Elphi Türkçe — EMA Lightning for Core ML

Elphi Türkçe is the Core ML distribution of EMA Lightning for the Elphi app. It lets Elphi read Turkish answers aloud on iPhone, iPad and Mac entirely on the device: once downloaded it works offline, and no text or audio is sent anywhere.

We did not train the underlying text-to-speech model. EMA Lightning was created by Canberk Aslan. This repository holds his published weights converted to Core ML for Apple devices — nothing more. The Core ML conversion, packaging and validation were done by Bosphorus Intelligence LLC.

This distribution is not affiliated with, sponsored by or endorsed by the author of EMA Lightning.

The original model

Model	EMA Lightning by Canberk Aslan
Hugging Face	canberkkkkkk/ema-lightning
GitHub	canberk7/ema-lightning
Weights converted	canberkkkkkk/ema-lightning at revision 7a6ba1ad216bb2f1da9863f80ac8770a6a807632 (ema.pt SHA-256 95aec03dafbe0e1d69bca774ab597c779464729a14bc99bfcb52480090c7dfe6, decoder.pt SHA-256 9595819b173f411340f63d11332695121a97f8bf1f6d8b6fef0b21cf99c7ad67)
Code followed	canberk7/ema-lightning at commit 12797c5f4dbcced8e4b3392b12bba2fe8b9f5ec6 (v1.0.4)
License	Apache License 2.0, for the weights and the code (see LICENSE)

How the model was designed and trained is described in the original model card; this repository makes no claims beyond it.

Files

File	Bytes	SHA-256
EMADecoder.mlpackage/Data/com.apple.CoreML/model.mlmodel	171,303	a952147fbfe0bd43e878a61eae3d8718e2307dcf8475664e8d75c06ca2883ce5
EMADecoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin	11,984,304	ed8e4a1701ae8b7b2c1298db38ce34d83fbf107f13f82b77281c1a87f6fcbab9
EMADecoder.mlpackage/Manifest.json	617	bbf2a573a9fc80dddc5d80f03e9a35d33b6d2a291dfa741aa1fd6bd8290b1acd
EMASound.mlpackage/Data/com.apple.CoreML/model.mlmodel	275,635	b38839114c7b430312881e27d007f955273697ede50739d2e8fcfed90e965b65
EMASound.mlpackage/Data/com.apple.CoreML/weights/weight.bin	15,319,296	3081db93ad88e7ed0fbe3864e66c3e0cbd8723adac88e0eada64a41ebe92b827
EMASound.mlpackage/Manifest.json	617	e558217deb6ec55e3aaf91f587fc0743e1596c257db76de43797b4f7e951d872
EMAText.mlpackage/Data/com.apple.CoreML/model.mlmodel	29,210	24ec2f1acc76eb34fa8979e34e2b29d6fb668533794050957e251ac8e2b3b1ee
EMAText.mlpackage/Data/com.apple.CoreML/weights/weight.bin	4,792,000	f2ec3166e629929b159fbe12b06cb445a202a5a16e35e78b3b5c30dbefa60db3
EMAText.mlpackage/Manifest.json	617	94ca43b45ecdbefc8d250ded9b325132ecb241ce4df42c5d17ee4c67003b755b
voice.json	1,443	fba9baf368a1176f9b43d9c37c56a8497684c0d0c14c4ef074732523f14300d8

32,575,042 bytes in all.

• EMAText.mlpackage — the text stage: letter ids → letter features and per-letter durations.
• EMASound.mlpackage — the sound stage: the aligner and the four flow-matching steps → 64-dimensional latents at 25 Hz.
• EMADecoder.mlpackage — the original decoder: latents → 48 kHz mono audio.
• voice.json — the alphabet, the constants and the pinned sources.

Core ML ML Programs with float32 weights, for iOS, iPadOS and macOS 26 and later; Elphi runs them on the CPU.

Changes from the original

As the Apache License 2.0 asks, the changes made to the original:

• The acoustic model was split into a text stage and a sound stage; the decoder is unchanged. The index math the original computes inside its forward pass (word timeline, letter and frame positions, the aligner's window) is computed by the app, exactly as the original computes it.
• The stages were traced with PyTorch 2.7.0 and converted with coremltools 9.0 to ML Programs, then packaged deterministically (the same bytes on every build).
• The weights' values are unchanged: no retraining, no fine-tuning, no quantization.

Validation

Against the original PyTorch model on the CPU, with identical noise, on 12 reference pieces: identical frame timelines, and audio within 48.0–67.4 dB SNR of the original (median 61.3 dB) — rounding-level differences. On an iPhone 11 (A13): first audio in 60–80 ms, speech made about 20× faster than it plays.

How Elphi uses it

Elphi downloads these files only when the person asks for Elphi Türkçe, checks every file against its pinned size and SHA-256, compiles the models on the device and keeps them there. The download sends no text. Text normalization (numbers, dates, amounts) uses normalizer-tr 0.4.0 by Erdem Tuna (Apache License 2.0), which ships inside the app and is not part of this repository.

Responsible use

EMA Lightning makes synthetic speech, and — as the original model card asks — people who hear it should know that. Elphi presents Elphi Türkçe as an AI-generated voice. Do not present it as a real person's voice or use it for impersonation or scams. Numbers, dates and abbreviations go through automatic normalization, which can misread unusual input.

License and attribution

Apache License 2.0 — LICENSE is the original's license text; NOTICE carries the attribution. Provided "as is", without warranty of any kind.

Citation of the original model

@misc{aslan2026emalightning,
  title        = {EMA Lightning: Tiny, Fast and Accurate Turkish Text to Speech},
  author       = {Aslan, Canberk},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/canberkkkkkk/ema-lightning}}
}