NeuTTS Nano Spanish

🚀 Spaces Demo, 🔧 Github

Q8 GGUF version, Q4 GGUF version

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. The NeuTTS Nano Multilingual Collection is a collection of super-fast, highly realistic, on-device TTS speech language models with instant voice cloning - built to run smoothly on CPUs and edge devices. With a compact backbone and an efficient LM + codec design, Nano models deliver strong naturalness and cloning quality at a fraction of the compute, making them ideal for embedded voice agents, assistants, toys, and privacy-sensitive applications.

[!NOTE] This model is Spanish only: see the multilingual collection page for other languages.

Key Features

• ⚡️ Ultra-fast for on-device — built for real-time or better-than-real-time generation on laptop-class CPUs
• 🗣 High realism for its size — natural, expressive speech in a comp act footprint
• 👫 Instant voice cloning — create a new speaker from just a few seconds of audio
• 📦 GGUF/GGML-friendly deployment — easy to run locally via CPU-first tooling
• 🔒 Local-first + compliance-friendly — keep audio and text on-device

[!CAUTION] Websites like neutts.com are popping up and they're not affliated with Neuphonic, our github or this repo.

We are on neuphonic.com only. Please be careful out there! 🙏

Model Details

NeuTTS Nano models are designed for maximum speed per parameter while retaining strong speaker similarity and naturalness:

• Backbone: compact LM backbone tuned for TTS token generation (Nano class)
• Audio Codec: NeuCodec - our open-source neural audio codec that achieves exceptional audio quality at low bitrates using a single codebook
• Format: quantisations available in GGUF format for efficient on-device inference
• Responsibility: Watermarked outputs
• Inference Speed: Optimised for real-time generation on CPUs
• Power Consumption: Designed for mobile and embedded devices

Parameter Count (Nano class)

• Active params (backbone only): ~116.8M
• Total params (backbone + tied embeddings/head): ~228.7M

Get Started with NeuTTS

1. Install System Dependencies (required): espeak-ng

[!CAUTION] espeak-ng is an updated version of espeak, as of February 2026 on version 1.52.0. Older versions of espeak and espeak-ng can exhibit significant phonemisation issues, particularly for non-English languages. Updating your system version of espeak-ng to the latest version possible is highly recommended.

[!NOTE] brew on macOS Ventura and later, apt in Ubuntu version 25 or Debian version 13, and choco/winget on Windows, install the latest version of espeak-ng with the commands below. If you have a different or older operating system, you may need to install from source: see the following link https://github.com/espeak-ng/espeak-ng/blob/master/docs/building.md

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

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

# Mac OS
brew install espeak-ng

# Ubuntu/Debian
sudo apt install espeak-ng

# Windows install
# via chocolatey (https://community.chocolatey.org/packages?page=1&prerelease=False&moderatorQueue=False&tags=espeak)
choco install espeak-ng
# via winget
winget install -e --id eSpeak-NG.eSpeak-NG
# via msi (need to add to path or folow the "Windows users who installed via msi" below)
# find the msi at https://github.com/espeak-ng/espeak-ng/releases

Windows users who installed via msi / do not have their install on path need to run the following (see https://github.com/bootphon/phonemizer/issues/163)

$env:PHONEMIZER_ESPEAK_LIBRARY = "c:\Program Files\eSpeak NG\libespeak-ng.dll"
$env:PHONEMIZER_ESPEAK_PATH = "c:\Program Files\eSpeak NG"
setx PHONEMIZER_ESPEAK_LIBRARY "c:\Program Files\eSpeak NG\libespeak-ng.dll"
setx PHONEMIZER_ESPEAK_PATH "c:\Program Files\eSpeak NG"

2. Install NeuTTS

   pip install neutts
   

   Or for a local editable install, clone the neutts repository and run in the base folder:

   pip install -e .
   

   Alternatively to install all dependencies, including onnxruntime and llama-cpp-python (equivalent to steps 3 and 4 below):

   pip install neutts[all]
   

   or for an editable install:

   pip install -e .[all]
   

3. (Optional) Install llama-cpp-python to use .gguf models.

   pip install "neutts[llama]"
   

   Note that this installs llama-cpp-python without GPU support. To install with GPU support (e.g., CUDA, MPS) please refer to: https://pypi.org/project/llama-cpp-python/

4. (Optional) Install onnxruntime to use the .onnx decoder.

   pip install "neutts[onnx]"
   

Examples

To get started with the example scripts, clone the neutts repository and navigate into the project directory:

git clone https://github.com/neuphonic/neutts.git
cd neutts

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

Basic Example

Run the basic example script to synthesize speech:

python -m examples.basic_example \
  --input_text "Me llamo José. Tengo 25 años y acabo de mudarme a Londres. El metro es bastante confuso, pero me lleva a todos lados en poco tiempo." \
  --ref_audio samples/mateo.wav \
  --ref_text samples/mateo.txt \
  --backbone neuphonic/neutts-nano-spanish

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

[!CAUTION] It is highly recommended to use a same-language reference for best performance: see this readme section for appropriate example references.

Simple One-Code Block Usage

from neutts import NeuTTS
import soundfile as sf

tts = NeuTTS(
    backbone_repo="neuphonic/neutts-nano-spanish",
    backbone_device="cpu",
    codec_repo="neuphonic/neucodec",
    codec_device="cpu",
)

input_text = "Me llamo José. Tengo 25 años y acabo de mudarme a Londres. El metro es bastante confuso, pero me lleva a todos lados en poco tiempo."

ref_text_path = "samples/mateo.txt"
ref_audio_path = "samples/mateo.wav"

ref_text = open(ref_text_path, "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)

Tips

NeuTTS Nano 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 Nano’s instant voice cloning capability.

Example Reference Files

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

• samples/mateo.wav

Guidelines for Best Results

For optimal performance, reference audio samples should be:

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