AEROMamba: Efficient Audio Super-Resolution

AI-Generated README - Original: GitHub | Demo

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Model Overview

Architecture: Hybrid GAN + Mamba SSM
Task: 11.025 kHz → 44.1 kHz audio upsampling
Key Improvements:

• 14x faster inference vs AERO
• 5x less GPU memory usage
• 66.47 subjective score (vs AERO's 60.03)

Checkpoint: MUSDB18-HQ Model

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Quick Start

# Installation
pip install torch==1.12.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install causal-conv1d==1.1.2 mamba-ssm==1.1.3

# Inference
from src.models.aeromamba import AEROMamba
import torchaudio

model = AEROMamba.load_from_checkpoint("checkpoint.th")
lr_audio, sr = torchaudio.load("low_res.wav")  # 11kHz input
hr_audio = model(lr_audio)  # 44.1kHz output

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Performance (MUSDB18)

Metric	Low-Res	AERO	AEROMamba
ViSQOL ↑	1.82	2.90	2.93
LSD ↓	3.98	1.34	1.23
Subjective ↑	38.22	60.03	66.47

Hardware: 14x faster on RTX 3090 (0.087s vs 1.246s)

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Training Data

MUSDB18-HQ:

• 150 full-track music recordings
• 44.1 kHz originals → 11.025 kHz downsampled pairs
• 87.5/12.5 train-val split

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Citation

@inproceedings{Abreu2024lamir,
  author    = {Wallace Abreu and Luiz Wagner Pereira Biscainho},
  title     = {AEROMamba: Efficient Audio SR with GANs and SSMs},
  booktitle = {Proc. Latin American Music IR Workshop},
  year      = {2024}
}

This README was AI-generated based on original project materials. For training code and OLA inference scripts, visit the GitHub repo.