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ema-lightning

Tiny, fast and accurate Turkish text-to-speech. 8.6M parameters, 0.92% WER on Freya-TR-Eval, first audio in ~4 ms and 1,300× real time on one GPU. Streams audio, batches many callers on one GPU, and runs offline on a GPU or CPU.

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This is 1 of 174 launches in voice AI models and infrastructure — see how it stacks up on momentum and crowding →

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Details

External ID
1406339660
Source
GITHUB
Company
—
Product
ema-lightning
Website domain
github.com
Launched
Oct. 5, 2026
Cohort
—
Upvotes
145
Upvotes percentile
0.9149519890260631
Tags
—
Fetched at
Oct. 8, 2026, 5:02 p.m.
Updated at
Oct. 8, 2026, 5:02 p.m.

Enrichment

Niche
voice AI models and infrastructure
Vertical
Media & entertainment
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
fast turkish text-to-speech model for developers
Manually corrected
False

Could you build this?

No Training and deploying an ultra-compact (8.6M parameter), high-accuracy, 1300x real-time streaming TTS neural network specifically for Turkish requires specialized deep learning audio research and custom model architecture design.

What it would actually take: Requires deep ML audio research using PyTorch, custom acoustic and vocoder architectures (such as lightweight VITS or diffusion/flow-matching variants), a high-quality annotated Turkish phoneme speech corpus, and specialized TensorRT/ONNX runtime optimization to achieve sub-4ms time-to-first-audio and high RTF throughput.

Competitors

Other products that read as similar to this one — 114 launches clear the similarity bar, closest 8 shown.

Attention rank: #15 of 115 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 274 days after the earliest competitor.

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