whatmodel.sh / tts / Trendyol-TTS

Trendyol-TTS is an open-weights text-to-speech model from Trendyol, released June 2026. Its published size is 2.38B parameters. Licence: mit.

Catalogue identity notes

Trendyol's Turkish TTS research model built on openbmb/VoxCPM2 (merged LoRA checkpoint step_0002000; Trendyol/Trendyol-TTS, pinned 66a8018, MIT). The Antalia re-run harness loads VoxCPM.from_pretrained(hf_model_id='Trendyol/Trendyol-TTS') with cfg 2.0 and 16 inference steps; the EMA card lists 'Trendyol-TTS 2.38B'.

Model information sources

Release date ↗ · Weights licence ↗

2.38B as printed in both evaluators' tables (HF safetensors metadata reports 2.29B for the repo's safetensors).

39.3±2.9comparison only: no current run option verifiedfrom 2 benchmark results across 2 sources* 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 Trendyol-TTS, out of 148 with a score.

#modelscorewhereRAMreleased
110Antalia-2 Mini Patientdesk.ai40±4comparison only2026-10
111HierSpeech++ v1.1 Korea University (Sang-Hoon Lee et al.)39±3local~2.1GB2023-11
112Trendyol-TTS Trendyol39±3comparison only2026-06
113Magpie TTS Multilingual 357M NVIDIA39±6local~0.7GB2026-07
114Zonos v0.1 Zyphra39±4api price unknown +local~4.3GB2025-02

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

$ what it's good at

other tests1 not in a task · 97% of its score
testresultvs the others in its rundifficultycountswhy
Freya-TR-Eval Turkish TTS intelligibility (WER, faster-whisper large-v3, 8 kHz band) +11.2% ✎▄··▄··▄···▂█ beats 8/10avg 0.3%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%several runs

$ where to run it

No verified API or download recorded yet · add one

Official weights and documentation: huggingface.co/Trendyol ↗

$ how the score was worked out · Antalia-2 Mini model card (cl… 53%, EMA Lightning model card (can… 43%

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
Antalia-2 Mini model card (cloud0day3/antalia-mini), public Freya-TR-Eval re-run table1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%Apache-2.0
EMA Lightning model card (canberkkkkkk/ema-lightning), Benchmarks: Freya-TR-Eval1▇▇▇▇▇▇▇▇▇▇▇▇▇ 43%Apache-2.0
prior · pull toward the middle▇ 3%
weighted average= 39.3

$ improve this page

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