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Merge pull request #1677 from PaddlePaddle/Jackwaterveg-patch-1

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[Doc] update readem for aishell/asr0
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Hui Zhang 3 years ago committed by GitHub
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17
examples/aishell/asr0/README.md
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@ -151,21 +151,14 @@ avg.sh best exp/deepspeech2/checkpoints 1
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1
```
## Pretrained Model
You can get the pretrained transformer or conformer using the scripts below:
```bash
Deepspeech2 offline:
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz
Deepspeech2 online:
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz
You can get the pretrained models from [this](../../../docs/source/released_model.md).
```
using the `tar` scripts to unpack the model and then you can use the script to test the model.
For example:
```
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz
tar xzvf ds2.model.tar.gz
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz
tar xzvf asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz
source path.sh
# 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
@ -209,8 +202,8 @@ if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
```
you can train the model by yourself, or you can download the pretrained model by the script below:
```bash
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz
tar xzvf ds2.model.tar.gz
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz
tar xzvf asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz
```
You can download the audio demo:
```bash

19
examples/aishell/asr1/README.md
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@ -143,25 +143,14 @@ avg.sh best exp/conformer/checkpoints 20
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20
```
## Pretrained Model
You can get the pretrained transformer or conformer using the scripts below:
You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md)
```bash
# Conformer:
wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz
# Chunk Conformer:
wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz
# Transformer:
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
```
using the `tar` scripts to unpack the model and then you can use the script to test the model.
For example:
```
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
tar xzvf transformer.model.tar.gz
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz
tar xzvf asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz
source path.sh
# 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
@ -206,7 +195,7 @@ In some situations, you want to use the trained model to do the inference for th
```
you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below:
```bash
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz
tar xzvf transformer.model.tar.gz
```
You can download the audio demo:

36
examples/librispeech/asr1/README.md
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@ -151,44 +151,22 @@ avg.sh best exp/conformer/checkpoints 20
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20
```
## Pretrained Model
You can get the pretrained transformer or conformer using the scripts below:
```bash
# Conformer:
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz
# Transformer:
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/transformer.model.tar.gz
```
You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md).
using the `tar` scripts to unpack the model and then you can use the script to test the model.
For example:
```bash
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz
tar xzvf transformer.model.tar.gz
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz
tar xzvf asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz
source path.sh
# 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 2 --stop_stage 2
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20
```
The performance of the released models are shown below:
## Conformer
train: Epoch 70, 4 V100-32G, best avg: 20
| Model | Params | Config | Augmentation | Test set | Decode method | Loss | WER |
| --------- | ------- | ------------------- | ------------ | ---------- | ---------------------- | ----------------- | -------- |
| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | attention | 6.433612394332886 | 0.039771 |
| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | ctc_greedy_search | 6.433612394332886 | 0.040342 |
| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | ctc_prefix_beam_search | 6.433612394332886 | 0.040342 |
| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | attention_rescoring | 6.433612394332886 | 0.033761 |
## Transformer
train: Epoch 120, 4 V100-32G, 27 Day, best avg: 10
The performance of the released models are shown in [here](./RESULTS.md).
| Model | Params | Config | Augmentation | Test set | Decode method | Loss | WER |
| ----------- | ------- | --------------------- | ------------ | ---------- | ---------------------- | ----------------- | -------- |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention | 6.382194232940674 | 0.049661 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_greedy_search | 6.382194232940674 | 0.049566 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_prefix_beam_search | 6.382194232940674 | 0.049585 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention_rescoring | 6.382194232940674 | 0.038135 |
## Stage 4: CTC Alignment
If you want to get the alignment between the audio and the text, you can use the ctc alignment. The code of this stage is shown below:
```bash
@ -227,8 +205,8 @@ In some situations, you want to use the trained model to do the inference for th
```
you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below:
```bash
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz
tar xzvf conformer.model.tar.gz
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz
tar xzvf asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz
```
You can download the audio demo:
```bash

34
examples/librispeech/asr2/README.md
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@ -1,4 +1,4 @@
# Transformer/Conformer ASR with Librispeech Asr2
# Transformer/Conformer ASR with Librispeech ASR2
This example contains code used to train a Transformer or [Conformer](http://arxiv.org/abs/2008.03802) model with [Librispeech dataset](http://www.openslr.org/resources/12) and use some functions in kaldi.
@ -213,17 +213,14 @@ avg.sh latest exp/transformer/checkpoints 10
./local/recog.sh --ckpt_prefix exp/transformer/checkpoints/avg_10
```
## Pretrained Model
You can get the pretrained transformer using the scripts below:
```bash
# Transformer:
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz
```
You can get the pretrained models from [this](../../../docs/source/released_model.md).
using the `tar` scripts to unpack the model and then you can use the script to test the model.
For example:
```bash
wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz
tar xzvf 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 asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz
source path.sh
# 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
@ -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
```
The performance of the released models are shown below:
### 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 |
The performance of the released models are shown [here](./RESULTS.md).
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

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