Reproducible open-source STT API benchmarks with full methodology
Fixes WER scores by normalizing '$50' and 'fifty dollars' as equivalent.
Benchmark transcription APIs against real meeting audio. Measure WER, diarization, latency, and cost.
Tests Deepgram and AssemblyAI on actual crosstalk instead of clean audiobook samples.
Backend developers, CTOs choosing transcription infrastructure
Hugging Face Spaces · MLPerf
Fixes WER scores by normalizing '$50' and 'fifty dollars' as equivalent.
Finally exposes vendor BS by ranking scrapers on hard targets like Amazon and Cloudflare.
Dual-channel audio capture via PulseAudio is a genuinely useful detail most wrappers miss.
Direct MFT parsing beats Everything by 2.8× in 30/30 benchmark cells.
Mining your own PRs as benchmarks beats generic SWE-bench tasks for agent config tuning.
Cuts keyterm hallucinations by 60% before audio hits Deepgram.