Compare audio representation models using benchmark results
Evaluate and submit AI model results for Frugal AI Challenge
Evaluate open LLMs in the languages of LATAM and Spain.
Calculate memory usage for LLM models
Calculate survival probability based on passenger details
Convert and upload model files for Stable Diffusion
Leaderboard of information retrieval models in French
Evaluate code generation with diverse feedback types
Compare LLM performance across benchmarks
Browse and submit evaluations for CaselawQA benchmarks
Rank machines based on LLaMA 7B v2 benchmark results
Request model evaluation on COCO val 2017 dataset
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
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.