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README.md

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+# RAVE Models by Tangible Music Lab
+
+This is a collection of RAVE (Realtime Audio Variational autoEncoder) models trained by the Tangible Music Lab for audio generation and transformation. The aim of this repository is to provide musicians with pre-trained models for building embedded RAVE models on the Raspberry Pi platform or similar, for physical hardware and tangible interface development for sound and music experimentation. These models enable real-time audio manipulation and generation while being optimized for resource-constrained environments, making them ideal for interactive musical instruments and sound installations.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Tangible Music Lab
+- **Model type:** RAVE (Realtime Audio Variational autoEncoder) 
+- **License:** MIT
+
+### Model Sources
+
+- **Repository:** https://huggingface.co/TangibleMusicLab/rave-models
+- **Training Code:** https://github.com/victor-shepardson/RAVE
+
+## Uses
+
+### Direct Use
+
+These models are designed for real-time audio generation and transformation. They can be used with:
+- nn~
+- NN.ar
+- rave-supercollider
+
+### Models
+
+#### tam_freesoundloop10k_default_z16.ts
+- Dataset: Freesound Loop Dataset
+- Model: RAVE v3 with default configuration 
+- Latent dimensions: 16
+- Sample rate: 48kHz
+
+#### tam_freesoundloop10k_raspi_z16.ts
+- Dataset: Freesound Loop Dataset
+- Model: Modified RAVE v3, optimized for Raspberry Pi 5
+- Latent dimensions: 16
+- Sample rate: 44.1kHz
+- Special features: Scaled down for real-time performance on RPi 5
+
+## Features
+- All models are exported for streaming inference
+- Compatible with nn~, NN.ar, and rave-supercollider
+- Models focus on encoder-decoder architecture without prior networks
+- Training checkpoints provided for transfer learning 
+- For training, use the Intelligent Instruments Lab RAVE fork: https://github.com/victor-shepardson/RAVE
+
+## Training Details
+
+### Training Data
+
+The models were trained on the [Freesound Loop Dataset (FSL10K)](https://zenodo.org/records/3967852), a comprehensive collection of musical loops curated for machine learning applications. The dataset consists of 9,455 loops from Freesound.org.
+All sounds in the dataset are licensed under various Creative Commons licenses.
+
+### Training Procedure
+
+Training checkpoints are provided for both models to enable transfer learning on custom datasets.
+
+## Citation
+
+```bibtex
+@misc {tangible_music_lab_2025,
+   author       = { {Tangible Music Lab} },
+   title        = { RAVE Models },
+   year         = 2025,
+   url          = { https://huggingface.co/TangibleMusicLab/rave-models },
+   publisher    = { Hugging Face }
+}