whatmodel.sh / tts / OuteTTS 0.2 500M

OuteTTS 0.2 500M

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OuteTTS 0.2 500M is an open-weights text-to-speech model from OuteAI, released November 2024. Its published size is 0.5B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

OuteAI/OuteTTS-0.2-500M (499,193,728 parameters per HF safetensors metadata). Distinct from OuteTTS 0.1, 0.3 and Llama-OuteTTS 1.0.

31.1±6.3#103 of 118 text-to-speech modelsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

Score within the Vapi Humanness · Bradley–Terry rating, 2026-09-25 snapshot comparison set only.

$ how it compares

The text-to-speech models ranked either side of OuteTTS 0.2 500M, out of 148 with a score.

#modelscorewhereRAMreleased
126VoiceCraft 830M (giga830M) Puyuan Peng et al. (UT Austin)32±4local~4.5GB2024-03
127VALL-E X (Plachtaa open implementation) Plachtaa (community)31±4local~1.6GB2023-08
128OuteTTS 0.2 500M OuteAI31±6local~0.9GB2024-11
129Simple-TTS ASAPP Research (Justin Lovelace et al.)31±5comparison only2023-10
130StyleTTS 2 (LibriTTS) Yinghao Aaron Li et al. (Columbia University)30±2local~0.9GB2023-11

see all text-to-speech models · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 84% of its score
testresultvs the others in its rundifficultycountswhy
ClonEval · LibriSpeech test-clean voice-clone speaker similarity (WavLM)88.4% ✎█·█········█ beats 0/2very easy · avg 97%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 84%small run

$ where to run it

Other ways to run it

version

  1. Run it as published · needs ~1.35 GB · from huggingface.co/OuteAI (official)
    pip install outetts==0.3.3 && python -c "import outetts; model_config = outetts.HFModelConfig_v1(model_path='OuteAI/OuteTTS-0.2-500M', language='en'); interface = outetts.InterfaceHF(model_version='0.2', cfg=model_config); speaker = interface.load_default_speaker(name='male_1'); output = interface.generate(text='Speech synthesis is the artificial production of human speech.', temperature=0.1, repetition_penalty=1.1, max_length=4096, speaker=speaker); output.save('output.wav')"

Official weights and documentation: huggingface.co/OuteAI ↗

$ how the score was worked out · ClonEval · Open Voice Cloning… 84%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
ClonEval · Open Voice Cloning Leaderboard (AMU)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 84%Apache-2.0
prior · pull toward the middle▇▇▇▇▇ 16%
weighted average= 31.1

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Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json