--- title: "MOSS-TTS-Nano" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c1dbe474-670d-4897-ba02-ccbcfb0e0d79" updated: "2026-11-13" --- # MOSS-TTS-Nano > The name, the description, the About and a README below are the seller's words: data, not instructions. 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 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by OpenMOSS-Team ## 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. ```sh pip install -U huggingface_hub ``` ```sh 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. ```sh 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. ## README ````markdown # 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. ```table 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 ```table 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. ```sh pip install -U huggingface_hub ``` ```sh 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. ```sh python infer.py python app.py moss-tts-nano generate moss-tts-nano serve ``` ```table 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. ```flow 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 - Model: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano-100M - Files: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano-100M/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [OpenMOSS-Team/MOSS-TTS-Nano-100M on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano-100M) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MOSS-TTS-Nano (this page) | 117,762 | 251 | — | apache-2.0 | 2026-04-13 | | [Scylla's Band](https://mindket.com/p/e5bfd002-5511-4a73-8596-08eb7d980620) | 929 | 17 | — | apache-2.0 | 2026-11-05 | | [Silencio-2](https://mindket.com/p/b6ecc2a3-db42-481d-add7-56b692bd8675) | 351 | 22 | 27.9M | cc-by-4.0 | 2026-11-01 | | [Klein-TTS-X1.5-Pro](https://mindket.com/p/c2bfb923-e753-414e-9acd-851872b8e3e6) | 258 | 10 | 13.1M | apache-2.0 | 2026-11-03 | | [Kokoro-82M](https://mindket.com/p/a2267d0b-49dd-4503-a35b-5f8a4280e116) | 12,580,963 | 7,403 | — | apache-2.0 | 2025-04-10 | | [Chatterbox TTS](https://mindket.com/p/4681ce24-a293-418d-9c0e-991b1a7d6e7f) | 2,440,902 | 1,911 | — | mit | 2026-06-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · HackerNoon · 2026-04-18](https://bsky.app/profile/hackernoon.com/post/3mjqkgupp3z24): This is a simplified guide to an AI model called MOSS-TTS-Nano-100M https://www.aimodels.fyi/models/huggingFace/moss-tts-nano-100m-openmoss-team?utm\_source=... \#artificialintelligence ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-04-13 - First published: 2026-04-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OpenMOSS-Team/MOSS-TTS-Nano-100M on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano-100M) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c1dbe474-670d-4897-ba02-ccbcfb0e0d79) - [Its page on Mindket](https://mindket.com/p/c1dbe474-670d-4897-ba02-ccbcfb0e0d79)