg 6 --max-duration 600 |
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| fast beam search | 4.91 | 4.52 | --epoch 42 --avg 6 --max-duration 600 |
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Training command is:
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```bash
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1"
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./pruned_transducer_stateless7/train.py \
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--world-size 2 \
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--num-epochs 50 \
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--start-epoch 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless7/exp \
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--context-size 1 \
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--max-duration 300
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```
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**Caution**: It uses `--context-size=1`.
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/MHYo3ApfQxaCdYLr38cQOQ>
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The decoding command is:
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```bash
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for m in greedy_search modified_beam_search fast_beam_search ; do
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./pruned_transducer_stateless7/decode.py \
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--epoch 42 \
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--avg 6 \
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--exp-dir ./pruned_transducer_stateless7/exp \
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--lang-dir data/lang_char \
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--max-duration 300 \
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--context-size 1 \
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--decoding-method $m
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done
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```
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Pretrained models, training logs, decoding logs, and decoding results
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are available at
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<https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21>
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#### Pruned transducer stateless 7 (zipformer)
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See <https://github.com/k2-fsa/icefall/pull/986>
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[./pruned_transducer_stateless7_bbpe](./pruned_transducer_stateless7_bbpe)
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**Note**: The modeling units are byte level BPEs
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The best results I have gotten are:
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Vocab size | Greedy search(dev & test) | Modified beam search(dev & test) | Fast beam search (dev & test) | Fast beam search LG (dev & test) | comments
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-- | -- | -- | -- | -- | --
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500 | 4.31 & 4.59 | 4.25 & 4.54 | 4.27 & 4.55 | 4.07 & 4.38 | --epoch 48 --avg 29
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The training command:
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```
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export CUDA_VISIBLE_DEVICES="4,5,6,7"
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./pruned_transducer_stateless7_bbpe/train.py \
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--world-size 4 \
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--num-epochs 50 \
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--start-epoch 1 \
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--use-fp16 1 \
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--max-duration 800 \
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--bpe-model data/lang_bbpe_500/bbpe.model \
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--exp-dir pruned_transducer_stateless7_bbpe/exp \
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--lr-epochs 6 \
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--master-port 12535
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```
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The decoding command:
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```
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for m in greedy_search modified_beam_search fast_beam_search fast_beam_search_LG; do
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./pruned_transducer_stateless7_bbpe/decode.py \
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--epoch 48 \
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--avg 29 \
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--exp-dir ./pruned_transducer_stateless7_bbpe/exp \
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--max-sym-per-frame 1 \
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--ngram-lm-scale 0.25 \
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--ilme-scale 0.2 \
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--bpe-model data/lang_bbpe_500/bbpe.model \
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--max-duration 2000 \
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--decoding-method $m
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done
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```
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The pretrained model is available at: https://huggingface.co/pkufool/icefall_asr_aishell_pruned_transducer_stateless7_bbpe
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#### Pruned transducer stateless 3
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See <https://github.com/k2-fsa/icefall/pull/436>
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[./pruned_transducer_stateless3](./pruned_transducer_stateless3)
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It uses pruned RNN-T.
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| | test | dev | comment |
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|------------------------|------|------|---------------------------------------|
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| greedy search | 5.39 | 5.09 | --epoch 29 --avg 5 --max-duration 600 |
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| modified beam search | 5.05 | 4.79 | --epoch 29 --avg 5 --max-duration 600 |
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| modified beam search + RNNLM shallow fusion | 4.73 | 4.53 | --epoch 29 --avg 5 --max-duration 600 |
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| modified beam search + LODR | 4.57 | 4.37 | --epoch 29 --avg 5 --max-duration 600 |
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| fast beam search | 5.13 | 4.91 | --epoch 29 --avg 5 --max-duration 600 |
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Training command is:
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```bash
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./prepare.sh
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./prepare_aidatatang_200zh.sh
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export CUDA_VISIBLE_DEVICES="4,5,6,7"
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./pruned_transducer_stateless3/train.py \
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--exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \
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--world-size 4 \
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--max-duration 200 \
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--datatang-prob 0.5 \
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--start-epoch 1 \
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--num-epochs 30 \
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--use-fp16 1 \
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--num-encoder-layers 12 \
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--dim-feedforward 2048 \
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--nhead 8 \
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--encoder-dim 512 \
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--context-size 1 \
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--decoder-dim 512 \
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--joiner-dim 512 \
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--master-port 12356
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```
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**Caution**: It uses `--context-size=1`.
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/OKKacljwR6ik7rbDr5gMqQ>
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The decoding command is:
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```bash
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for epoch in 29; do
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for avg in 5; do
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for m in greedy_search modified_beam_search fast_beam_search; do
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./pruned_transducer_stateless3/decode.py \
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--exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \
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--epoch $epoch \
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--avg $avg \
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--use-averaged-model 1 \
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--max-duration 600 \
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--decoding-method $m \
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--num-encoder-layers 12 \
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--dim-feedforward 2048 \
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--nhead 8 \
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--context-size 1 \
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--encoder-dim 512 \
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--decoder-dim 512 \
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--joiner-dim 512
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done
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done
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done
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```
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We provide the option of shallow fusion with a RNN language model. The pre-trained language model is
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available at <https://huggingface.co/marcoyang/icefall-aishell-rnn-lm>. To decode with the language model,
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please use the following command:
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```bash
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# download pre-trained model
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git lfs install
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git clone https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20
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aishell_exp=icefall-aishell-pruned-transducer-stateless3-2022-06-20/
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pushd ${aishell_exp}/exp
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ln -s pretrained-epoch-29-avg-5-torch-1.10.0.pt epoch-99.pt
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popd
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# download RNN LM
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git lfs install
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git clone https://huggingface.co/marcoyang/icefall-aishell-rnn-lm
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rnnlm_dir=icefall-aishell-rnn-lm
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# RNNLM shallow fusion
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for lm_scale in $(seq 0.26 0.02 0.34); do
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python ./pruned_transducer_stateless3/decode.py \
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--epoch 99 \
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--avg 1 \
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--lang-dir ${aishell_exp}/data/lang_char \
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--exp-dir ${aishell_exp}/exp \
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--use-averaged-model False \
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--decoding-method modified_beam_search_lm_shallow_fusion \
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--use-shallow-fusion 1 \
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--lm-type rnn \
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--lm-exp-dir ${rnnlm_dir}/exp \
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--lm-epoch 99 \
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--lm-scale $lm_scale \
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--lm-avg 1 \
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--rnn-lm-embedding-dim 2048 \
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--rnn-lm-hidden-dim 2048 \
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--rnn-lm-num-layers 2 \
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--lm-vocab-size 4336
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done
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# RNNLM Low-order density ratio (LODR) with a 2-gram
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cp ${rnnlm_dir}/2gram.fst.txt ${aishell_exp}/data/lang_char/2gram.fst.txt
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for lm_scale in 0.48; do
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for LODR_scale in -0.28; do
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python ./pruned_transducer_stateless3/decode.py \
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--epoch 99 \
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--avg 1 \
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--lang-dir ${aishell_exp}/data/lang_char \
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--exp-dir ${aishell_exp}/exp \
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--use-averaged-model False \
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--decoding-method modified_beam_search_LODR \
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--use-shallow-fusion 1 \
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--lm-type rnn \
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--lm-exp-dir ${rnnlm_dir}/exp \
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--lm-epoch 99 \
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--lm-scale $lm_scale \
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--lm-avg 1 \
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--rnn-lm-embedding-dim 2048 \
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--rnn-lm-hidden-dim 2048 \
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--rnn-lm-num-layers 2 \
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--lm-vocab-size 4336 \
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--tokens-ngram 2 \
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--backoff-id 4336 \
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--ngram-lm-scale $LODR_scale
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done
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done
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```
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Pretrained models, training logs, decoding logs, and decoding results
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are available at
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<https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20>
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We have a tutorial in [sherpa](https://github.com/k2-fsa/sherpa) about how
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to use the pre-trained model for non-streaming ASR. See
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<https://k2-fsa.github.io/sherpa/offline_asr/conformer/aishell.html>
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#### Pruned transducer stateless 2
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See https://github.com/k2-fsa/icefall/pull/536
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[./pruned_transducer_stateless2](./pruned_transducer_stateless2)
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It uses pruned RNN-T.
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| | test | dev | comment |
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| -------------------- | ---- | ---- | -------------------------------------- |
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| greedy search | 5.20 | 4.78 | --epoch 72 --avg 14 --max-duration 200 |
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| modified beam search | 5.07 | 4.63 | --epoch 72 --avg 14 --max-duration 200 |
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| fast beam search | 5.13 | 4.70 | --epoch 72 --avg 14 --max-duration 200 |
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Training command is:
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```bash
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1"
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./pruned_transducer_stateless2/train.py \
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--world-size 2 \
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--num-epochs 90 \
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--start-epoch 0 \
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--exp-dir pruned_transducer_stateless2/exp \
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--max-duration 200 \
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```
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The tensorboard log is available at
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https://tensorboard.dev/experiment/QI3PVzrGRrebxpbWUPwmkA/
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The decoding command is:
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```bash
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for m in greedy_search modified_beam_search fast_beam_search ; do
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./pruned_transducer_stateless2/decode.py \
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--epoch 72 \
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--avg 14 \
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--exp-dir ./pruned_transducer_stateless2/exp \
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--lang-dir data/lang_char \
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--max-duration 200 \
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--decoding-method $m
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done
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```
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Pretrained models, training logs, decoding logs, and decoding results
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are available at
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<https://huggingface.co/teapoly/icefall-aishell-pruned-transducer-stateless2-2022-08-18>
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#### 2022-03-01
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[./transducer_stateless_modified-2](./transducer_stateless_modified-2)
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It uses [optimized_transducer](https://github.com/csukuangfj/optimized_transducer)
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for computing RNN-T loss.
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Stateless transducer + modified transducer + using [aidatatang_200zh](http://www.openslr.org/62/) as extra training data.
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|------------------------|------|----------------------------------------------------------------|
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| greedy search | 4.94 |--epoch 89, --avg 38, --max-duration 100, --max-sym-per-frame 1 |
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| modified beam search | 4.68 |--epoch 89, --avg 38, --max-duration 100 --beam-size 4 |
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The training commands are:
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```bash
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cd egs/aishell/ASR
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./prepare.sh --stop-stage 6
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./prepare_aidatatang_200zh.sh
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export CUDA_VISIBLE_DEVICES="0,1,2"
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./transducer_stateless_modified-2/train.py \
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--world-size 3 \
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--num-epochs 90 \
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--start-epoch 0 \
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--exp-dir transducer_stateless_modified-2/exp-2 \
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--max-duration 250 \
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--lr-factor 2.0 \
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--context-size 2 \
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--modified-transducer-prob 0.25 \
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--datatang-prob 0.2
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```
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/oG72ZlWaSGua6fXkcGRRjA/>
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The commands for decoding are
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```bash
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# greedy search
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for epoch in 89; do
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for avg in 38; do
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./transducer_stateless_modified-2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified-2/exp-2 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method greedy_search \
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--max-sym-per-frame 1
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done
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done
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# modified beam search
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for epoch in 89; do
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for avg in 38; do
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./transducer_stateless_modified-2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified-2/exp-2 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method modified_beam_search \
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--beam-size 4
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done
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done
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```
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You can find a pre-trained model, decoding logs, and decoding results at
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<https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01>
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#### 2022-03-01
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[./transducer_stateless_modified](./transducer_stateless_modified)
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Stateless transducer + modified transducer.
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| | test |comment |
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|------------------------|------|----------------------------------------------------------------|
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| greedy search | 5.22 |--epoch 64, --avg 33, --max-duration 100, --max-sym-per-frame 1 |
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| modified beam search | 5.02 |--epoch 64, --avg 33, --max-duration 100 --beam-size 4 |
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The training commands are:
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```bash
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cd egs/aishell/ASR
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./prepare.sh --stop-stage 6
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export CUDA_VISIBLE_DEVICES="0,1,2"
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./transducer_stateless_modified/train.py \
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--world-size 3 \
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--num-epochs 90 \
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--start-epoch 0 \
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--exp-dir transducer_stateless_modified/exp-4 \
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--max-duration 250 \
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--lr-factor 2.0 \
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--context-size 2 \
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--modified-transducer-prob 0.25
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```
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/C27M8YxRQCa1t2XglTqlWg/>
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The commands for decoding are
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```bash
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# greedy search
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for epoch in 64; do
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for avg in 33; do
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./transducer_stateless_modified/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified/exp-4 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method greedy_search \
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--max-sym-per-frame 1
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done
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done
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# modified beam search
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for epoch in 64; do
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for avg in 33; do
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./transducer_stateless_modified/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified/exp-4 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method modified_beam_search \
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--beam-size 4
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done
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done
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```
|
|
|
|
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% |
|