Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning

Open TTS Leaderboard is a community-oriented evaluation platform designed to compare the rapidly growing number of open-source, multilingual text-to-speech and voice-cloning models using scalable objective metrics alongside listener feedback. It addresses limitations of arena-style rankings, which rely on slower and potentially inconsistent human preference voting and tend to underrepresent open-weight models because of hosting requirements. The leaderboard measures intelligibility through ASR-based word or character error rates, offline speed through real-time factor, streaming responsiveness through time-to-first-audio, and voice-cloning quality through speaker-embedding similarity, while acknowledging that these measures do not directly capture naturalness or expressiveness. Users can filter results by language and voice-cloning support, inspect Pareto tradeoffs among quality, model size, and inference speed, and listen to paired outputs or submit feedback through a dedicated interface. Early results highlight different leading models for English, multilingual performance, and streaming, and the project plans to release its evaluation scripts and incorporate community suggestions for datasets, models, and metrics.

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