Compare audio representation models using benchmark results
Push a ML model to Hugging Face Hub
Browse and submit LLM evaluations
Evaluate and submit AI model results for Frugal AI Challenge
Create demo spaces for models on Hugging Face
Download a TriplaneGaussian model checkpoint
Convert Hugging Face model repo to Safetensors
Measure over-refusal in LLMs using OR-Bench
Calculate GPU requirements for running LLMs
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Measure BERT model performance using WASM and WebGPU
Explain GPU usage for model training
ARCH is a tool designed for comparing audio representation models using benchmark results. It provides a comprehensive platform to evaluate and analyze different audio models against various benchmarks. ARCH is particularly useful for researchers and developers working in audio processing and machine learning fields.
• Support for multiple audio representation models: Including waveform, spectrogram, and other advanced models.
• Pre-defined benchmark datasets: Users can evaluate models on common audio tasks.
• Visualization tools: Generate plots and charts to compare model performance.
• Model zoo: Access pre-trained models for quick comparison.
• Customizable evaluation: Define specific metrics and benchmarks for tailored analysis.
pip install arch-benchmark
from arch import benchmark
results = benchmark.run(models, dataset='urbansound8k')
benchmark.visualize(results, save_path='results_plot.png')
What models are supported by ARCH?
ARCH supports a variety of pre-trained audio representation models, including popular ones like VGG Sound, PANNs, and OpenL3. Custom models can also be integrated for comparison.
Can I use my own dataset for benchmarking?
Yes, ARCH allows users to use custom datasets. Simply specify the dataset path and configuration when running the benchmark script.
How do I interpret the benchmark results?
Benchmark results are provided in a structured format, including metrics like accuracy, F1-score, and inference time. Use the visualization tools to generate plots that help compare model performance effectively.