delete the unsupport

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huangyuxin 3 years ago
parent 50cf88b7f1
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@ -1,6 +1,6 @@
# Conformer ASR with Aishell
# Transformer/Conformer ASR with Aishell
This example contains code used to train a [Conformer](http://arxiv.org/abs/2008.03802) model with [Aishell dataset](http://www.openslr.org/resources/33)
This example contains code used to train a Transformer or [Conformer](http://arxiv.org/abs/2008.03802) model with [Aishell dataset](http://www.openslr.org/resources/33)
## Overview
@ -14,16 +14,16 @@ All the scirpts you need are in the ```run.sh```. There are several stages in th
| 3 | Test the final model performance |
| 4 | Get ctc alignment of test data using the final model |
| 5 | Infer the single audio file |
| 51 | (Not supported at now) Transform the dynamic graph model to static graph model |
| 101 | (Need further installation) Train language model and Build TLG |
You can choose to run a range of stages by setting the ```stage``` and ```stop_stage ``` .
You can choose to run a range of stages by setting the ```stage``` and ```stop_stage ``` .
For example, if you want to execute the code in stage 2 and stage 3, you can run this script:
```bash
bash run.sh --stage 2 --stop_stage 3
```
Or you can set ```stage``` equal to ```stop-stage``` to only run one stage.
For example, if you only want to run ```stage 0```, you can use the script below:
@ -38,9 +38,11 @@ The document below will describe the scripts in the ```run.sh``` in detail.
## The environment variables
The path.sh contains the environment variable.
```bash
source path.sh
```
This script needs to be run firstly.
And another script is also needed:
@ -56,20 +58,20 @@ It will support the way of using```--varibale value``` in the shell scripts.
## The local variables
Some local variables are set in the ```run.sh```.
```gpus``` denotes the GPU number you want to use. If you set ```gpus=```, it means you only use CPU.
```gpus``` denotes the GPU number you want to use. If you set ```gpus=```, it means you only use CPU.
```stage``` denotes the number of stage you want to start from in the expriments.
```stage``` denotes the number of stage you want to start from in the expriments.
```stop stage```denotes the number of stage you want to end at in the expriments.
```conf_path``` denotes the config path of the model.
```avg_num``` denotes the number K of top-K models you want to average to get the final model.
```ckpt``` denotes the checkpoint prefix of the model, e.g. "conformer"
```audio file``` denotes the file path of the single file you want to infer in stage 6
You can set the local variables when you use the ```run.sh```
```ckpt``` denotes the checkpoint prefix of the model, e.g. "conformer"
You can set the local variables (except ```ckpt```) when you use the ```run.sh```
For example, you can set the ```gpus``` and ``avg_num`` when you use the command line.:
@ -81,7 +83,8 @@ bash run.sh --gpus 0,1 --avg_num 20
## Stage 0: Data processing
To use this example, you need to process data firstly and you can use stage 0 in the ```run.sh``` to do this. The code is shown below:
To use this example, you need to process data firstly and you can use stage 0 in the ```run.sh``` to do this. The code is shown below:
```bash
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
@ -101,7 +104,6 @@ You can also just run these scripts in your command line.
```bash
source path.sh
source ${MAIN_ROOT}/utils/parse_options.sh
bash ./local/data.sh
```
@ -128,47 +130,52 @@ data/
## Stage 1: Model training
If you want to train the model. you can use stage 1 in the ```run.sh```. The code is shown below.
```bash
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
fi
```
If you want to train the model, you can use the script below to execute stage 0 and stage 1:
```bash
bash run.sh --stage 0 --stop_stage 1
```
or you can run these scripts in the command line (only use CPU).
```bash
source path.sh
source ${MAIN_ROOT}/utils/parse_options.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
```
## Stage 2: Top-k models averaging
## Stage 2: Top-k Models Averaging
After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below:
After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below:
```bash
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# avg n best model
avg.sh best exp/${ckpt}/checkpoints ${avg_num}
fi
```
The ```avg.sh``` is in the ```../../../utils/``` which is define in the ```path.sh```.
If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:
If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:
```bash
bash run.sh --stage 0 --stop_stage 2
```
or you can run these scripts in the command line (only use CPU).
```bash
source path.sh
source ${MAIN_ROOT}/utils/parse_options.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20
@ -187,7 +194,7 @@ The test stage is to evaluate the model performance.. The code of test stage is
fi
```
If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3:
If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
```bash
bash run.sh --stage 0 --stop_stage 3
@ -197,7 +204,6 @@ or you can run these scripts in the command line (only use CPU).
```bash
source path.sh
source ${MAIN_ROOT}/utils/parse_options.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20
@ -206,7 +212,75 @@ CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoi
## Stage 4: CTC alignment
## Pretrained Model
You can get the pretrained transfomer or conformer using the scripts below:
```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
Transfomer:
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 modle.
For example:
```
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
tar xzvf transformer.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/transformer.yaml exp/transformer/checkpoints/avg_20
```
The performance of the released models are shown below:
### Conformer
| Model | Params | Config | Augmentation | Test set | Decode method | Loss | CER |
| --------- | ------ | ------------------- | ---------------- | -------- | ---------------------- | ---- | -------- |
| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test | attention | - | 0.059858 |
| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | - | 0.062311 |
| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | - | 0.062196 |
| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | - | 0.054694 |
### Chunk Conformer
Need set `decoding.decoding_chunk_size=16` when decoding.
| Model | Params | Config | Augmentation | Test set | Decode method | Chunk Size & Left Chunks | Loss | CER |
| --------- | ------ | ------------------------- | ---------------- | -------- | ---------------------- | ------------------------ | ---- | -------- |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16, -1 | - | 0.061939 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16, -1 | - | 0.070806 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16, -1 | - | 0.070739 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16, -1 | - | 0.059400 |
### Transformer
| Model | Params | Config | Augmentation | Test set | Decode method | Loss | CER |
| ----------- | ------ | --------------------- | ------------ | -------- | ---------------------- | ----------------- | -------- |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | attention | 3.858648955821991 | 0.057293 |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | ctc_greedy_search | 3.858648955821991 | 0.061837 |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | ctc_prefix_beam_search | 3.858648955821991 | 0.061685 |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | attention_rescoring | 3.858648955821991 | 0.053844 |
## 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:
@ -217,7 +291,7 @@ If you want to get the alignment between the audio and the text, you can use the
fi
```
If you want to train the model, test it and do the alignment, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
If you want to train the model, test it and do the alignment, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
```bash
bash run.sh --stage 0 --stop_stage 4
@ -234,7 +308,6 @@ or you can also use these scripts in the command line (only use CPU).
```bash
source path.sh
source ${MAIN_ROOT}/utils/parse_options.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20
@ -245,61 +318,29 @@ CUDA_VISIBLE_DEVICES= ./local/align.sh conf/conformer.yaml exp/conformer/checkpo
## Stage 5: Single audio file inference
## Stage 5: Single Audio File Inference
In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
```bash
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# test a single .wav file
CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
fi
```
you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model by the script below:
```
wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz
tar aishell.release.tar.gz
```
You need to prepare an audio file, please confirm the sample rate of the audio is 16K. Assume the path of the audio file is ```data/test_audio.wav```, you can get the result by running the script below.
```bash
CUDA_VISIBLE_DEVICES= ./local/test_hub.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 data/test_audio.wav
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
tar xzvf transformer.model.tar.gz
```
## Stage 51: Static model transformingnot supported at now
To transform the dynamic model to static modelstage 51 can be used. The code of this stage is shown below:
```bash
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi
```
It is not supported at now, so we set a large stage number for this stage.
## Stage: 101 Language model training and TLG building (Need further installation! )
You need to install the kaldi and srilm to use stage 101, it is used for training language model and building TLG. To do further installation, you need to do these:
You need to prepare an audio file, please confirm the sample rate of the audio is 16K. Assume the path of the audio file is ```data/test_audio.wav```, you can get the result by running the script below.
```bash
# go to the root of the repo
cd ../../../
# Do the further installation
pip install -e .
cd tools
bash extras/install_openblas.sh
bash extras/install_kaldi.sh
CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/transformer.yaml exp/transformer/checkpoints/avg_20 data/test_audio.wav
```
You need to be patient, since installing the kaldi takes some time.

@ -14,7 +14,7 @@ avg_ckpt=avg_${avg_num}
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
echo "checkpoint name ${ckpt}"
audio_file="data/tmp.wav"
audio_file="data/test_single_audio.wav"
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
@ -44,14 +44,16 @@ fi
# Optionally, you can add LM and test it with runtime.
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# test a single .wav file
CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
fi
# if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
# # export ckpt avg_n
# CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
# fi
# Not supported at now!!!
if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi
# Need further installation! Read the install.md to complete further installation
if [ ${stage} -le 101 ] && [ ${stop_stage} -ge 101 ]; then
echo "warning: deps on kaldi and srilm, please make sure installed."
# train lm and build TLG

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