README.md

---
license: mit
task_categories:
- audio-to-audio
language:
- en
datasets:
- Blinorot/lensless_mic_librispeech
- Blinorot/lensless_mic_random
- Blinorot/lensless_mic_songdescriber
---

# Model Card for LenslessMic Reconstruction Algorithms

## Models Summary
Reconstruction algoritms from the
["LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging"](https://arxiv.org/abs/2509.16418) paper.

To download the models and work with them, use our [official repository](https://github.com/Blinorot/LenslessMic).

## Models Details
The models are saved in the following format:

```
.
└── checkpoint_tag
    ├── checkpoint_name.pth # PyTorch checkpoint with model state dict under 'state_dict' key.
    └── config.yaml # Hydra config used to train the model
```

Checkpoint tag is represented in the following format:

```
{latent_size}_{training_dataset}_{loss_functions_used}_{reconstruction_algorithm}
```

1. The `latent_size` is either 16x16 or 32x32, depends on the neural audio codec used in the dataset.
2. The training dataset is either `random` or `librispeech`. For `librispeech`, a groupped version can be used, tagged as
    `group_n_m_r_c` (see [LenslessMic Version of Librispeech](https://huggingface.co/datasets/Blinorot/lensless_mic_librispeech)
    (with 288x288 after group if the sensor image size is not the default 256x256). The version of the model, which is
    fine-tuned using `train-other`, is tagged as `librispeech_other` and `_ft` at the end.
3. The `loss_function` is usually MSE, SSIM, and Raw SSIM, as in the paper. We also provide checkpoints with only MSE,
   MSE and SSIM, and all three with L1 waveform or Mel Losses.
4. The reconstruction algorithm: `PSF_Unet4M_U5_Unet4M` is the Learned and R-Learned methods from the paper.
   `Unet8M` is the `NoPSF` method.

## Citation

If you use these models, please cite it as follows:

```bibtex
@article{grinberg2025lenslessmic,
  title = {LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging},
  author = {Grinberg, Petr and Bezzam, Eric and Prandoni, Paolo and Vetterli, Martin},
  journal = {arXiv preprint arXiv:2509.16418},
  year = {2025},
}
```