📐 The 🗣️ Open TTS Leaderboard benchmarks open-source text-to-speech models on intelligibility (WER), speed (RTFx, TTFA), naturalness (UTMOS), and speaker similarity (SIM).
Every model is scored on the same texts across three multilingual eval sets (Seed-TTS eval, CV3-Eval and MiniMax's TTS-Multilingual-Test-Set) for the language it supports, both without voice cloning (the model's own default voice) and — where the model supports it — with voice cloning from a reference clip.
⚠️ TO REQUEST A MODEL/DATASET/METRIC: open a pull request or issue on the GitHub repo! See here for adding a new model.
🏅 Leaderboard
Averages across the selected languages and datasets. A model is ranked, with a headline average, only if it has results for every selected language on every selected dataset, so all ranked models are compared on the same set.
If batch inference is supported, the model is evaluated with batch size 32; otherwise, it is evaluated at batch size 1. See the 🗣️ Streaming tab for single-request, streaming-mode latency instead.
Evals are run with HF jobs, flavor h200 (unless noted otherwise): 1x H200 (141 GB), 23 vCPU, 256 GB RAM, 3000 GB storage
See the 🤗 About tab for metric definitions (WER, RTFx, SIM, TTFA, UTMOS, KOMOS).
📊 Pareto frontiers
🏆 Top models
Last updated on 3 November 2026
Pick a past snapshot to see the leaderboard as it stood then.