Nicheloom visits: 58,238 all-time · 7,637 in the last 7 days

The opportunity tracker for new startups.

Today: 1,008 launches · Niche of the day: specialized calculation tools · See what launched today →

Voice gender classifier for European voice AI (1MB, ONNX, 4ms)

Get picks like this daily. Today's launch numbers, the top launches, AI and tech news, and a daily opportunity pick, straight to your inbox.

Want to reach these readers? Sponsor an issue.

Hi, I'm Kamil and I'm a founder of Applied AI agency in Warsaw, Poland.We've trained a small <1MB voice classifier model that runs on CPU in 4ms. Can be run next to silero VAD in voice AI deployments.What we noticed in production deployments of voice assistants in Contact Centers in EU is that human consultants pick up immediately how to inflect verbs and ajdectives after one utterance from the caller. But voice AI agents don't know it until 1-2 minutes into the call when they are either corrected or the caller uses explicitly words with male/female form a couple of times.Our model solves this just from the first utterance of caller speech. The voice AI pipeline can inject the classification as context to the system prompt. We observed a significant impact of this on the adoption of voice AI in practice.Model + paper: https://huggingface.co/syntropicsignal-ai/gender-voice-class...

HN · launched May 12, 2026 · 5 upvotes (top 89%) · 2 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
  • Hobby / open-source project

Visit website ↗ Source listing ↗

More like this: all 143 launches in speech and voice ai models →

Could you build this?

No Training an ultra-compact (<1MB), low-latency (4ms) Bi-LSTM audio classifier across multiple European languages requires specialized DSP, speech dataset curation, and deep acoustic ML training pipelines.

What it would actually take: Building this requires acoustic DSP preprocessing (spectrograms, MFCCs/FBanks), curated and balanced multilingual speech corpora (e.g., Common Voice, FLEURS, LibriSpeech with gender labels), and PyTorch model architecture exploration (small Bi-LSTM or 1D-CNN) constrained under 166k parameters. It demands quantization and export to ONNX Runtime with zero-copy C++/Python bindings to achieve <5ms single-threaded CPU execution, requiring audio ML and systems engineering expertise.

Discussion

2 comments analyzed.

Competitors

Other products that read as similar to this one : 704 launches clear the similarity bar, closest 8 shown.

Attention rank: #676 of 705 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 190 days after the earliest competitor.

Related data

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.