Detailed Introduction
IMS Toucan is an open-source text-to-speech (TTS) toolkit developed by the Institute for Natural Language Processing (IMS) at the University of Stuttgart. It supports synthesis for over 7,000 languages and targets both research and engineering use cases. The project provides training and fine-tuning pipelines, inference interfaces, and pretrained models. Designed for controllability and speed, IMS Toucan emphasizes high-quality multilingual synthesis under constrained compute and offers an online demo for quick evaluation.
Main Features
- Multilingual coverage: Support for training and synthesis across 7000+ languages, leveraging language embeddings and meta-learning techniques.
- Controllability: Speaker embeddings, emotion and prosody control, including exact prosody-cloning capabilities.
- Performance optimisations: Engineering work to enable efficient inference on limited GPU resources.
- Open-source ecosystem: Apache-2.0 licensed code, models and datasets available via GitHub and Hugging Face.
Use Cases
IMS Toucan is suitable for research experiments, multilingual speech services, and rapid prototyping for low-resource languages. Typical applications include academic research, speech assistants, cross-language voice experience testing, and voice cloning tasks that require fine-grained prosody control.
Technical Features
The toolkit integrates modern neural TTS architectures with language and speaker embeddings, combining meta-learning and data engineering to scale to thousands of languages. It provides complete training pipelines, inference interfaces, and example scripts, and leverages the Hugging Face ecosystem for model distribution and online demos. For the demo and dataset links, see the README and the Hugging Face demo: MassivelyMultilingualTTS Demo .