LibreAI / Audio / MOSS-TTS
// audio · OpenMOSS/MOSS-TTS

MOSS-TTS

MOSS-TTS family: open text-to-speech models for voice cloning, dialogue, voice design, and real-time agents

★ 4.2k stars⑂ 378 forksApache-2.0updated 2026-09
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

MOSS-TTS is an open-source audio project by OpenMOSS (OpenMOSS/MOSS-TTS on GitHub). It has 4,184 GitHub stars and 378 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 6 September 2026.

GitHub stars
4,184
Forks
378
License
Apache-2.0
Last updated on GitHub
6 September 2026
Category
Audio
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Pick the family member

    Choose by job: flagship or v1.5 for general TTS/cloning, TTSD for dialogue, VoiceGenerator for designed voices, Realtime for agents, SoundEffect for SFX.

  2. Clone and install

    Clone the repo and install with the torch-runtime extra (or the torch-free runtime path).

  3. Download weights

    Fetch the chosen model's weights from Hugging Face or ModelScope as linked in the README.

  4. Generate or serve

    Run the model's inference example, or serve it behind the OpenAI-compatible speech endpoint.

// features

Why people love it

A full model family

Flagship, v1.5, local-transformer, dialogue (TTSD), voice-design, realtime, and sound-effect models in one project.

31-language v1.5

Multilingual synthesis with language tags, explicit [pause X.Ys] control, and Pinyin/phoneme/duration control.

Stable zero-shot cloning

Clone voices from reference audio, with long-form generation support.

Dialogue generation

MOSS-TTSD targets multi-speaker podcasts, dialogue, and dubbing.

OpenAI-compatible serving

Serve models via SGLang-Omni or vLLM-Omni at /v1/audio/speech for drop-in integration.

Flexible runtimes

PyTorch runtime, optional flash-attention, or a torch-free llama.cpp/ONNX path; fine-tuning tutorials included.

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// run it

Run it yourself

  1. Clone the repository

    Clone the project and enter its directory.

    git clone https://github.com/OpenMOSS/MOSS-TTS.git
    cd MOSS-TTS
  2. Install with the torch runtime

    Install the package with its PyTorch runtime extra (CUDA 12.8 wheels per the README).

    pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e ".[torch-runtime]"
  3. Generate speech

    Download a family member's weights from Hugging Face and follow its README example to synthesize from text (and reference audio for cloning).

    # see the chosen model's section in the README for the exact inference command
// use cases

What you can build

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// faq

Questions, answered

Who maintains MOSS-TTS?

OpenMOSS, the MOSI.AI lab, maintains the MOSS model family.

Is it free?

Yes — the project is Apache-2.0 licensed and weights are published on Hugging Face and ModelScope.

Does it need a paid API key?

No. Models run locally; the larger ones expect a capable GPU.

How does it compare to MOSS-TTS-Nano?

Nano is the tiny 0.1B CPU-friendly sibling. This repo is the full family — up to the 8B flagship — with dialogue, voice design, realtime, and sound-effect models for higher fidelity.

How do I get started?

Clone the repo, install with the `.[torch-runtime]` extra, pick a family member, download its weights, and run its README example.

// keep exploring

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// alternatives

Alternatives in Audio

The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if MOSS-TTS is not the right fit.

Get MOSS-TTS

Free and open — grab the source, star the repo, and start creating.