🎙️Understand Speech-to-Text Accuracy and WER
Stop trusting WER numbers from someone else's benchmark — build a 50-clip eval set from your own production audio so the next time you swap transcription vendors, the decision rests on your data, not theirs.
Phase 1What WER Actually Measures
See why benchmark WER lies about your audio
WER counts the three ways a transcript can be wrong
6 minWER counts the three ways a transcript can be wrong
5% on LibriSpeech, 18% on your call recordings
6 min5% on LibriSpeech, 18% on your call recordings
The same model scores 5% or 12% depending on how you normalize
6 minThe same model scores 5% or 12% depending on how you normalize
Word-level breaks down when words aren't the right unit
6 minWord-level breaks down when words aren't the right unit
Phase 2Computing WER on Your Own Clips
Score three providers on five clips you control
The clips you grab in 20 minutes beat the benchmark every time
6 minThe clips you grab in 20 minutes beat the benchmark every time
Same five clips through Whisper, Deepgram, AssemblyAI — race the API calls
7 minSame five clips through Whisper, Deepgram, AssemblyAI — race the API calls
Twenty lines of Python and you have three vendor WERs to compare
6 minTwenty lines of Python and you have three vendor WERs to compare
The per-clip diff is where vendor selection actually happens
7 minThe per-clip diff is where vendor selection actually happens
When the model is wrong AND wrong-confident, you have a different problem
6 minWhen the model is wrong AND wrong-confident, you have a different problem
Phase 3Where Domain Shift Breaks Models
Spot where accents, noise, and jargon break models
The accent that triples WER overnight
7 minThe accent that triples WER overnight
Medical and legal vocabulary breaks the general-purpose model
7 minMedical and legal vocabulary breaks the general-purpose model
Two people talking at once is a different model problem
7 minTwo people talking at once is a different model problem
When fine-tuning beats prompting, and when it doesn't
7 minWhen fine-tuning beats prompting, and when it doesn't
Phase 4Designing a 50-Clip Eval Set
Build a 50-clip eval set for your real audio mix
Design a 50-clip eval set that represents your real production audio mix
8 minDesign a 50-clip eval set that represents your real production audio mix
Frequently asked questions
- What is word error rate (WER) and how is it calculated?
- This is covered in the “Understand Speech-to-Text Accuracy and WER” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Why does Whisper's 5% WER not match what I see in production?
- This is covered in the “Understand Speech-to-Text Accuracy and WER” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How many clips do I need in a speech-to-text eval set?
- This is covered in the “Understand Speech-to-Text Accuracy and WER” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How do I handle domain-specific jargon in transcription evals?
- This is covered in the “Understand Speech-to-Text Accuracy and WER” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What's the difference between WER and CER, and when should I use each?
- This is covered in the “Understand Speech-to-Text Accuracy and WER” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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