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Cover image for AudioForge — Parameter-Efficient Audio Classification Built
AudioForge — Parameter-Efficient Audio Classification Built and evaluated an audio-classification pipeline on FSD50K, covering 36K+ training clips across 200 multi-label sound categories. I compared a CNN trained from scratch against a pretrained Audio Spectrogram Transformer adapted using LoRA. The LoRA setup trained only 0.52% of the AST’s 86.8M parameters while improving mean Average Precision from 0.302 to 0.557 — an 84% improvement. Beyond model training, I handled the GPU infrastructure end-to-end: VRAM sizing, AWS EC2 provisioning, quota management, spot-instance fallback, cost-controlled start/stop workflows, and debugging failed training runs with smoke tests before full experiments. What I worked on Audio Spectrogram Transformer fine-tuning LoRA / parameter-efficient training Multi-label audio classification PyTorch training and evaluation pipelines GPU memory and infrastructure optimization Experiment debugging and validation Stack: Python · PyTorch · Hugging Face · AWS EC2
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