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Kitten TTS Based Low-Latency Streaming Voice Assistant on CPU

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Kitten TTS Based Low-Latency Streaming Voice Assistant on CPU

We asked Neo AI to build a small voice assistant pipeline that runs with low latency on CPU instead of requiring a GPU. The goal was to see how responsive a LLM → speech system can be on normal laptops or edge devices. It includes: - Voice Activity Detection - CPU-friendly LLM + TTS streaming - Async pipeline to reduce latency Modular LLM backend Useful for local assistants, robotics prototypes, privacy-first setups, or benchmarking STT/LLM/TTS latency. We’ve been experimenting with similar CPU-first pipelines inside NEO workflows for on-device agents, and this repo is a minimal standalone version. Would love suggestions on lightweight STT/TTS models or latency tricks people have used on CPU.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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