Find the best ASR model for a language and dataset
Generate and convert speech using text and audio inputs
Generate Ukrainian voice audio from text
Convert audio to a specific voice
Transform and convert audio voices to different styles
Generate audio from text with different voices
Anonymize your voice with a chosen model
Better AI powered platform to purify your speech signal
Create cloned voice from your text and audio
Install and run a voice processing application
Voices transform your audio or text into singing
Remove vocals from your music tracks easily
Convert audio to a different voice
The 🤗 Speech Bench is a comprehensive benchmarking platform designed to evaluate Automatic Speech Recognition (ASR) models across various languages and datasets. It provides a centralized framework for comparing model performance, ensuring transparency, and facilitating research advancements in voice recognition technology.
• Multi-Lingual Support: Evaluate ASR models across multiple languages and dialects. • Extensive Dataset Coverage: Test models on diverse datasets to assess real-world performance. • Model Comparison: Directly compare different ASR models using standardized metrics. • Customizable Benchmarks: Define specific evaluation criteria tailored to your needs. • Community-Driven: Leverage insights and contributions from the broader speech recognition community. • Open-Source Access: Utilize and contribute to the platform's open-source resources.
What is The 🤗 Speech Bench used for?
The 🤗 Speech Bench is used to evaluate and compare the performance of ASR models across various languages and datasets, helping users identify the best model for their specific needs.
Is The 🤗 Speech Bench free to use?
Yes, The 🤗 Speech Bench is part of the Hugging Face ecosystem, which offers free access to its benchmarking tools and resources.
How do I interpret the benchmark results?
Benchmark results are presented in standardized metrics such as Word Error Rate (WER) and Character Error Rate (CER). Lower values indicate better performance. Use these scores to compare models and select the most suitable one for your application.