https://github.com/fgnt/ham_radio

https://github.com/fgnt/ham_radio

Repository

https://github.com/fgnt/ham_radio/blob/main/

# Neural network based SAD

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/fgnt/lazy_dataset/blob/master/LICENSE)

If you want to train a neural network for speech activity detection 
on the ham_radio database follow these steps:
1. Clone this repository and install it with pip. (We assume that Cython and numpy are already installed)
1. Download the database with 
```bash
    wget -qO- https://zenodo.org/record/5175960/files/ham_radio.tar.gz.parta{a,b,c} \
	| tar -C /PATH/TO/HAM_RADIO_DB/ -zx --checkpoint=10000 --checkpoint-action=echo="%u/5530000 %c"
``` 
where `/PATH/TO/HAM_RADIO_DB` has to be replaced with the chosen 
database directory
1. Set the variable ```HAM_RADIO_JSON``` to the file name the database json
should be written to
    ```export HAM_RADIO_JSON=/PATH/TO/JSON```
1. Create a database json with
```bash
python -m ham_radio.database.ham_radio.create_json \
    with database_path=/PATH/TO/HAM_RADIO_DB
```
1. Set a directory to which to write all models with
    ```export STORAGE_ROOT=/PATH/TO/MODEL_DIR```
1. Start a training with:
    ```bash
    python -m ham_radio.train with cnn
    ``` 
The trained model and the event files are written to 
```
  /PATH/TO/MODEL_DIR/ham_radio/SADModel_{number_of_train_runs}
```
 For more information about the training script and the event files visit 
 our [padertorch repository](https://github.com/fgnt/padertorch)
 
 If you want to reduce the required space for the gpu you can reduce 
 the batch size by adding ```provider_opts.batch_size=4``` or any other 
 value for the batch size.
   
 If you want to use a simple RNN structure instead of the  
 RNN you can replace ```cnn```  with ```rnn```
 Most paramters are adjustable in a similar fashion.
 
 # Citation
 ```
 @misc{heitkaemper2021database,
      title={A Database for Research on Detection and Enhancement of Speech Transmitted over HF links}, 
      author={Jens Heitkaemper and Joerg Schmalenstroeer and Joerg Ullmann and Valentin Ion and Reinhold Haeb-Umbach},
      year={2021},
      eprint={2106.02472},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}
```

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