SleepLM: Natural-Language Intelligence for Human Sleep

SleepLM is, to our knowledge, the first sleep-language foundation model family that enables targeted natural language generation from multimodal polysomnography (PSG) while also learning a shared signal–text embedding space for retrieval and open vocabulary sleep understanding. It is trained on the largest paired sleep–text corpus to date, built from five NSRR cohorts totaling 100K+ hours of PSG from 10,000+ individuals.

SleepLM supports controllable, domain-specific generation (brain, cardiac, respiration, somatic) as well as holistic summaries, moving beyond fixed label spaces like sleep stages and events. The model combines contrastive alignment, captioning, and signal reconstruction to preserve physiological fidelity while learning strong cross-modal semantics. Across a broad benchmark, SleepLM enables sleep-text retrieval, zero-shot and few-shot generalization, and robust transfer to unseen concepts.

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📰 News

• [2026-02-23] Code released on GitHub!
• [2026-02-23] Project website is live!

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✨ What you can do with this repo

• Targeted caption generation for 30-second sleep epochs using modality tokens (brain / cardiac / respiration / somatic).
• Cross modal retrieval by encoding signals and text into a shared embedding space and computing cosine similarity.
• Run an interactive demo in demo.ipynb.

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🚀 Quickstart

1) Install

git clone https://github.com/yang-ai-lab/SleepLM
cd SleepLM
pip install -r requirements.txt

2) Download checkpoint

The model checkpoint is hosted on Hugging Face Hub:

from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(repo_id="yang-ai-lab/SleepLM-Base", filename="model_checkpoint.pt")

Or via the CLI:

huggingface-cli download yang-ai-lab/SleepLM-Base model_checkpoint.pt

The checkpoint will be cached locally by huggingface_hub and the returned path can be passed directly to load_checkpoint() in demo.ipynb.

3) Prepare your data

Preprocess your PSG recordings into a float32 PyTorch tensor of shape [N, 10, 1920] (N epochs × 10 channels × 1920 samples) following the channel order and signal requirements in Using your own signals below. Save it as a .pt file and update the path in demo.ipynb.

4) Run the demo

Open and run:

• demo.ipynb

The notebook includes:

• similarity calculation between signal and text embeddings
• targeted caption generation with per-modality conditioning

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📦 Repository contents

• demo.ipynb — interactive inference + visualization
• requirements.txt — dependencies

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🧾 Input format

SleepLM expects a 30-second epoch, sampled at 64 Hz → 1920 samples/channel, with 10 channels in the order below.

Channel order

Body position encoding (POS channel)

POSITION_ENCODING = {
    0: "Right",
    1: "Left",
    2: "Supine",
    3: "Prone",
    4: "Upright",
   -1: "Other/Unknown",  # Use for missing data
}

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🧪 Using your own signals

You can generate captions for your own sleep recordings by loading preprocessed epochs directly in demo.ipynb.

Signal requirements

• Resample to 64 Hz
• Normalize each channel (z-score)
• If a channel is missing, zero-pad it
• POS must follow the integer encoding above
• Each epoch must be exactly 30 seconds (1920 samples @ 64 Hz)
• Pack epochs into a float32 PyTorch tensor of shape [N, 10, 1920]

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🔁 Reproducibility notes

This repo is intentionally lightweight and focuses on inference. If you plan to:

• reproduce paper benchmarks,
• train on NSRR cohorts,
• or evaluate cross-cohort generalization,

We are planning to opensource our training pipeline upon the acceptance of the paper. Note that the training data will not be opensourced due credential issue. If you wish to use the same NSRR datasets, please apply here.

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📝 Citation

If you use SleepLM in your research, please cite the paper:

@article{xu2026sleeplm,
  title={SleepLM: Natural-Language Intelligence for Human Sleep},
  author={Xu, Zongzhe and Shuai, Zitao and Mozaffari, Eideen and Aysola, Ravi S and Kumar, Rajesh and Yang, Yuzhe},
  journal={arXiv preprint arXiv:2602.23605},
  year={2026}
}

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Data sources and cohort infrastructure: NSRR (if applicable to your paper/training pipeline)
• Model architecture inspiration: OpenCLIP (https://github.com/mlfoundations/open_clip)