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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+10
STARS · LAST 30 DAYS
1
PER DAY
#1,807
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
ema-lightning gained 10 stars in the last 30 days, about 1 a day, and now has 109. It is about 1 year old and has averaged roughly 109 stars a year. It ranks #1,807 among Python repositories and #14,106 across all languages on GitHubRepo.
Trending Record
4
DAYS ON TRENDING
#12523
BEST RANK
Oct 7, 2026
FIRST APPEARANCE
Active
STATUS TODAY
ema-lightning has maintained a continuous presence across global trending indexes, peaking at #12523. Below is the 30-day activity profile:
💡 Overview
ema-lightning is an open-source project written in Python: 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.
Engineered for speed, consistency, and developer ease, it solves common hurdles in Python. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install ema-lightning⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
ema-lightning coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed ema-lightning into Python backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.
🚀 Getting Started
Install ema-lightning using your package manager: `pip install ema-lightning`
Initialize your project workspace or configuration file for ema-lightning.
Import ema-lightning into your codebase or invoke it directly from your terminal.
Execute your test suite or run `ema-lightning --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
canberk7/ema-lightning is currently ranked #14,106 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #14,061 | supermax92/qgraphflow | JavaScript | ★ 110 | Compare ↗ |
| #14,061 | NousResearch/hermes-plugin-claude-subscription-directsdk | Python | ★ 110 | Compare ↗ |
| #14,061 | phprs-cms/kaletacms | PHP | ★ 110 | Compare ↗ |
| #14,061 | shhivv/biome-s1 | Python | ★ 110 | Compare ↗ |
| #14,061 | wieslawsoltes/VB6 | JavaScript | ★ 110 | Compare ↗ |
| #14,106 | canberk7/ema-lightning This Project | Python | ★ 109 | |
| #14,106 | telmich/gpm | C | ★ 109 | Compare ↗ |
| #14,106 | sjlombardo/acts_as_network | Ruby | ★ 109 | Compare ↗ |
| #14,106 | robbyrussell/shorturl | Ruby | ★ 109 | Compare ↗ |
| #14,106 | nicksieger/jsonpretty | Go | ★ 109 | Compare ↗ |
| #14,106 | vidageek/mirror | Java | ★ 109 | Compare ↗ |
Frequently Asked Questions
What does ema-lightning do? +
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.
What language is ema-lightning written in? +
The primary language is Python. Topics include: software.
Is ema-lightning actively maintained? +
Yes, the last recorded push was on Oct 7, 2026 with 1 open issues being tracked.
How many stars does ema-lightning have? +
ema-lightning has 109 stars and 17 forks on GitHub.
How does ema-lightning rank among GitHub repositories? +
With 109 stars, canberk7/ema-lightning is ranked #14,106 globally across all repositories tracked on GitHubRepo and #1,807 among Python projects.
What license is ema-lightning distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
