Mozilla TTS
Deep learning TTS library by Mozilla with Tacotron and WaveRNN implementations.
About
Before Coqui, there was Mozilla TTS: a deep learning text-to-speech library that collected research-grade implementations of text-to-spectrogram models (Tacotron, Tacotron2, Glow-TTS, Speedy-Speech) and neural vocoders (MelGAN, Multiband-MelGAN, ParallelWaveGAN, WaveGrad, WaveRNN) behind a common training and inference framework, along with a GE2E-based speaker encoder and dataset quality analysis tools. Pretrained models were released in PyTorch, TensorFlow, and TFLite formats, and the stack saw use in products and research projects across more than 20 languages. Training expects a GPU while inference can run on CPU. The repository is licensed under MPL 2.0, but it is no longer where active development happens: the core team continued the codebase as Coqui TTS after leaving Mozilla. It remains relevant as a well-documented historical codebase for studying classic neural TTS architectures and for projects still pinned to its released models.
Should you use Mozilla TTS?
Pick it when
Pick Mozilla TTS when you are studying classic Tacotron and neural vocoder architectures from a well documented codebase, or you maintain a project pinned to its released PyTorch, TensorFlow, or TFLite models.
Look elsewhere when
Skip it for new work: development moved to Coqui TTS when the core team left Mozilla, so fixes and newer models land there. For production speech, Piper or Kokoro are far simpler to deploy than this training framework.
Alternatives to Mozilla TTS
- Coqui TTS
Direct continuation by the same core team with the Tacotron and Glow-TTS lineage plus VITS, so it carries fixes this frozen repo does not get.
- VITS
Single-stage MIT model that produced more natural audio than the two-stage spectrogram plus vocoder designs Mozilla TTS centers on, but one architecture, not a framework.
- Piper TTS
Ready-to-use offline voices in dozens of languages on a Raspberry Pi, for when you want speech output rather than a training framework.
- Matcha-TTS
Modern MIT flow matching model with pip install, training scripts, and ONNX export, a lighter research codebase than Mozilla's legacy stack.
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Details
- Category
- Text-to-Speech (TTS)
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Intermediate (3/5)
- License
- MPL-2.0
- Minimum VRAM
- 4 GB
- Added
- Apr 3, 2026