Compare audio representation models using benchmark results
Retrain models for new data at edge devices
Display and submit LLM benchmarks
Evaluate LLM over-refusal rates with OR-Bench
Explain GPU usage for model training
Find and download models from Hugging Face
Merge machine learning models using a YAML configuration file
Optimize and train foundation models using IBM's FMS
Compare code model performance on benchmarks
Visualize model performance on function calling tasks
View and compare language model evaluations
Browse and submit model evaluations in LLM benchmarks
Display leaderboard of language model evaluations
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.