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license: cc0-1.0 |
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# AEROMamba: Efficient Audio Super-Resolution |
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*AI-Generated README - Original: [GitHub](https://github.com/aeromamba-super-resolution/aeromamba) | [Demo](https://aeromamba-super-resolution.github.io/)* |
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## Model Overview |
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**Architecture**: Hybrid GAN + Mamba SSM |
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**Task**: 11.025 kHz β 44.1 kHz audio upsampling |
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**Key Improvements**: |
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- 14x faster inference vs AERO |
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- 5x less GPU memory usage |
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- 66.47 subjective score (vs AERO's 60.03) |
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**Checkpoint**: [MUSDB18-HQ Model](https://huggingface.co/KingNish/AEROMamba/blob/main/checkpoint.th) |
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## Quick Start |
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```python |
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# Installation |
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pip install torch==1.12.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113 |
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pip install causal-conv1d==1.1.2 mamba-ssm==1.1.3 |
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# Inference |
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from src.models.aeromamba import AEROMamba |
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import torchaudio |
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model = AEROMamba.load_from_checkpoint("checkpoint.th") |
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lr_audio, sr = torchaudio.load("low_res.wav") # 11kHz input |
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hr_audio = model(lr_audio) # 44.1kHz output |
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``` |
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## Performance (MUSDB18) |
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| Metric | Low-Res | AERO | AEROMamba | |
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|-----------------|---------|-------|-----------| |
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| ViSQOL β | 1.82 | 2.90 | **2.93** | |
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| LSD β | 3.98 | 1.34 | **1.23** | |
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| Subjective β | 38.22 | 60.03 | **66.47** | |
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**Hardware**: 14x faster on RTX 3090 (0.087s vs 1.246s) |
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## Training Data |
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**MUSDB18-HQ**: |
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- 150 full-track music recordings |
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- 44.1 kHz originals β 11.025 kHz downsampled pairs |
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- 87.5/12.5 train-val split |
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## Citation |
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```bibtex |
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@inproceedings{Abreu2024lamir, |
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author = {Wallace Abreu and Luiz Wagner Pereira Biscainho}, |
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title = {AEROMamba: Efficient Audio SR with GANs and SSMs}, |
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booktitle = {Proc. Latin American Music IR Workshop}, |
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year = {2024} |
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} |
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``` |
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*This README was AI-generated based on original project materials. For training code and OLA inference scripts, visit the [GitHub repo](https://github.com/aeromamba-super-resolution/aeromamba).* |