Introducing Monlam TTS: Giving Tibetan a Digital Voice

Monlam TTS is the state-of-the-art Tibetan speech generation model developed by the Monlam AI team, designed to transform written Tibetan text into natural and expressive speech.

Introducing Monlam TTS

Humans communicate primarily through speech. However, the Tibetan language still lacks many of the robust speech technologies available for high-resource languages. Building AI systems that can fully interact in Tibetan requires two core capabilities: speech recognition and speech synthesis. With Monlam STT enabling AI systems to understand Tibetan speech, Monlam TTS completes the other half of the interaction by enabling AI to speak Tibetan. Together, these technologies represent an important step towards creating complete Tibetan voice-based AI systems.

We developed Monlam Text-to-Speech (TTS), a Tibetan speech generation model that converts Tibetan text into natural speech, making Tibetan digital content more accessible for education, communication, and future AI applications. The system is built specifically for the Tibetan language and produces realistic speech in over 9 voices with dialect variations. Thanks to a robust pipeline, the model handles long texts gracefully and consistently. The generated speech features accurate pronunciation and smooth rhythm that closely resembles human speech.

གལ་སྲིད་དུས་ཚོད་ཡོད་ན། ང་ཚོ་མཉམ་དུ་ཇ་ཁང་ལ་ཕྱིན་ནས་ལས་ཀའི་སྐོར་གོ་བསྡུར་ཞིག་བྱས་ན་བསམ་གྱིན་འདུག ཧ་ཧ་ཧ་ ཁྱེད་རང་ག་དུས་ལྷོད་ལྷོད་ཆགས་ན་ང་ལ་ལན་ཞིག་གནང་རོགས།

Why Tibetan Text-to-Speech is challenging

Building a mature Tibetan text-to-speech system comes with several distinct challenges. Unlike high-resource languages, Tibetan has limited publicly available speech resources for training modern AI models. In addition, variations across Tibetan dialects makes it difficult to achieve accurate pronunciation and natural-sounding speech.

Another major challenge is generating speech that captures not only correct pronunciation, but also rhythm, pacing, and natural expression, creating voices that are pleasant to listen to. Finally, there is very little prior work on Tibetan speech generation to use as a reference.

To address these challenges, we created comprehensive pipelines for corpus collection and creation of the training data. We adapted latest speech generation techniques for Tibetan and developed a custom tokenizer for Tibetan text.

Building the Model

Robust AI systems are built on high-quality data. To develop Monlam TTS, we trained the model on more than 2,400 hours of carefully prepared Tibetan speech data. For a low-resource language like Tibetan, assembling a dataset of this scale required extensive collection, processing, and quality verification by dedicated teams.

Experience Monlam TTS

བཀྲ་ཤིས་བདེ་ལེགས། ང་ད་ལྟ་ལས་ཁུངས་ལ་འབྱོར་སོང་། ཁྱེད་རང་དེ་རིང་ལས་ཀ་བྲེལ་བ་ཚ་པོ་ཡོད་པེ།

ཨ་མདོ། མོ། ༡

དབུས་གཙང་། ཕོ། ༡

དབུས་གཙང་། མོ། ༡

Applications and future vision

Monlam TTS’s ability to generate natural Tibetan speech makes the language more accessible than ever. People can now experience Tibetan digital content not only through reading but also by listening. This creates new possibilities, ranging from audiobooks of Tibetan literature to personal AI assistants with speech-to-speech capabilities.

We are actively working on the next steps. Monlam TTS will soon be available via API on our upcoming Monlam AI Studio, along with our other models. We hope this will enable its integration into independent projects dedicated to preserving the Tibetan language and culture. We are refining the model to improve emotional expression in the generated speech and to add more regional dialects as new voices.

The current capability of the Monlam TTS represents a major step in our goal of digital inclusion and preservation of the Tibetan language through AI. Together with the Monlam STT model, this lets us build a complete interactive Tibetan speech system and lays the groundwork for the future of the language in AI and other emerging technologies. We hope this technology will make Tibetan more accessible to everyone, from new learners to fluent speakers who wish to engage with the language in digital environments.