| ---
|
| license: cc-by-4.0
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| tags:
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| - audio-visual-learning
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| - acoustic
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| - audio
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| - computer-vision
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| - pytorch
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| library_name: pytorch
|
| ---
|
|
|
| # FLAC
|
|
|
| Official model weights for **FLAC**, from the paper "Few-shot Acoustic Synthesis with Multimodal Flow Matching" by Amandine Brunetto (CVPR 2026).
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|
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| FLAC is a model for generating room impulse responses in novel scenes in a one-shot setting, relying only on a single RIR example and a depth image.
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|
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| This repository contains the pretrained weights. To run FLAC, please use the official [codebase](https://github.com/AmandineBtto/FLAC).
|
|
|
| ---
|
|
|
| # Available checkpoints
|
|
|
| | file | description |
|
| |-----|-------------|
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| | FLAC.ckpt | main trained model on the AR dataset |
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| | FLAC_EMA.ckpt | EMA weights for finetuning FLAC on the HAA dataset |
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| | FLAC_HAA.ckpt | fine-tuned FLAC model on the HAA dataset |
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| | VAE.safetensors | VAE model for training FLAC |
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| | VAE.ckpt | VAE model for VAE evaluation |
|
|
|
| ---
|
|
|
| # Download
|
|
|
| Weights can be downloaded with:
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|
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| ```bash
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| huggingface-cli download AmandineBtto/FLAC --local-dir weights/FLAC
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| ```
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|
|
