ARCH
Compare audio representation models using benchmark results
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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.