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e6a6727012
* add egs/xbmu_amdo31 * fix xbmu_amdo31/ASR/pruned_transducer_stateless5/train.py * fix xbmu_amdo31/ASR/pruned_transducer_stateless5/asr_datamodule.py * fix xbmu_amdo31/ASR/prepare.sh * add RESULTS.md and README.md * dix pruned_transducer_stateless5 decode.py * add transducer stateless7 * fix transducer_stateless7 * fix RESULTS.md error * Add pruned_transducer_stateless7 validation set results
93 lines
2.7 KiB
Markdown
93 lines
2.7 KiB
Markdown
## Results
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### XBMU-AMDO31 BPE training result (Stateless Transducer)
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#### Pruned transducer stateless 5
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[./pruned_transducer_stateless5](./pruned_transducer_stateless5)
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It uses pruned RNN-T.
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A pre-trained model and decoding logs can be found at <https://huggingface.co/syzym/icefall-asr-xbmu-amdo31-pruned-transducer-stateless5-2022-11-29>
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You can use <https://github.com/k2-fsa/sherpa> to deploy it.
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Number of model parameters: 87801200, i.e., 87.8 M
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| | test | dev | comment |
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|------------------------|------|------|---------------------------------------|
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| greedy search | 11.06| 11.73| --epoch 28 --avg 23 --max-duration 600|
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| beam search | 10.64| 11.42| --epoch 28 --avg 23 --max-duration 600|
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| modified beam search | 10.57| 11.24| --epoch 28 --avg 23 --max-duration 600|
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Training command is:
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```bash
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cd egs/xbmu_amdo31/ASR
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0"
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./pruned_transducer_stateless5/train.py
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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 method in greedy_search beam_search modified_beam_search;
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do
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 23 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method $method
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done
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```
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### pruned_transducer_stateless7 (zipformer)
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See <https://github.com/k2-fsa/icefall/pull/672> for more details.
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[pruned_transducer_stateless7](./pruned_transducer_stateless7)
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You can find a pretrained model, training logs, decoding logs, and decoding
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results at:
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<https://huggingface.co/syzym/icefall-asr-xbmu-amdo31-pruned-transducer-stateless7-2022-12-02>
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You can use <https://github.com/k2-fsa/sherpa> to deploy it.
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Number of model parameters: 70369391, i.e., 70.37 M
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| | test | dev | comment |
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|----------------------|------|------|----------------------------------------|
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| greedy search | 10.06| 10.59| --epoch 23 --avg 11 --max-duration 600 |
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| beam search | 9.77 | 10.11| --epoch 23 --avg 11 --max-duration 600 |
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| modified beam search | 9.7 | 10.12| --epoch 23 --avg 11 --max-duration 600 |
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The training commands are:
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```bash
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export CUDA_VISIBLE_DEVICES="0"
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./pruned_transducer_stateless7/train.py
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```
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The decoding commands are:
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```bash
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for m in greedy_search beam_search modified_beam_search; do
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for epoch in 23; do
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for avg in 11; do
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./pruned_transducer_stateless7/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless7/exp \
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--max-duration 600 \
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--decoding-method $m
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done
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done
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done
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```
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