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Speechos – Benchmark 25 speech AI models locally, no cloud needed
Side-by-side model comparison eliminates guessing which speech engine fits your hardware.
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114mo ago
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GitHub Repository
A lightweight library for normalizing speech transcripts before computing WER
27 starsPython
Reproducible open-source STT API benchmarks with full methodology
by jilijeanlouis·Mar 24, 2026·1 point·1 comment
AI Analysis
●●SolidSolve My ProblemCozy
Fixes WER scores by normalizing '$50' and 'fifty dollars' as equivalent.
Strengths
- •Three-stage pipeline (pre-process, word, post-process) is deterministic and YAML-configurable.
- •Expands contractions, converts symbols, removes fillers before WER computation.
- •Language-aware normalization prevents false penalties for formatting differences.
Weaknesses
- •From Gladia—an STT vendor—may bias toward their evaluation needs.
- •Only 2 GitHub stars suggests limited community adoption so far.
Category
Target Audience
STT engineers, ML researchers evaluating speech models
Similar To
jiwer · SpeechBrain · Kaldi scoring tools
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