Speech-to-Digits API – 95% accuracy on spoken numbers

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Hi HN, I built EchoEntry ( https://echoentry.ai ) – a speech-to-text API optimized specifically for digits. The problem: Generic STT APIs struggle with numbers. "One oh five" becomes "105" sometimes, "15" other times. For healthcare apps, warehouse systems, or IVR, this inconsistency breaks workflows. My solution: Fine-tuned Whisper-small on 1-999 spoken numbers across 5 English accents. Gets 95% accuracy on 1-3 digit numbers. Tech stack: - Custom Whisper model (1.7GB) - FastAPI backend - Deployed on 8GB Linode - FFmpeg for audio processing Try it now (two commands, no signup): # Download test audio curl -O https://echoentry.ai/test_audio.wav # Test the API curl -X POST https://api.echoentry.ai/v1/transcribe \ -H "X-Api-Key: demo_key_12345" \ -F "file=@test_audio.wav;type=audio/wav" Currently free beta (1,000 calls/month per key). Looking for feedback on: 1. What accuracy threshold makes this production-ready for you? 2. Are there other number-heavy use cases I'm missing? 3. Would you pay for this vs. using generic STT? Docs: https://echoentry.ai/docs.html Happy to answer technical questions about the fine-tuning process or deployment!

Visit product View on Hacker NewsJan 27, 2026

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