Open Source AI Benchmarking toolkit for benchmarking speech to text services
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Updated
Apr 17, 2024 - Python
Open Source AI Benchmarking toolkit for benchmarking speech to text services
🐍📦 Ultra-fast Python package for calculating and analyzing the Word Error Rate (WER). Built for the scalable evaluation of speech and transcription accuracy.
Phone and word timestamp accuracy on human-labeled TIMIT and Buckeye, clean and under reverb, noise, music and babble. Two tracks, forced aligners given the transcript and ASR systems that find their own words. MFA, WhisperX, Charsiu, Qwen3, Parakeet-TDT, Olign and more. Monthly leaderboards, paper on arXiv.
Core engine behind Calibrate, a framework for evaluating AI agents: speech-to-text, text-to-speech, LLM evaluation, end-to-end simulations
Backend for Calibrate, a framework for evaluating AI agents: speech-to-text, text-to-speech, LLM evaluation, end-to-end simulations
Frontend for Calibrate, a framework for evaluating AI agents: speech-to-text, text-to-speech, LLM evaluation, end-to-end simulations
Run AI agent evaluations in your CI/CD pipeline using Calibrate and catch regressions
Python SDK for Calibrate's API
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