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Earlybird

29M-param speech-to-text that outputs words before they end

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Earlybird is a 29M-parameter, 17 MB on-device speech-to-text model that predicts each word correctly about 90 ms before the speaker finishes it, with a confidence on every word. Built for voice agents that need to act sooner. Open weights and code.

PH · launched Nov 11, 2026 · 87 upvotes (top 1%) · 0 views

Niche: speech and voice ai models · 143 launches, 7 in the last 7 days ▼ -30% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Commercial product
  • Open Source
  • Developer Tools
  • Artificial Intelligence
  • GitHub

Visit website ↗ Source listing ↗

Could you build this?

No The product is a custom-trained speech-to-text model that predicts words before they are spoken, which requires ML research, training data, and speech-recognition expertise.

What it would actually take: A real version requires a streaming acoustic model architecture (such as a causal or lookahead-limited transformer or conformer), large labeled speech corpora, training infrastructure with GPUs, and careful evaluation of latency and per-word confidence calibration. Deploying it on-device also needs model compression and optimization for mobile hardware.

Competitors

Other products that read as similar to this one : 250 launches clear the similarity bar, closest 8 shown.

Attention rank: #11 of 251 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 365 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.