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Users record or upload an audio clip and the model provides a list of matching dance styles.\n\n## Getting Started\n\n1. Clone this repo: `git clone https://github.com/Waidhoferj/dance-classifier`\n2. Download git LSF files: `git lfs pull`\n3. Download dependencies: `conda env create --file environment.yml`\n4. Open environment: `conda activate dancer-classifier`\n5. Start the demo application: `python app.py`\n\n## Training\n\nYou 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/).\n\n```bash\n# Train a model using a custom configuration\npython train.py --config models/config/train_local.yaml\n```\n\nThe 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.\n\n### Model Configuration\n\nThe YAML configuration files for training are located in [`models/config`](./models/config/). They specify the training environment, data, architecture, and hyperparameters of the model.\n\n## Testing\n\nSee tests in the `tests` folder. Use Pytest to run the tests.\n\n```bash\npytest\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwaidhoferj%2Fdance-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwaidhoferj%2Fdance-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwaidhoferj%2Fdance-classifier/lists"}