Instructions to use Rumiii/RS1_BeatTheta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rumiii/RS1_BeatTheta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Rumiii/RS1_BeatTheta", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Rumiii/RS1_BeatTheta", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
RS1_BeatTheta
A compact diffusion model that generates ambient sound textures for theta-range binaural-beat sessions.
RS1_BeatTheta creates the soft background layer (pads, drones, ocean, rain) and can lay an exact binaural beat on top, so a calm, headphone-based listening session takes a few lines of Python.
| Model | Class-conditional diffusion, about 5.2M parameters |
| Output | 8 kHz audio: mono texture, or stereo with a binaural beat |
| Textures | warm_pad 路 deep_drone 路 ocean_swell 路 soft_rain 路 crystal_pad |
| Training data | 100% synthetic, generated by code |
| License | MIT |
Quick start
pip install transformers torch scipy soundfile
import soundfile as sf
from transformers import AutoModel
model = AutoModel.from_pretrained("Rumiii/RS1_BeatTheta", trust_remote_code=True) # add .to("cuda") for GPU
audio = model.generate("ocean_swell", seconds=20, beat_hz=6.0) # stereo, 6 Hz theta beat
sf.write("session.wav", audio, model.sample_rate)
Leave out beat_hz for the texture alone. trust_remote_code=True is needed because the model code ships in this repo.
Training data
Every training clip is synthesized from code: five texture families, 600 clips each, 3,000 clips of about 2 seconds (roughly 1.7 hours of audio). Each clip is rendered with randomized pitch, detuning, slow modulation, noise colour and filtering, so no two are alike. No recordings or third-party data were used.
Training
Class-conditional diffusion with classifier-free guidance, trained from scratch for 12,000 steps (batch size 24, about 96 passes over the data) with AdamW, EMA weights and mixed precision. About 36 minutes on a single NVIDIA T4.
Intended use and limitations
- Intended for relaxation and focus audio, and as a lightweight base for experiments with synthetic-data audio models.
- Output is 8 kHz; longer audio is built by crossfading short generated clips. Textures only, no music or speech.
- The binaural tones are exact signal processing, not learned. Responses to binaural beats vary between people and the research evidence is mixed. This is a research release (v1), not a medical device or treatment.
Safety
Use stereo headphones at a comfortable volume. Do not listen while driving or operating machinery. Ask a doctor first if you have a seizure disorder or heart condition, or are pregnant.
Hemi-Sync is a registered trademark of The Monroe Institute; this project is independent and not affiliated with it.
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