ARCH
Compare audio representation models using benchmark results
You May Also Like
View AllPush Model From Web
Upload a machine learning model to Hugging Face Hub
Russian LLM Leaderboard
View and submit LLM benchmark evaluations
CaselawQA leaderboard (WIP)
Browse and submit evaluations for CaselawQA benchmarks
OpenVINO Benchmark
Benchmark models using PyTorch and OpenVINO
SolidityBench Leaderboard
SolidityBench Leaderboard
NNCF quantization
Quantize a model for faster inference
Open Persian LLM Leaderboard
Open Persian LLM Leaderboard
InspectorRAGet
Evaluate RAG systems with visual analytics
OR-Bench Leaderboard
Evaluate LLM over-refusal rates with OR-Bench
Project RewardMATH
Evaluate reward models for math reasoning
PTEB Leaderboard
Persian Text Embedding Benchmark
Cetvel
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
What is ARCH ?
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.
Features
ā¢ Support for multiple audio representation models: Including waveform, spectrogram, and other advanced models.
ā¢ Pre-defined benchmark datasets: Users can evaluate models on common audio tasks.
ā¢ Visualization tools: Generate plots and charts to compare model performance.
ā¢ Model zoo: Access pre-trained models for quick comparison.
ā¢ Customizable evaluation: Define specific metrics and benchmarks for tailored analysis.
How to use ARCH ?
- Install the package: Use pip to install the latest version of ARCH.
pip install arch-benchmark - Select models: Choose from the pre-supported models or import custom models.
- Run benchmarks: Execute the benchmarking script on your dataset.
from arch import benchmark results = benchmark.run(models, dataset='urbansound8k') - Analyze results: Use the visualization tools to generate comparison plots.
benchmark.visualize(results, save_path='results_plot.png') - Export results: Save the benchmark results for further analysis or reporting.
Frequently Asked Questions
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.