neutts

Following our #1 trending NeuTTS Air release in October, we’re pushing the limits of "small" even further. We’ve just released NeuTTS Nano, an ultra-lightweight speech foundation model under Apache 2.0. The main idea: Frontier-quality text-to-speech with a tiny 120M active parameter footprint - 3x smaller than our previous model. Why we built this: - Extreme Edge Deployment: At this size, real-time TTS is now viable on the most resource-constrained hardware, including older Raspberry Pis and mobile devices, without needing a local GPU; - Llama3 Architecture: Built off a Llama3-based LM + codec architecture, it maintains high-quality prosody and realism despite the 3x size reduction; - Privacy & Control: Full on-device inference means sensitive data never leaves the hardware, with zero API costs or rate limits; - Instant Cloning: Supports

Removing the watermark looks trivial https://github.com/neuphonic/neutts-air/blob/d9761a3d938b06c... Watermarking is usually very fragile and generally relies on an adversary not knowing about it. I honestly don't know why anyone bothers with it.

The model weighs 1.5GB [1] (the q4 quant is ~500MB) The demo is impressive. It uses reference audio at inference time, and it looks like the training code is mostly available [2][3] with a reference dataset [4] as well. From the README: > NeuTTS Air is built off Qwen 0.5B 1. https://huggingface.co/neuphonic/neutts-air/tree/main 2. https://github.com/neuphonic/neutts-air/issues/7 3. https://github.com/neuphonic/neutts-air/blob/feat/example-fi... 4. https://huggingface.co/datasets/neuphonic/emilia-yodas-engli...

"the model has a 2048 tokens limit. This includes the reference text/phones as well as the reference and generation audio tokens." https://github.com/neuphonic/neutts-air/issues/15#issuecomme... So "no rate limits", while true, is kind of setting different expectations.

We’ve just released Neuphonic TTS Air, a lightweight open-source speech foundation model under Apache 2.0. The main idea: frontier-quality text-to-speech, but small enough to run in realtime on CPU. No GPUs, no cloud APIs, no rate limits. Why we built this: - Most speech models today live behind paid APIs → privacy tradeoffs, recurring costs, and external dependencies. - With Air, you get full control, privacy, and zero marginal cost. - It enables new use cases where running speech models on-device matters (edge compute, accessibility tools, offline apps). Repo: https://github.com/neuphonic/neutts-air Would love feedback from HN on performance, applications, and contributions.