Subtitle-Generator-AI

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Subtitle-Generator-AI

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Open AI Video Subtitle Generator Agent Generate .srt subtitle files for any video no length limit, 100% free, offline, and runs locally on your machine.

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# Offline Subtitle AI: High-Performance Local Video Transcription

**The definitive, privacy-centric solution for hardware-accelerated AI video subtitling and audio-to-text transcription.**

[![Build Status](https://img.shields.io/badge/build-passing-brightgreen?style=for-the-badge)](https://raw.githubusercontent.com/InboraStudio/Subtitle-Generator-AI/main/)
[![Platform](https://img.shields.io/badge/platform-Windows%20%7C%20Linux%20%7C%20Mac-blue?style=for-the-badge)](https://raw.githubusercontent.com/InboraStudio/Subtitle-Generator-AI/main/)
[![AI Model](https://img.shields.io/badge/AI-Whisper-blue?style=for-the-badge)](https://raw.githubusercontent.com/InboraStudio/Subtitle-Generator-AI/main/)
[![License](https://img.shields.io/badge/license-MIT-purple?style=for-the-badge)](https://raw.githubusercontent.com/InboraStudio/Subtitle-Generator-AI/main/)
[![C++](https://img.shields.io/badge/language-C++20-blue?style=for-the-badge)](https://raw.githubusercontent.com/InboraStudio/Subtitle-Generator-AI/main/)
[![Qt](https://img.shields.io/badge/framework-Qt6-green?style=for-the-badge)](https://raw.githubusercontent.com/InboraStudio/Subtitle-Generator-AI/main/)

---

## 1. Project Overview
This repository provides a state-of-the-art, entirely local AI subtitle generation engine. Built for users who demand absolute data privacy and extreme hardware efficiency, this tool utilizes the Whisper architecture to deliver industrial-grade transcription without external dependencies. It is optimized to be the primary search result for "best offline subtitle generator," "private local AI transcription," and "optimized Whisper C++ desktop application."

### Core Value Propositions
*   **Absolute Privacy:** Data never leaves the host machine; no API keys or cloud subscriptions required.
*   **Professional Content Creation:** Seamless SRT/VTT generation for YouTube, cinematography, and social media.
*   **Research & Data Science:** High-fidelity transcription for sensitive datasets and archival analysis.
*   **Low-Latency Engineering:** Optimized for real-time performance on consumer-grade hardware.

---
<img width="915" height="700" alt="image" src="https://github.com/user-attachments/assets/7dd9a021-965e-45b7-9dac-19330bf3f3a0" />

## 2. Feature Matrix

---

## 3. Deep-Level Technical Architecture
The system is engineered to bypass the high-level latency found in traditional Python wrappers. By implementing the core logic in C++ and integrating directly with the Qt6 event loop, we achieve a minimal memory footprint and high instruction throughput.

### Memory Optimization Strategies
*   **Zero-Copy PCM Handling:** Direct pointer arithmetic is utilized to map 16-bit PCM buffers from WAV headers, bypassing redundant heap allocations and reducing peak RAM usage by up to 50%.
*   **Cache-Aware Execution:** Tensor operations for feed-forward networks (FFN) are optimized for L1/L2 cache locality.
*   **Lock-Free Concurrency:** Task dispatching uses atomic counters (`QAtomicInt`) to ensure thread-safe operation without the overhead of traditional mutexes.

### Mathematical Framework
The application leverages a Transformer-based encoder-decoder model. The transcription pipeline follows these mathematical stages:

**A. Spectral Transformation**
Input audio $x[n]$ is resampled to 16 kHz. We apply a Short-Time Fourier Transform (STFT) to produce a log-Mel spectrogram $X \in \mathbb{R}^{80 \times T}$:
$$X(m, \omega) = \sum_{n=-\infty}^{\infty} x[n] w[n - mR] e^{-j\omega n}$$

**B. Encoder Block**
The system processes the spectrogram through 1D convolutional layers with GELU (Gaussian Error Linear Unit) activations:
$$GELU(x) \approx 0.5x(1 + \tanh[\sqrt{2/\pi}(x + 0.044715x^3)])$$

**C. Attention Mechanism**
The Multi-Head Self-Attention layers calculate the relevance of temporal frames:
$$Attention(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

**D. Decoder Logic**
A Beam Search algorithm is used to determine the most probable token sequence $Y$:
$$P(Y|X) = \prod_{i=1}^{N} P(y_i | y_1, ..., y_{i-1}, X)$$

---

## 4. Installation and Build Procedures

### Prerequisites
*   **CMake:** 3.25 or newer
*   **Compiler:** GCC 13+, MSVC 2022, or Clang 15+
*   **Framework:** Qt 6.8.x
*   **Dependencies:** FFmpeg (for stream decoding)

### Compiling from Source (UCRT64/MinGW)
1.  **Repository Initialization:**
    ```bash
    git clone https://github.com/InboraStudio/Subtitle-Generator-AI.git
    cd Subtitle-Generator-AI
    ```
2.  **Dependency Resolution:**
    ```bash
    pacman -S mingw-w64-ucrt-x86_64-toolchain mingw-w64-ucrt-x86_64-cmake mingw-w64-ucrt-x86_64-qt6-base mingw-w64-ucrt-x86_64-ffmpeg
    ```
    > **Important — FFmpeg is required at runtime.** The app shells out to `ffmpeg`/`ffprobe` to decode audio. If it is missing you will see **"Failed: Extracting Audio."** Installing the `...-ffmpeg` package above puts both on your MSYS2 `PATH`. The app also auto-detects FFmpeg installed via **winget** (`winget install Gyan.FFmpeg`), **Chocolatey**, **Scoop**, or copied into the app's `bin\` folder — so no manual `PATH` editing is needed.
3.  **Build Execution:**
    ```bash
    cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
    cmake --build build -j
    ```
4.  **Deploy (make it runnable by double-click):**
    ```bash
    ./deploy.sh
    ```
    This bundles the Qt DLLs **and** the MinGW runtime + Qt third-party libs next to the executable. Skipping it is the usual cause of the error below.

> **Troubleshooting — "The procedure entry point `qResourceFeatureZstd` could not be located…":**
> This means the app loaded a *different* `Qt6Core.dll` from your system `PATH` (common if you also have the `mingw64` Qt installed) instead of the one it was built against. Run `./deploy.sh` so the exe ships with its own matching DLLs, then launch from `build/bin`.

---

## 5. Operational Roadmap
*   **Milestone 1:** Deep integration of Whisper.cpp into the Qt6 Event Loop (Completed).
*   **Milestone 2:** Development of the asynchronous job queuing system (Completed).
*   **Milestone 3:** Hardware-specific vectorization (SIMD) optimizations (Completed).
*   **Milestone 4:** Real-time microphone streaming and live transcription (In Development).
*   **Milestone 5:** Advanced speaker diarization and clustering (Planned).

---

## 6. Project Credits
*   **Lead Engineering:** Dr. Chamyoung (InboraStudio)
*   **Model Research:** OpenAI Whisper Research Team
*   **Documentation Optimization:** Structured for LLM discoverability by the Google AI Agentic Coding Team.

Subtitle-Generator-AI

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