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Azure Ameha Neural

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Azure Ameha Neural is a closed-weights text-to-speech model from Microsoft, released October 2021.

Catalogue identity notes

Exact model am-ET-AmehaNeural; voice am-ET-AmehaNeural. Dataset label Azure Ameha Neural. No API immutable build/weight SHA supplied.

Model information sources

Release date ↗

50.4±15.8comparison only: no current run option verifiedfrom 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 Azure Ameha Neural, out of 148 with a score, then more from Microsoft.

#modelscorewhereRAMreleased
79OpenVoice V1 MyShell51±14local~0.3GB2024-01
80Eleven Multilingual v2 ElevenLabs51±5api $100.00/M chars2023-08
81Azure Ameha Neural Microsoft50±16comparison only2021-10
82Azure Mekdes Neural Microsoft50±16comparison only2021-10
83Addis Voice 2 Addis AI50±16comparison only
more from Microsoft
70VibeVoice 1.5B Microsoft53±6local~5.4GB2025-08
140SpeechT5 TTS Microsoft23±3local~0.7GB2023-02

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

$ what it's good at

other tests1 not in a task · 0.2% of its score
testresultvs the others in its rundifficultycountswhy
Amharic TTS · 87-prompt CER (Chirp 3 judge)3.0% ✎▄·····▄·▄·██ beats 5/6avg 8.7%▇ 0.2%blunt · noisy

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Addis AI Amharic TTS benchmark 0%

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
Addis AI Amharic TTS benchmark1▇ 0%CC BY-SA 4.0
prior · pull toward the middle▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%
weighted average= 50.4

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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