Coqui TTS

Once the standard open TTS toolkit, now unmaintained; the Idiap fork on PyPI as coqui-tts carries it on.

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About

For several years the coqui-ai/TTS repository was the default open toolkit for speech synthesis: one Python package with a command line tool, a model zoo, training recipes, and implementations of Tacotron 2, Glow-TTS, VITS, YourTTS, Bark, Tortoise and the XTTS cloning models. Its maker, a startup founded by former Mozilla speech engineers, closed in January 2024. The original repo took its last commit in February 2024, and its final PyPI release, TTS 0.22.0 from December 2023, installs only on Python 3.9 to 3.11, which rules out current distributions unless you keep an older interpreter around. The code itself is sound, and the community carried it forward: Idiap Research Institute maintains a fork published on PyPI as coqui-tts, with releases through 2026, support for Python 3.10 to 3.14, prebuilt wheels for Windows and macOS, and the same TTS import path, so most old scripts run after swapping the package name. Code stays MPL-2.0, while each pretrained voice has its own license. For new work, install the Idiap fork, or try lighter current models such as Kokoro or Piper for plain narration.

Should you use Coqui TTS?

Pick it when

Pick Coqui TTS when you want to train or fine-tune your own voices across classic architectures like VITS, Glow-TTS, and Tacotron in one Python toolkit, with models that can run inference on CPU, and the MPL-2.0 code license works for you.

Look elsewhere when

Skip it for new commercial products: Coqui the company shut down in early 2024, upkeep now falls to community forks, and its XTTS cloning weights restrict commercial use. Kokoro or Piper are simpler for plain local speech.

Alternatives to Coqui TTS

  • Kokoro TTS

    Single 82M Apache-2.0 model that runs on CPU with little setup, but you give up Coqui's training recipes and choice of architectures.

  • Piper TTS

    Faster and lighter, runs on a Raspberry Pi with many prebuilt voices, though it offers one VITS-based design instead of a research toolkit.

  • Chatterbox TTS

    Zero-shot cloning with emotion control under a permissive license, a cleaner commercial path than XTTS weights, but it needs a GPU with about 4 GB.

  • F5-TTS

    Newer flow matching model that clones a voice from a short clip, but it needs a GPU and its pretrained checkpoints are CC-BY-NC, so commercial use means training your own.

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