Mindket

MOSS-TTS-Nano

Newversion 1 · updated

Overview

Turns text into speech with a tiny 0.1B-parameter model that can run on a CPU, supports voice cloning and covers 20 languages. It suits developers who add speech to local or web apps.

MOSS-TTS-Nano is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Python on your computer
  • A CPU is enough, though a GPU is optional
  • The project's code, which provides the scripts and the packaged command line
Runs in
  • A CPU-only computer, since the card says streaming can run on a 4-core CPU
  • A local web demo started from the project's app script
  • A command line, for generating speech or serving it
  • Windows, macOS or Linux, with Python
Start with
pip install -U huggingface_hub

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

MOSS-TTS-Nano is an open-source text-to-speech model from MOSI.AI and the OpenMOSS team. It has only 0.1B parameters and is built for realtime speech. It can run on a CPU with no GPU. The setup is light enough to try locally, serve on the web or build into a small product.

Who it suits

It suits developers who want speech output without a heavy GPU setup. People who clone voices should use only voices they have the right to use.

IndustryWho uses itHow they use itExample
SoftwareApp developersAdd spoken replies to a local or web productServing speech to a web page from your own machine
Content creationCreatorsRead long text aloud, with voice cloning from a reference clipNarrating a long article in a cloned voice you own
EducationLanguage teachersGenerate speech in several languagesProducing spoken examples in Japanese or Spanish
ResearchSpeech researchersStudy a small audio-tokenizer-plus-LLM designTesting realtime latency on a CPU

People who need the highest studio quality can skip it, since the card aims for good-enough quality in realtime products.

Where to use it

  • A CPU-only computer, since the card says streaming can run on a 4-core CPU
  • A local web demo started from the project's app script
  • A command line, for generating speech or serving it
  • Windows, macOS or Linux, with Python
Use caseWhoWhat they get back
Voice cloningCreatorsSpeech in a cloned voice, with long text split into chunks
Local web demoDevelopersA browser page that speaks text you type
Command line servingDevelopersA service that produces speech on request

How to set it up

  • Python on your computer
  • A CPU is enough, though a GPU is optional
  • The project's code, which provides the scripts and the packaged command line
  1. Install the Hugging Face hub package.
  2. Download the model files.
  3. Set up the environment as the model card describes.
pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-TTS-Nano-100M

How to use it

The card names four ways to run it. One script does voice cloning, one starts a local web demo, and the packaged command line can generate speech or run a server. The options each one takes are not in the text provided here.

python infer.py
python app.py
moss-tts-nano generate
moss-tts-nano serve
ToolWhat it does
infer.pyRuns voice cloning from a script
app.pyStarts a local web demo
moss-tts-nano generateGenerates speech from the command line
moss-tts-nano serveRuns the model as a local server

Example

You start the local web demo, type a short paragraph and play back the result. With voice cloning, you supply a reference clip of a voice you may use and read a long text in that voice. The card says long input is split into chunks automatically.

For newcomers and experts

A text-to-speech model reads written text aloud. A first small step is to try the hosted demo the card links, then run the web demo on your own machine.

It is a pure autoregressive design that pairs an audio tokenizer with a language model. Output is 48 kHz in two channels, and streaming inference keeps the delay to the first audio low. The card lists 20 languages, including Japanese, Korean, Arabic, Russian, Polish, Portuguese and Swedish.

  1. You enter text
  2. Language model predicts audio tokens
  3. Audio tokenizer decodes them to sound
  4. Speech streams out at 48 kHz

Good to know

  • License: Apache 2.0.
  • Cost to run: none named for local use. The card also links a hosted studio and API docs, with no price given.
  • Data it reaches: it downloads files from the Hugging Face Hub. Your text and any reference voice clips stay on your machine.
  • Not for: cloning voices you have no right to use.

README and files

README

MOSS-TTS-Nano

Turns text into speech with a tiny 0.1B-parameter model that can run on a CPU, supports voice cloning and covers 20 languages. It suits developers who add speech to local or web apps.

Made by OpenMOSS-Team on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: MOSS-TTS-Nano is an open-source text-to-speech model from MOSI.AI and the OpenMOSS team. It has only 0.1B parameters and is built for realtime speech. It can run on a CPU with no GPU. The setup is light enough to try locally, serve on the web or build into a small product.

Who it suits: It suits developers who want speech output without a heavy GPU setup. People who clone voices should use only voices they have the right to use.

Industry | Who uses it | How they use it | Example
Software | App developers | Add spoken replies to a local or web product | Serving speech to a web page from your own machine
Content creation | Creators | Read long text aloud, with voice cloning from a reference clip | Narrating a long article in a cloned voice you own
Education | Language teachers | Generate speech in several languages | Producing spoken examples in Japanese or Spanish
Research | Speech researchers | Study a small audio-tokenizer-plus-LLM design | Testing realtime latency on a CPU

People who need the highest studio quality can skip it, since the card aims for good-enough quality in realtime products.

Where to use it:

  • A CPU-only computer, since the card says streaming can run on a 4-core CPU
  • A local web demo started from the project's app script
  • A command line, for generating speech or serving it
  • Windows, macOS or Linux, with Python
Use case | Who | What they get back
Voice cloning | Creators | Speech in a cloned voice, with long text split into chunks
Local web demo | Developers | A browser page that speaks text you type
Command line serving | Developers | A service that produces speech on request

How to set it up:

  • Python on your computer
  • A CPU is enough, though a GPU is optional
  • The project's code, which provides the scripts and the packaged command line
  1. Install the Hugging Face hub package.
  2. Download the model files.
  3. Set up the environment as the model card describes.
pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-TTS-Nano-100M

How to use it: The card names four ways to run it. One script does voice cloning, one starts a local web demo, and the packaged command line can generate speech or run a server. The options each one takes are not in the text provided here.

python infer.py
python app.py
moss-tts-nano generate
moss-tts-nano serve
Tool | What it does
infer.py | Runs voice cloning from a script
app.py | Starts a local web demo
moss-tts-nano generate | Generates speech from the command line
moss-tts-nano serve | Runs the model as a local server

Example: You start the local web demo, type a short paragraph and play back the result. With voice cloning, you supply a reference clip of a voice you may use and read a long text in that voice. The card says long input is split into chunks automatically.

For newcomers and experts: A text-to-speech model reads written text aloud. A first small step is to try the hosted demo the card links, then run the web demo on your own machine.

It is a pure autoregressive design that pairs an audio tokenizer with a language model. Output is 48 kHz in two channels, and streaming inference keeps the delay to the first audio low. The card lists 20 languages, including Japanese, Korean, Arabic, Russian, Polish, Portuguese and Swedish.

You enter text
Language model predicts audio tokens
Audio tokenizer decodes them to sound
Speech streams out at 48 kHz

Good to know:

  • License: Apache 2.0.
  • Cost to run: none named for local use. The card also links a hosted studio and API docs, with no price given.
  • Data it reaches: it downloads files from the Hugging Face Hub. Your text and any reference voice clips stay on your machine.
  • Not for: cloning voices you have no right to use.

On Hugging Face, 13 November 2026

  • Task: text to speech
  • Downloads in the last 30 days: 117,762
  • Likes: 251
  • License: apache-2.0
  • Languages: zh, en, de, es, fr, ja, it, he
  • Quantized versions: 5
  • Fine-tunes: 9
  • Tags: pytorch, moss_tts_nano, text-to-speech

Links

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-TTS-Nano-100M

Then follow the usage in its model card. Nothing in this kit runs by itself.

Files
README.md  5 KB

1 file, 5 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-13

Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

ProductDownloads, last 30 daysLikesParametersLicenseLast change
MOSS-TTS-Nano This page117,762251—apache-2.0
Scylla's BandShares text-to-speech · pytorch92917—apache-2.0
Silencio-2Shares text-to-speech · pytorch3512227.9Mcc-by-4.0
Klein-TTS-X1.5-ProShares text-to-speech · pytorch2581013.1Mapache-2.0
Kokoro-82MShares text-to-speech12,580,9637,403—apache-2.0
Chatterbox TTSShares text-to-speech2,440,9021,911—mit
Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

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