Huashu Design
alchaincyf · Skill
Turns a one-line request in your AI agent into a finished design: a launch animation, a clickable app prototype, an editable slide deck, an infographic or a se…
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.
pip install -U huggingface_hub
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.
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.
| 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 |
pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-TTS-Nano-100M
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 |
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.
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.
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.
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:
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:
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:
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.
README.md 5 KB
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 | |
| Scylla's BandShares text-to-speech · pytorch | 929 | 17 | — | apache-2.0 | |
| Silencio-2Shares text-to-speech · pytorch | 351 | 22 | 27.9M | cc-by-4.0 | |
| Klein-TTS-X1.5-ProShares text-to-speech · pytorch | 258 | 10 | 13.1M | apache-2.0 | |
| Kokoro-82MShares text-to-speech | 12,580,963 | 7,403 | — | apache-2.0 | |
| Chatterbox TTSShares text-to-speech | 2,440,902 | 1,911 | — | mit |
Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search and on Trendshift. Mindket doesn’t write or check them. Open one to read it where it was posted.
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