Open source speech foundation model that runs locally on CPU in real-time

Created by Neuphonic - building faster, smaller, on-device voice AI

State-of-the-art Voice AI has been locked behind web APIs for too long. NeuTTS Air is the world’s first super-realistic, on-device, TTS speech language model with instant voice cloning. Built off a 0.5B LLM backbone, NeuTTS Air brings natural-sounding speech, real-time performance, built-in security and speaker cloning to your local device - unlocking a new category of embedded voice agents, assistants, toys, and compliance-safe apps.

Key Features

• 🗣Best-in-class realism for its size - produces natural, ultra-realistic voices that sound human
• 📱Optimised for on-device deployment - provided in GGML format, ready to run on phones, laptops, or even Raspberry Pis
• 👫Instant voice cloning - create your own speaker with as little as 3 seconds of audio
• 🚄Simple LM + codec architecture built off a 0.5B backbone - the sweet spot between speed, size, and quality for real-world applications

Model Details

NeuTTS Air is built off Qwen 0.5B - a lightweight yet capable language model optimised for text understanding and generation - as well as a powerful combination of technologies designed for efficiency and quality:

• Audio Codec: NeuCodec - our proprietary neural audio codec that achieves exceptional audio quality at low bitrates using a single codebook
• Format: Available in GGML format for efficient on-device inference
• Responsibility: Watermarked outputs
• Inference Speed: Real-time generation on mid-range devices
• Power Consumption: Optimised for mobile and embedded devices

Get Started

1. Clone the Git Repo

   git clone https://github.com/neuphonic/neutts-air.git cd neuttsair

2. Install espeak (required dependency)

   Please refer to the following link for instructions on how to install espeak:

   https://github.com/espeak-ng/espeak-ng/blob/master/docs/guide.md

   brew install espeak sudo apt install espeak

3. Install Python dependencies

   The requirements file includes the dependencies needed to run the model with PyTorch. When using an ONNX decoder or a GGML model, some dependencies (such as PyTorch) are no longer required.

   The inference is compatible and tested on python>=3.11.

   pip install -r requirements.txt

Basic Example

Run the basic example script to synthesize speech:

python -m examples.basic_example \ --input_text "My name is Dave, and um, I'm from London" \ --ref_audio samples/dave.wav \ --ref_text samples/dave.txt

To specify a particular model repo for the backbone or codec, add the --backbone argument. Available backbones are listed in NeuTTS-Air huggingface collection.

Several examples are available, including a Jupyter notebook in the examples folder.

Simple One-Code Block Usage

from neuttsair.neutts import NeuTTSAir import soundfile as sf tts = NeuTTSAir( backbone_repo="neuphonic/neutts-air-q4-gguf", backbone_device="cpu", codec_repo="neuphonic/neucodec", codec_device="cpu") input_text = "My name is Dave, and um, I'm from London." ref_text = "samples/dave.txt" ref_audio_path = "samples/dave.wav" ref_text = open(ref_text, "r").read().strip() ref_codes = tts.encode_reference(ref_audio_path) wav = tts.infer(input_text, ref_codes, ref_text) sf.write("test.wav", wav, 24000)

NeuTTS Air requires two inputs:

1. A reference audio sample (.wav file)
2. A text string

The model then synthesises the text as speech in the style of the reference audio. This is what enables NeuTTS Air’s instant voice cloning capability.

Example Reference Files

You can find some ready-to-use samples in the examples folder:

• samples/dave.wav
• samples/jo.wav

Guidelines for Best Results

For optimal performance, reference audio samples should be:

1. Mono channel
2. 16-44 kHz sample rate
3. 3–15 seconds in length
4. Saved as a .wav file
5. Clean — minimal to no background noise
6. Natural, continuous speech — like a monologue or conversation, with few pauses, so the model can capture tone effectively

Every audio file generated by NeuTTS Air includes **Perth (Perceptual Threshold) Watermarker.**

Don't use this model to do bad things… please.