Benchmarks
Metric: Word Error Rate (WER), lower is better
Word Error Rate (WER) is a common way to measure how accurate speech recognition systems are. It compares what the system heard to what was actually said. It measures the model’s ERROR, so lower is better. It divides the number of word-level errors by the total number of words
A Quick Primer on Word Error Rate (WER)
What is WER?
Word Error Rate (WER) is a common way to measure how accurate speech recognition systems are. It compares what the system heard to what was actually said. It measures the model’s ERROR, so lower is better. It divides the number of word-level errors by the total number of words
Why it matters: WER tells us how reliable a speech-to-text system is. A lower WER means fewer mistakes and better performance—critical for areas like healthcare, legal, and customer service.
Strengths: Simple to calculate, Easy to compare different systems, Works across languages
Weaknesses: Treats all errors equally—even if some are more harmful (e.g., “don’t take” vs “take”); Even single character errors like carrot vs carot get full penalty, so it can be overly harsh and punitive; Doesn’t consider punctuation or context; May not reflect user satisfaction or usefulness
What is WER?
WER = (Substitutions +
Insertions + Deletions) ÷ Total Words
Substitutions: wrong words
Insertions: extra words
Deletions: missing words
Spoken: “Take your medicine daily”
Transcript: “Take your message daily”
WER = 1 error ÷ 4 words = 25%
Meet the family
of models
intron-sahara-asr
general purpose cross-domain speech recognition model
intron-sahara-stream
streaming model optimised for medical conversations
intron-sahara-voice-lock
biometric voice-based authentication tuned for African accents and languages to combat fraud
intron-sahara-asr
general purpose cross-domain speech recognition model
intron-sahara-stream
streaming model optimised for medical conversations
Sahara-TTS
Sahara Voice Lock
Spoof-aware Voice authentication and security, tuned for African voices, accents and languages to combat fraud and deepfakes
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