LibreAI / Audio / Qwen3-TTS
// audio · QwenLM/Qwen3-TTS

Qwen3-TTS

Alibaba's open voice models: expressive, streaming-capable speech with free-form voice design.

★ 13.6k stars⑂ 1.8k forksApache-2.0updated 2026-03
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

Qwen3-TTS is an open-source audio project by QwenLM (QwenLM/Qwen3-TTS on GitHub). It has 13,619 GitHub stars and 1,764 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 17 March 2026.

GitHub stars
13,619
Forks
1,764
License
Apache-2.0
Last updated on GitHub
17 March 2026
Category
Audio
Listing
Full install guide
Official site
Read the docs
// how it works

How it works

  1. Set up a clean environment

    Create an isolated Python 3.12 environment so the audio dependencies don't clash with your other projects.

  2. Install the package

    One pip command pulls the runtime and lets you load any released Qwen3-TTS model.

  3. Load a model and synthesize

    Pick a model size for your hardware, feed it text, and get expressive speech — with streaming for realtime use.

  4. Design or clone voices

    Describe the voice you want in words, or provide a short sample to clone it, then reuse it across projects.

// features

Why people love it

Expressive neural voices

Natural intonation and emotion, well beyond robotic reader voices.

Streaming generation

Audio starts flowing before the full text is processed — ideal for live agents and assistants.

Free-form voice design

Describe a voice in plain language (“warm, middle-aged narrator”) and get it.

Voice cloning

Clone a voice from a short sample for consistent characters and branding.

A family of sizes

Multiple released models so you can trade quality against speed and VRAM.

Apache-2.0

Permissive licensing for personal and commercial projects.

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// run it

Run it yourself

  1. Step 1 — Create the environment

    Make a fresh, isolated Python 3.12 environment.

    conda create -n qwen3-tts python=3.12 -y
    conda activate qwen3-tts
  2. Step 2 — Install

    Install the qwen-tts package from PyPI.

    pip install -U qwen-tts
  3. Step 3 — Save VRAM (optional)

    Add FlashAttention 2 to cut GPU memory usage.

    pip install -U flash-attn --no-build-isolation
// use cases

What you can build

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// faq

Questions, answered

Qwen3-TTS or Kokoro?

Qwen3-TTS brings larger, more expressive models with streaming and voice design. Kokoro is lighter and faster to run. Choose expressive depth or lightweight speed.

Do I need a GPU?

A GPU is recommended for comfortable speeds; FlashAttention 2 substantially reduces the memory footprint.

Does it stream audio?

Yes — streaming generation is a headline feature, built for realtime applications.

Who makes it?

The Qwen team at Alibaba Cloud, the same group behind the popular Qwen language models.

Can I use it commercially?

Yes — it is released under the Apache-2.0 license.

// keep exploring

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// alternatives

Alternatives in Audio

The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if Qwen3-TTS is not the right fit.

Get Qwen3-TTS

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