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title: Dance Classifier | |
emoji: π | |
colorFrom: blue | |
colorTo: yellow | |
sdk: gradio | |
python_version: 3.10.8 | |
sdk_version: 3.15.0 | |
app_file: app.py | |
pinned: false | |
# Dance Classifier | |
Classifies the dance style that best accompanies a provided song. Users record or upload an audio clip and the model provides a list of matching dance styles. | |
## Getting Started | |
1. Download dependencies: `conda env create --file environment.yml` | |
2. Open environment: `conda activate dancer-net` | |
3. Start the demo application: `python app.py` | |
## Training | |
You can update and train models with the `train.py` script. The specific logic for training each model can be found in training functions located in the [models folder](./models/). You can customize and parameterize these training loops by directing the training script towards a custom [yaml config file](./models/config/). | |
```bash | |
# Train a model using a custom configuration | |
python train.py --config models/config/train_local.yaml | |
``` | |
The training loops output the weights into either the `models/weights` or `lightning_logs` directories depending on the training script. You can then reference these pretrained weights for inference. | |
### Model Configuration | |
The YAML configuration files for training are located in [`models/config`](./models/config/). They specify the training environment, data, architecture, and hyperparameters of the model. | |
## Testing | |
See tests in the `tests` folder. Use Pytest to run the tests. | |
```bash | |
pytest | |
``` | |