Quickstart¶

Get started with the podcast benchmark framework in minutes.

Setup¶

To download data and set up your local virtual environment:

./setup.sh

This will: - Create a Python virtual environment - Install all required dependencies - Download the necessary podcast listening data

Training Your First Model¶

The framework comes with two pre-configured models you can train immediately.

1. Neural Convolutional Decoder¶

This recreates the decoder from Tang et al. 2022, which decodes word embeddings directly from neural data:

make neural-conv

2. Foundation Model Decoder¶

This trains a decoder from a foundation model's latent representations to word embeddings:

make foundation-model

Results¶

Training results will be saved to: - results/ - Performance metrics and CSV files - checkpoints/ - Saved model checkpoints - event_logs/ - TensorBoard logs

See Baseline Results for performance benchmarks across all tasks.

Configuration¶

To modify data, behavior, or hyperparameters:

Edit the relevant configuration file in configs/: - configs/neural_conv_decoder/ - Neural convolutional decoder settings - configs/example_foundation_model/ - Foundation model decoder settings

Model implementations can be found in the models/ directory.

See Onboarding a New Model for details on configuration options.