Compare audio representation models using benchmark results
Explore and submit models using the LLM Leaderboard
Evaluate code generation with diverse feedback types
Explore and benchmark visual document retrieval models
Load AI models and prepare your space
Evaluate model predictions with TruLens
Convert Hugging Face model repo to Safetensors
Explore GenAI model efficiency on ML.ENERGY leaderboard
Upload a machine learning model to Hugging Face Hub
Export Hugging Face models to ONNX
Convert PyTorch models to waifu2x-ios format
View and compare language model evaluations
Display model benchmark results
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.