title | emoji | colorFrom | colorTo | sdk | python_version | sdk_version | app_file | pinned |
---|---|---|---|---|---|---|---|---|
Dance Classifier |
💃 |
blue |
yellow |
gradio |
3.10.8 |
3.15.0 |
app.py |
false |
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.
- Download dependencies:
conda env create --file environment.yml
- Open environment:
conda activate dancer-net
- Start the demo application:
python app.py
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. You can customize and parameterize these training loops by directing the training script towards a custom yaml config file.
# 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.
The YAML configuration files for training are located in models/config
. They specify the training environment, data, architecture, and hyperparameters of the model.
See tests in the tests
folder. Use Pytest to run the tests.
pytest