README.md

metadata

title: Dance Classifier
emoji: 💃
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. 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.

Model Configuration

The YAML configuration files for training are located in 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.

pytest