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examples/librispeech/asr2/README.md
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@ -213,17 +213,14 @@ avg.sh latest exp/transformer/checkpoints 10
./local/recog.sh --ckpt_prefix exp/transformer/checkpoints/avg_10 ./local/recog.sh --ckpt_prefix exp/transformer/checkpoints/avg_10
``` ```
## Pretrained Model ## Pretrained Model
You can get the pretrained transformer using the scripts below: You can get the pretrained models from [this](../../../docs/source/released_model.md).
```bash
# Transformer:
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz
```
using the `tar` scripts to unpack the model and then you can use the script to test the model. using the `tar` scripts to unpack the model and then you can use the script to test the model.
For example: For example:
```bash ```bash
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz
tar xzvf transformer.model.tar.gz tar xzvf asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz
source path.sh source path.sh
# If you have process the data and get the manifest file， you can skip the following 2 steps # If you have process the data and get the manifest file， you can skip the following 2 steps
bash local/data.sh --stage -1 --stop_stage -1 bash local/data.sh --stage -1 --stop_stage -1
@ -231,26 +228,7 @@ bash local/data.sh --stage 2 --stop_stage 2
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/transformer.yaml exp/ctc/checkpoints/avg_10 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/transformer.yaml exp/ctc/checkpoints/avg_10
``` ```
The performance of the released models are shown below: The performance of the released models are shown [here](./RESULTS.md).
### Transformer
| Model | Params | GPUS | Averaged Model | Config | Augmentation | Loss |
| :---------: | :----: | :--------------------: | :--------------: | :-------------------: | :----------: | :-------------: |
| transformer | 32.52M | 8 Tesla V100-SXM2-32GB | 10-best val_loss | conf/transformer.yaml | spec_aug | 6.3197922706604 |
#### Attention Rescore
| Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err |
| ---------- | --------------------- | ---- | ----- | ---- | ---- | ---- | ---- | ---- | ----- |
| test-clean | attention | 2620 | 52576 | 96.4 | 2.5 | 1.1 | 0.4 | 4.0 | 34.7 |
| test-clean | ctc_greedy_search | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 48.0 |
| test-clean | ctc_prefix_beamsearch | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 47.6 |
| test-clean | attention_rescore | 2620 | 52576 | 96.8 | 2.9 | 0.3 | 0.4 | 3.7 | 38.0 |
#### JoinCTC
| Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err |
| ---------- | ----------------- | ---- | ----- | ---- | ---- | ---- | ---- | ---- | ----- |
| test-clean | join_ctc_only_att | 2620 | 52576 | 96.1 | 2.5 | 1.4 | 0.4 | 4.4 | 34.7 |
| test-clean | join_ctc_w/o_lm | 2620 | 52576 | 97.2 | 2.6 | 0.3 | 0.4 | 3.2 | 34.9 |
| test-clean | join_ctc_w_lm | 2620 | 52576 | 97.9 | 1.8 | 0.2 | 0.3 | 2.4 | 27.8 |
Compare with [ESPNET](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-transformer-with-specaug-4-gpus--transformer-lm-4-gpus) we using 8gpu, but the model size (aheads4-adim256) small than it. Compare with [ESPNET](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-transformer-with-specaug-4-gpus--transformer-lm-4-gpus) we using 8gpu, but the model size (aheads4-adim256) small than it.
## Stage 5: CTC Alignment ## Stage 5: CTC Alignment

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