or refer to `example/librispeech/local/run_train.sh`.
or refer to `example/librispeech/local/train.sh`.
### Data Augmentation Pipeline
@ -239,7 +239,7 @@ Be careful when utilizing the data augmentation technique, as improper augmentat
### Training for Mandarin Language
The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell/local```. As mentioned above, please execute ```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh``` and ```sh run_infer.sh``` to do data preparation, training, testing and inference correspondingly. We have also prepared a pre-trained model (downloaded by ./models/aishell/download_model.sh) for users to try with ```sh run_infer_golden.sh``` and ```sh run_test_golden.sh```. Notice that, different from English LM, the Mandarin LM is character-based and please run ```tools/tune.py``` to find an optimal setting.
The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell/local```. As mentioned above, please execute ```sh data.sh```, ```sh train.sh```, ```sh test.sh``` and ```sh infer.sh``` to do data preparation, training, testing and inference correspondingly. We have also prepared a pre-trained model (downloaded by ./models/aishell/download_model.sh) for users to try with ```sh infer_golden.sh``` and ```sh test_golden.sh```. Notice that, different from English LM, the Mandarin LM is character-based and please run ```tools/tune.py``` to find an optimal setting.
## Inference and Evaluation
@ -299,7 +299,7 @@ For more help on arguments:
```
python3 infer.py --help
```
or refer to `example/librispeech/local/run_infer.sh`.
or refer to `example/librispeech/local/infer.sh`.
### Evaluate a Model
@ -324,7 +324,7 @@ For more help on arguments:
```bash
python3 test.py --help
```
or refer to `example/librispeech/local/run_test.sh`.
or refer to `example/librispeech/local/test.sh`.
## Hyper-parameters Tuning
@ -364,7 +364,7 @@ After tuning, you can reset $\alpha$ and $\beta$ in the inference and evaluation
```bash
python3 tune.py --help
```
or refer to `example/librispeech/local/run_tune.sh`.
or refer to `example/librispeech/local/tune.sh`.
## Trying Live Demo with Your Own Voice
@ -403,7 +403,7 @@ Now, in the client console, press the `whitespace` key, hold, and start speaking
Notice that `deploy/demo_client.py` must be run on a machine with a microphone device, while `deploy/demo_server.py` could be run on one without any audio recording hardware, e.g. any remote server machine. Just be careful to set the `host_ip` and `host_port` argument with the actual accessible IP address and port, if the server and client are running with two separate machines. Nothing should be done if they are running on one single machine.
Please also refer to `examples/deploy_demo/run_english_demo_server.sh`, which will first download a pre-trained English model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/deploy_demo/run_demo_client.sh`, you can speak English to test it. If you would like to try some other models, just update `--model_path` argument in the script.
Please also refer to `examples/deploy_demo/english_demo_server.sh`, which will first download a pre-trained English model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/deploy_demo/demo_client.sh`, you can speak English to test it. If you would like to try some other models, just update `--model_path` argument in the script.
For more help on arguments:
@ -427,7 +427,7 @@ VoxForge European | 30.15 | 18.64
VoxForge Indian | 53.73 | 25.51
Baidu Internal Testset | 40.75 | 8.48
For reproducing benchmark results on VoxForge data, we provide a script to download data and generate VoxForge dialect manifest files. Please go to ```data/voxforge``` and execute ```sh run_data.sh``` to get VoxForge dialect manifest files. Notice that VoxForge data may keep updating and the generated manifest files may have difference from those we evaluated on.
For reproducing benchmark results on VoxForge data, we provide a script to download data and generate VoxForge dialect manifest files. Please go to ```data/voxforge``` and execute ```sh data.sh``` to get VoxForge dialect manifest files. Notice that VoxForge data may keep updating and the generated manifest files may have difference from those we evaluated on.
#### Benchmark Results for Mandarin Model (Character Error Rate)
`run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
`data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
- Train your own ASR model
```bash
sh local/run_train.sh
bash local/train.sh
```
`run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
`train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
- Case inference with an existing model
```bash
sh local/run_infer.sh
bash local/infer.sh
```
`run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
`infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
```bash
sh local/run_infer_golden.sh
bash local/infer_golden.sh
```
- Evaluate an existing model
```bash
sh local/run_test.sh
bash local/test.sh
```
`run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
`test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
Hui Zhang
5 years ago