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Guanbo Wang 2022-05-14 20:27:32 -04:00
parent e8f9c39382
commit 3b5acd16e9
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6
README.md
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@ -210,9 +210,9 @@ We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless mod
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 10.59 | 10.87 |
| fast beam search | 10.56 | 10.80 |
| modified beam search | 10.52 | 10.62 |
| greedy search | 10.51 | 10.73 |
| fast beam search | 10.50 | 10.69 |
| modified beam search | 10.40 | 10.51 |
## Deployment with C++

2
egs/gigaspeech/ASR/README.md
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@ -16,6 +16,6 @@ ln -sfv /path/to/GigaSpeech download/GigaSpeech
| | Dev | Test |
|--------------------------------|-------|-------|
| `conformer_ctc` | 10.47 | 10.58 |
| `pruned_transducer_stateless2` | 10.52 | 10.62 |
| `pruned_transducer_stateless2` | 10.40 | 10.51 |
See [RESULTS](/egs/gigaspeech/ASR/RESULTS.md) for details.

29
egs/gigaspeech/ASR/RESULTS.md
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@ -11,13 +11,15 @@ decoder contains only an embedding layer, a Conv1d (with kernel
size 2) and a linear layer (to transform tensor dim). k2 pruned
RNN-T loss is used.
The best WER, as of 2022-05-12, for the gigaspeech is below
Results are:
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 10.59 | 10.87 |
| fast beam search | 10.56 | 10.80 |
| modified beam search | 10.52 | 10.62 |
| greedy search | 10.51 | 10.73 |
| fast beam search | 10.50 | 10.69 |
| modified beam search | 10.40 | 10.51 |
To reproduce the above result, use the following commands for training:
@ -39,33 +41,30 @@ and the following commands for decoding:
```bash
# greedy search
./pruned_transducer_stateless2/decode.py \
--epoch 29 \
--avg 11 \
--iter 3488000 \
--avg 20 \
--decoding-method greedy_search \
--exp-dir pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 20 \
--num-workers 1
--max-duration 600
# fast beam search
./pruned_transducer_stateless2/decode.py \
--epoch 29 \
--avg 9 \
--iter 3488000 \
--avg 20 \
--decoding-method fast_beam_search \
--exp-dir pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 20 \
--num-workers 1
--max-duration 600
# modified beam search
./pruned_transducer_stateless2/decode.py \
--epoch 29 \
--avg 8 \
--iter 3488000 \
--avg 15 \
--decoding-method modified_beam_search \
--exp-dir pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 20 \
--num-workers 1
--max-duration 600
```
Pretrained model is available at