Compare audio representation models using benchmark results
Explore and visualize diverse models
View and submit LLM benchmark evaluations
Evaluate RAG systems with visual analytics
Display leaderboard for earthquake intent classification models
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
Explain GPU usage for model training
Display model benchmark results
Request model evaluation on COCO val 2017 dataset
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Track, rank and evaluate open LLMs and chatbots
Display genomic embedding leaderboard
Convert PaddleOCR models to ONNX format
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.