Feat: Integrated Local LLM (Llama 3.2 1B) for Intelligent Correction -- New Core: Added LLMEngine utilizing llama-cpp-python for local private text post-processing. -- Forensic Protocol: Engineered strict system prompts to prevent LLM refusals, censorship, or assistant chatter. -- Three Modes: Grammar, Standard, Rewrite. -- Start/Stop Logic: Consolidated conflicting recording methods. -- Hotkeys: Added dedicated F9 (Correct) vs F8 (Transcribe). -- UI: Updated Settings. -- Build: Updated portable_build.py. -- Docs: Updated README.

- Added robust CPU Fallback for AMD/Non-CUDA GPUs.
- Implemented Lazy Load for AI Engine to prevent startup crashes.
- Added explicit DLL injection for Cublas/Cudnn on Windows.
- Added Corrupt Model Auto-Repair logic.
- Includes pre-compiled v1.0.4 executable.

README.md

@@ -1,71 +1,196 @@
-# Whisper Voice
+<div align="center">
 
-**Reclaim Your Voice from the Cloud.**
+# 🎙️ W H I S P E R &nbsp; V O I C E
+### SOVEREIGN SPEECH RECOGNITION
 
-Whisper Voice is a high-performance, strictly local speech-to-text tool designed for the desktop. It provides instant, high-accuracy dictation anywhere on your system—no internet connection required, no corporate servers, and absolutely no data harvesting.
+<br>
 
-We believe that the tools of production—and communication—should belong to the individual, not rented from centralized tech giants.
+![Status](https://img.shields.io/badge/STATUS-OPERATIONAL-success?style=for-the-badge&logo=server&color=2ecc71)
+[![Download](https://img.shields.io/gitea/v/release/lashman/whisper_voice?gitea_url=https%3A%2F%2Fgit.lashman.live&label=Install&style=for-the-badge&logo=windows&logoColor=white&color=3b82f6)](https://git.lashman.live/lashman/whisper_voice/releases/latest)
+[![License](https://img.shields.io/badge/LICENSE-PUBLIC_DOMAIN-lightgrey?style=for-the-badge&logo=creative-commons&logoColor=black)](https://creativecommons.org/publicdomain/zero/1.0/)
+
+<br>
+
+> *"The master's tools will never dismantle the master's house."*
+> <br>
+> **Build your own tools. Run them locally. Free your mind.**
+
+[View Source](https://git.lashman.live/lashman/whisper_voice) • [Report Issue](https://git.lashman.live/lashman/whisper_voice/issues)
+
+</div>
+
+<br>
+<br>
+
+## 📡 The Transmission
+
+We are witnessing the **enshittification** of the digital world. What were once vibrant social commons are being walled off, strip-mined for data, and degraded into rent-seeking silos. Your voice is no longer your own; it is a training set for a corporate oracle that charges you for the privilege of listening.
+
+**Whisper Voice** is a small act of sabotage against this trend.
+
+It is built on the axiom of **Technological Sovereignty**. By moving state-of-the-art inference from the server farms to your own silicon, you reclaim the means of digital production. No telemetry. No subscriptions. No "cloud processing" that eavesdrops on your intent. 
 
 ---
 
-## ✊ Core Principles
+## ⚡ The Engine
 
-### 1. Total Autonomy (Local-First)
+Whisper Voice operates directly on the metal. It is not an API wrapper; it is an autonomous machine.
-Your voice data is yours alone. Unlike commercial alternatives that siphon your words to remote data centers for processing and profiling, Whisper Voice runs entirely on your hardware. **No masters, no servers.** You retain full sovereignty over your digital footprint.
 
-### 2. Decentralized Power
+| Component | Technology | Benefit |
-By leveraging optimized local processing, we strip away the need for reliance on massive, energy-hungry corporate infrastructure. This is technology scaled to the human level—powerful, efficient, and completely under your control.
+| :--- | :--- | :--- |
+| **Inference Core** | **Faster-Whisper** | Hyper-optimized C++ implementation via **CTranslate2**. Delivers **4x velocity** over standard PyTorch. |
+| **Compression** | **INT8 quantization** | Enables Pro-grade models (`Large-v3`) to run on consumer-grade GPUs, democratizing elite AI. |
+| **Sensory Gate** | **Silero VAD** | Enterprise-grade Voice Activity Detection filters out the noise, ensuring only pure intent is processed. |
+| **Interface** | **Qt 6 / QML** | Hardware-accelerated, glassmorphic UI that is fluid, responsive, and sovereign. |
 
-### 3. Accessible to All
+### 🛑 Compatibility Matrix (Windows)
-High-quality speech recognition shouldn't be gated behind subscriptions or paywalls. This tool is free, open, and built to empower users to interact with their machines on their own terms.
+The core engine (`CTranslate2`) is heavily optimized for Nvidia tensor cores.
+
+| Manufacturer | Hardware | Status | Notes |
+| :--- | :--- | :--- | :--- |
+| **Nvidia** | GTX 900+ / RTX | ✅ **Supported** | Full heavy-metal acceleration. |
+| **AMD** | Radeon RX | ⚠️ **CPU Fallback** | Runs on CPU. Valid for `Small/Medium`, slow for `Large`. |
+| **Intel** | Arc / Iris | ⚠️ **CPU Fallback** | Runs on CPU. Valid for `Small/Medium`, slow for `Large`. |
+| **Apple** | M1 / M2 / M3 | ❌ **Unsupported** | Release is strictly Windows x64. |
+
+> **AMD Users**: v1.0.3 auto-detects GPU failures and silently falls back to CPU.
+
+<br>
+
+## 🖋️ Universal Transcription
+
+At its core, Whisper Voice is the ultimate bridge between thought and text. It listens with superhuman precision, converting spoken word into written form across **99 languages**.
+
+*   **Punctuation Mastery**: Automatically handles capitalization and complex punctuation formatting.
+*   **Contextual Intelligence**: Smarter than standard dictation; it understands the flow of sentences to resolve homophones and technical jargon ($1.5k vs "fifteen hundred dollars").
+*   **Total Privacy**: Your private dictation, legal notes, or creative writing never leave your RAM.
+
+### Workflow: `F9 (Default)`
+The primary channel for native-language transcription. It transcribes precisely what it hears in the language you speak (or the one you've locked in Settings).
+
+### 🧠 Intelligent Correction (New in v1.1.0)
+Whisper Voice now integrates a local **Llama 3.2 1B** LLM to act as a "Silent Consultant". It post-processes transcripts to fix grammar or polish style without effectively "chatting" back. 
+
+It is strictly trained on a **Forensic Protocol**: it will never lecture you, never refuse to process explicit language, and never sanitize your words. Your profanity is yours to keep.
+
+#### Correction Modes:
+*   **Standard (Default)**: Fixes grammar, punctuation, and capitalization while keeping every word you said.
+*   **Grammar Only**: Strictly fixes objective errors (spelling/agreement). Touches nothing else.
+*   **Rewrite**: Polishes the flow and clarity of your sentences while explicitly preserving your original tone (Casual stays casual, Formal stays formal).
+
+#### Supported Languages:
+The correction engine is optimized for **English, German, French, Italian, Portuguese, Spanish, Hindi, and Thai**. It also performs well on **Russian, Chinese, Japanese, and Romanian**.
+
+This approach incurs a ~2s latency penalty but uses **zero extra VRAM** when in Low VRAM mode.
+
+<br>
+
+## 🌎 Universal Translation
+
+Whisper Voice v1.0.1 includes a **Neural Translation Engine** that allows you to bridge any linguistic gap instantly.
+
+*   **Input**: Speak in French, Japanese, Russian, or **96 other languages**.
+*   **Output**: The engine instantly reconstructs the semantic meaning into fluent **English**.
+*   **Task Protocol**: Handled via the dedicated `F10` channel.
+
+### 🔍 Why only English translation?
+A common question arises: *Why can't I translate from French to Japanese?*
+
+The architecture of the underlying Whisper model is a **Many-to-English** design. During its massive training phase (680,000 hours of audio), the translation task was specifically optimized to map the global linguistic commons onto a single bridge language: **English**. This allowed the model to reach incredible levels of semantic understanding without the exponential complexity of a "Many-to-Many" mapping. 
+
+By focusing its translation decoder solely on English, Whisper achieves "Zero-Shot" quality that rivals specialized translation engines while remaining lightweight enough to run on your local GPU.
 
 ---
 
-## ✨ Features
+## 🕹️ Command & Control
 
-*   **100% Offline Processing**: Once the recognition engine is downloaded, the cable can be cut. Nothing leaves your machine.
+### Global Hotkeys
-*   **Universal Compatibility**: Works in any text field—editors, chat apps, terminals, or browsers. If you can type there, you can speak there.
+The agent runs silently in the background, waiting for your signal.
-*   **Adaptive Input**:
+
-    *   *Clipboard Mode*: Standard paste injection.
+*   **Transcribe (F9)**: Opens the channel for standard speech-to-text.
-    *   *High-Speed Simulation*: Simulates keystrokes at supersonic speeds (up to 6000 CPM) for apps that block pasting.
+*   **Translate (F10)**: Opens the channel for neural translation.
-*   **System Integration**: Minimalist overlay and system tray presence. It exists when you need it and vanishes when you don't.
+*   **Customization**: Remap these keys in Settings. The recorder supports complex chords (e.g. `Ctrl + Alt + Space`) to fit your workflow.
-*   **Resource Efficiency**: Optimized to run smoothly on consumer hardware without monopolizing your system resources.
+
+### Injection Protocols
+*   **Clipboard Paste**: Standard text injection. Instant, reliable.
+*   **Simulate Typing**: Mimics physical keystrokes at superhuman speed (6000 CPM). Bypasses anti-paste restrictions and "protected" windows.
+
+<br>
+
+## 📊 Intelligence Matrix
+
+Select the model that aligns with your available resources.
+
+| Model | VRAM (GPU) | RAM (CPU) | Designation | Capability |
+| :--- | :--- | :--- | :--- | :--- |
+| `Tiny` | **~500 MB** | ~1 GB | ⚡ **Supersonic** | Command & Control, older hardware. |
+| `Base` | **~600 MB** | ~1 GB | 🚀 **Very Fast** | Daily driver for low-power laptops. |
+| `Small` | **~1 GB** | ~2 GB | ⏩ **Fast** | High accuracy English dictation. |
+| `Medium` | **~2 GB** | ~4 GB | ⚖️ **Balanced** | Complex vocabulary, foreign accents. |
+| `Large-v3 Turbo` | **~4 GB** | ~6 GB | ✨ **Optimal** | **The Sweet Spot.** Near-Large intelligence, Medium speed. |
+| `Large-v3` | **~5 GB** | ~8 GB | 🧠 **Maximum** | Professional grade. Uncompromised. |
+
+> *Note: Acceleration requires you to manually select your Compute Device (CUDA GPU or CPU) in Settings.*
+
+### 📉 Low VRAM Mode
+For users with limited GPU memory (e.g., 4GB cards) or those running heavy games simultaneously, Whisper Voice offers a specialized **Low VRAM Mode**.
+
+*   **Behavior**: The AI model is aggressively unloaded from the GPU immediately after every transcription.
+*   **Benefit**: When idle, the app consumes near-zero VRAM (~0MB), leaving your GPU completely free for gaming or rendering.
+*   **Trade-off**: There is a "cold start" latency of 1-2 seconds for every voice command as the model reloads from the disk cache.
 
 ---
 
-## 🚀 Getting Started
+## 🛠️ Deployment
 
-### Installation
+### 📥 Installation
-1.  Download the latest release.
+1.  **Acquire**: Download `WhisperVoice.exe` from [Releases](https://git.lashman.live/lashman/whisper_voice/releases).
-2.  Run `WhisperVoice.exe`.
+2.  **Deploy**: Place it anywhere. It is portable.
-3.  On the first run, the bootstrapper will autonomously provision the necessary runtime environment. This ensures your system remains clean and dependencies are self-contained.
+3.  **Bootstrap**: Run it. The agent will self-provision an isolated Python runtime (~2GB) on first launch.
+4.  **Sync**: Future updates are handled by the **Smart Bootstrapper**, which surgically updates only changed files, respecting your bandwidth and your settings.
 
-### Usage
+### 🔧 Troubleshooting
-1.  **Set Your Trigger**: Configure a global hotkey (default: `F9`) in the settings.
+*   **App crashes on start**: Ensure you have [Microsoft Visual C++ Redistributable 2015-2022](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist) installed.
-2.  **Speak Freely**: Hold the hotkey (or toggle it) and speak.
+*   **"Simulate Typing" is slow**: Some applications (remote desktops, legacy games) cannot handle the data stream. Lower the typing speed in Settings to ~1200 CPM.
-3.  **Direct Action**: Your words are instantly transcribed and injected into your active window.
+*   **No Audio**: The agent listens to the **Default Communication Device**. Verify your Windows Sound Control Panel.
+
+<br>
 
 ---
 
-## ⚙️ Configuration
+## 🌐 Supported Languages
 
-The **Settings** panel puts the means of configuration in your hands:
+The engine understands the following 99 languages. You can lock the focus to a specific language in Settings to improve accuracy, or rely on **Auto-Detect** for fluid multilingual usage.
 
-*   **Recognition Engine**: Choose the size of the model that fits your hardware capabilities (Tiny to Large). Larger models offer greater precision but require more computing power.
+| | | | | | |
-*   **Input Method**: Switch between "Clipboard Paste" and "Simulate Typing" depending on target application restrictions.
+| :--- | :--- | :--- | :--- | :--- | :--- |
-*   **Typing Speed**: Adjust the keystroke injection rate. Crank it up to 6000 CPM for instant text delivery.
+| Afrikaans 🇿🇦 | Albanian 🇦🇱 | Amharic 🇪🇹 | Arabic 🇸🇦 | Armenian 🇦🇲 | Assamese 🇮🇳 |
-*   **Run on Startup**: Configure the agent to be ready the moment your session begins.
+| Azerbaijani 🇦🇿 | Bashkir 🇷🇺 | Basque 🇪🇸 | Belarusian 🇧🇾 | Bengali 🇧🇩 | Bosnian 🇧🇦 |
+| Breton 🇫🇷 | Bulgarian 🇧🇬 | Burmese 🇲🇲 | Castilian 🇪🇸 | Catalan 🇪🇸 | Chinese 🇨🇳 |
+| Croatian 🇭🇷 | Czech 🇨🇿 | Danish 🇩🇰 | Dutch 🇳🇱 | English 🇺🇸 | Estonian 🇪🇪 |
+| Faroese 🇫🇴 | Finnish 🇫🇮 | Flemish 🇧🇪 | French 🇫🇷 | Galician 🇪🇸 | Georgian 🇬🇪 |
+| German 🇩🇪 | Greek 🇬🇷 | Gujarati 🇮🇳 | Haitian 🇭🇹 | Hausa 🇳🇬 | Hawaiian 🇺🇸 |
+| Hebrew 🇮🇱 | Hindi 🇮🇳 | Hungarian 🇭🇺 | Icelandic 🇮🇸 | Indonesian 🇮🇩 | Italian 🇮🇹 |
+| Japanese 🇯🇵 | Javanese 🇮 Indonesa | Kannada 🇮🇳 | Kazakh 🇰🇿 | Khmer 🇰🇭 | Korean 🇰🇷 |
+| Lao 🇱🇦 | Latin 🇻🇦 | Latvian 🇱🇻 | Lingala 🇨🇩 | Lithuanian 🇱🇹 | Luxembourgish 🇱🇺 |
+| Macedonian 🇲🇰 | Malagasy 🇲🇬 | Malay 🇲🇾 | Malayalam 🇮🇳 | Maltese 🇲🇹 | Maori 🇳🇿 |
+| Marathi 🇮🇳 | Moldavian 🇲🇩 | Mongolian 🇲🇳 | Myanmar 🇲🇲 | Nepali 🇳🇵 | Norwegian 🇳🇴 |
+| Occitan 🇫🇷 | Panjabi 🇮🇳 | Pashto 🇦🇫 | Persian 🇮🇷 | Polish 🇵🇱 | Portuguese 🇵🇹 |
+| Punjabi 🇮🇳 | Romanian 🇷🇴 | Russian 🇷🇺 | Sanskrit 🇮🇳 | Serbian 🇷🇸 | Shona 🇿🇼 |
+| Sindhi 🇵🇰 | Sinhala 🇱🇰 | Slovak 🇸🇰 | Slovenian 🇸🇮 | Somali 🇸🇴 | Spanish 🇪🇸 |
+| Sundanese 🇮🇩 | Swahili 🇰🇪 | Swedish 🇸🇪 | Tagalog 🇵🇭 | Tajik 🇹🇯 | Tamil 🇮🇳 |
+| Tatar 🇷🇺 | Telugu 🇮🇳 | Thai 🇹🇭 | Tibetan 🇨🇳 | Turkish 🇹🇷 | Turkmen 🇹🇲 |
+| Ukrainian 🇺🇦 | Urdu 🇵🇰 | Uzbek 🇺🇿 | Vietnamese 🇻e | Welsh 🏴󠁧󠁢󠁷󠁬󠁳󠁿 | Yiddish 🇮🇱 |
+| Yoruba 🇳🇬 | | | | | |
 
----
+<br>
+<br>
 
-## 🤝 Mutual Aid
+<div align="center">
 
-This project thrives on community collaboration. If you have improvements, fixes, or ideas, you are encouraged to contribute. We build better systems when we build them together, horizontally and transparently.
+### ⚖️ PUBLIC DOMAIN (CC0 1.0)
+*No Rights Reserved. No Gods. No Masters. No Managers.*
 
-*   **Report Issues**: If something breaks, let us know.
+Credit to **OpenAI** (Whisper), **Systran** (Faster-Whisper), and **Silero** (VAD).
-*   **Contribute Code**: The source is open. Fork it, improve it, share it.
 
----
+</div>
-
-*Built with local processing libraries and Qt.*
-*No gods, no cloud managers.*

RELEASE_NOTES.md

@@ -0,0 +1,28 @@
+# Release v1.0.4
+
+**"The Compatibility Update"**
+
+This release focuses on maximum stability across different hardware configurations (AMD, Intel, Nvidia) and fixing startup crashes related to corrupted models or missing drivers.
+
+## 🛠️ Critical Fixes
+
+### 1. Robust CPU Fallback (AMD / Intel Support)
+*   **Problem**: Previously, if an AMD user tried to run the app, it would crash instantly because it tried to load Nvidia CUDA libraries by default.
+*   **Fix**: The app now **silently detects** if CUDA initialization fails (due to missing DLLs or incompatible hardware) and **automatically falls back to CPU mode**.
+*   **Result**: The app "just works" on any Windows machine, regardless of GPU.
+
+### 2. Startup Crash Protection
+*   **Problem**: If `faster_whisper` was imported before checking for valid drivers, the app would crash on launch for some users.
+*   **Fix**: Implemented **Lazy Loading** for the AI engine. The app now starts the UI first, and only loads the heavy AI libraries inside a safety block that catches errors.
+
+### 3. Corrupt Model Auto-Repair
+*   **Problem**: Interrupted downloads could leave a corrupted model folder, preventing the app from ever starting again.
+*   **Fix**: If the app detects a "vocabulary missing" or invalid config error, it will now **automatically delete the corrupt folder** and allow you to re-download it cleanly.
+
+### 4. Windows DLL Injection
+*   **Fix**: Added explicit DLL path injection for `nvidia-cublas` and `nvidia-cudnn` to ensure Python 3.8+ can find the required CUDA libraries on Windows systems that don't have them in PATH.
+
+## 📦 Installation
+1.  Download `WhisperVoice.exe` below.
+2.  Replace your existing `.exe`.
+3.  Run it.

bootstrapper.py

@@ -245,62 +245,106 @@ class Bootstrapper:
             
         req_file = self.source_path / "requirements.txt"
         
+        # Use --prefer-binary to avoid building from source on Windows if possible
+        # Use --no-warn-script-location to reduce noise
+        # CRITICAL: Force --only-binary for llama-cpp-python to prevent picking new source-only versions
+        cmd = [
+            str(self.python_path / "python.exe"), "-m", "pip", "install", 
+            "--prefer-binary", 
+            "--only-binary", "llama-cpp-python", 
+            "--extra-index-url", "https://abetlen.github.io/llama-cpp-python/whl/cpu",
+            "-r", str(req_file)
+        ]
+        
         process = subprocess.Popen(
-            [str(self.python_path / "python.exe"), "-m", "pip", "install", "-r", str(req_file)],
+            cmd,
             stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT,
+            stderr=subprocess.STDOUT, # Merge stderr into stdout
             text=True,
             cwd=str(self.python_path),
             creationflags=subprocess.CREATE_NO_WINDOW
         )
         
+        output_buffer = []
         for line in process.stdout:
-            if self.ui: self.ui.set_detail(line.strip()[:60])
+            line_stripped = line.strip()
-        process.wait()
+            if self.ui: self.ui.set_detail(line_stripped[:60])
+            output_buffer.append(line_stripped)
+            log(line_stripped)
+            
+        return_code = process.wait()
+        
+        if return_code != 0:
+            err_msg = "\n".join(output_buffer[-15:]) # Show last 15 lines
+            raise RuntimeError(f"Pip install failed (Exit code {return_code}):\n{err_msg}")
         
     def refresh_app_source(self):
-        """Refresh app source files. Skips if already exists to save time."""
+        """
-        # Optimization: If app/main.py exists, skip update to improve startup speed.
+        Smartly updates app source files by only copying changed files.
-        # The user can delete the 'runtime' folder to force an update.
+        Preserves user settings and reduces disk I/O.
-        if (self.app_path / "main.py").exists():
+        """
-            log("App already exists. Skipping update.")
+        if self.ui: self.ui.set_status("Checking for updates...")
-            return True
-
-        if self.ui: self.ui.set_status("Updating app files...")
         
         try:
-            # Preserve settings.json if it exists
+            # 1. Ensure destination exists
-            settings_path = self.app_path / "settings.json"
+            if not self.app_path.exists():
-            temp_settings = None
+                self.app_path.mkdir(parents=True, exist_ok=True)
-            if settings_path.exists():
-                try:
-                    temp_settings = settings_path.read_bytes()
-                except:
-                    log("Failed to backup settings.json, it involves risk of data loss.")
-
-            if self.app_path.exists():
-                shutil.rmtree(self.app_path, ignore_errors=True)
                 
-            shutil.copytree(
+            # 2. Walk source and sync
-                self.source_path,
+            # source_path is the temporary bundled folder
-                self.app_path,
+            # app_path is the persistent runtime folder
-                ignore=shutil.ignore_patterns(
+            
-                    '__pycache__', '*.pyc', '.git', 'venv', 
+            changes_made = 0
-                    'build', 'dist', '*.egg-info', 'runtime'
+            
-                )
+            for src_dir, dirs, files in os.walk(self.source_path):
-            )
+                # Determine relative path from source root
-
+                rel_path = Path(src_dir).relative_to(self.source_path)
-            # Restore settings.json
+                dst_dir = self.app_path / rel_path
-            if temp_settings:
+                
-                try:
+                # Ensure directory exists
-                    settings_path.write_bytes(temp_settings)
+                if not dst_dir.exists():
-                    log("Restored settings.json")
+                    dst_dir.mkdir(parents=True, exist_ok=True)
-                except:
+                
-                    log("Failed to restore settings.json")
+                for file in files:
+                    # Skip ignored files
+                    if file in ['__pycache__', '.git', 'settings.json'] or file.endswith('.pyc'):
+                        continue
+                        
+                    src_file = Path(src_dir) / file
+                    dst_file = dst_dir / file
                     
+                    # Check if update needed
+                    should_copy = False
+                    if not dst_file.exists():
+                        should_copy = True
+                    else:
+                        # Compare size first (fast)
+                        if src_file.stat().st_size != dst_file.stat().st_size:
+                            should_copy = True
+                        else:
+                            # Compare content (slower but accurate)
+                            # Only read if size matches to verify diff
+                            if src_file.read_bytes() != dst_file.read_bytes():
+                                should_copy = True
+                    
+                    if should_copy:
+                        shutil.copy2(src_file, dst_file)
+                        changes_made += 1
+                        if self.ui: self.ui.set_detail(f"Updated: {file}")
+
+            # 3. Cleanup logic (Optional: remove files in dest that are not in source)
+            # For now, we only add/update to prevent deleting generated user files (logs, etc)
+            
+            if changes_made > 0:
+                log(f"Update complete. {changes_made} files changed.")
+            else:
+                log("App is up to date.")
+                
             return True
         except Exception as e:
             log(f"Error refreshing app source: {e}")
+            # Fallback to nuclear option if sync fails completely? 
+            # No, 'smart_sync' failing might mean permissions, nuclear wouldn't help.
             return False
         
     def run_app(self):
@@ -323,22 +367,51 @@ class Bootstrapper:
             messagebox.showerror("WhisperVoice Error", f"Failed to launch app: {e}")
             return False
 
+    def check_dependencies(self):
+        """Check if critical dependencies are importable in the embedded python."""
+        if not self.is_python_ready(): return False
+        
+        try:
+            # Check for core libs that might be missing
+            # We use a subprocess to check imports in the runtime environment
+            subprocess.check_call(
+                [str(self.python_path / "python.exe"), "-c", "import faster_whisper; import llama_cpp; import PySide6"],
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                cwd=str(self.python_path),
+                creationflags=subprocess.CREATE_NO_WINDOW
+            )
+            return True
+        except (subprocess.CalledProcessError, FileNotFoundError):
+            return False
+
     def setup_and_run(self):
         """Full setup/update and run flow."""
         try:
+            # 1. Ensure basics
             if not self.is_python_ready():
                 self.download_python()
+                self._fix_pth_file() # Ensure pth is fixed immediately after download
                 self.install_pip()
-                self.install_packages()
+                # self.install_packages() # We'll do this in the dependency check step now
             
             # Always refresh source to ensure we have the latest bundled code
             self.refresh_app_source()
+
+            # 2. Check and Install Dependencies
+            # We do this AFTER refreshing source so we have the latest requirements.txt
+            if not self.check_dependencies():
+                log("Dependencies missing or incomplete. Installing...")
+                self.install_packages()
             
             # Launch
             if self.run_app():
                 if self.ui: self.ui.root.quit()
         except Exception as e:
-            messagebox.showerror("Setup Error", f"Installation failed: {e}")
+            if self.ui:
+                import tkinter.messagebox as mb
+                mb.showerror("Setup Error", f"Installation failed: {e}") # Improved error visibility
+            log(f"Fatal error: {e}")
             import traceback
             traceback.print_exc()
 

main.py

@@ -9,6 +9,31 @@ app_dir = os.path.dirname(os.path.abspath(__file__))
 if app_dir not in sys.path:
     sys.path.insert(0, app_dir)
 
+# -----------------------------------------------------------------------------
+# WINDOWS DLL FIX (CRITICAL for Portable CUDA)
+# Python 3.8+ on Windows requires explicit DLL directory addition.
+# -----------------------------------------------------------------------------
+if os.name == 'nt' and hasattr(os, 'add_dll_directory'):
+    try:
+        from pathlib import Path
+        # Scan sys.path for site-packages
+        for p in sys.path:
+            path_obj = Path(p)
+            if path_obj.name == 'site-packages' and path_obj.exists():
+                nvidia_path = path_obj / "nvidia"
+                if nvidia_path.exists():
+                    for subdir in nvidia_path.iterdir():
+                        # Add 'bin' folder from each nvidia stub (cublas, cudnn, etc.)
+                        bin_path = subdir / "bin"
+                        if bin_path.exists():
+                            os.add_dll_directory(str(bin_path))
+                # Also try adding site-packages itself just in case
+                # os.add_dll_directory(str(path_obj))
+                break
+    except Exception:
+        pass
+# -----------------------------------------------------------------------------
+
 from PySide6.QtWidgets import QApplication, QFileDialog, QMessageBox
 from PySide6.QtCore import QObject, Slot, Signal, QThread, Qt, QUrl
 from PySide6.QtQml import QQmlApplicationEngine
@@ -19,6 +44,7 @@ from src.ui.bridge import UIBridge
 from src.ui.tray import SystemTray
 from src.core.audio_engine import AudioEngine
 from src.core.transcriber import WhisperTranscriber
+from src.core.llm_engine import LLMEngine
 from src.core.hotkey_manager import HotkeyManager
 from src.core.config import ConfigManager
 from src.utils.injector import InputInjector
@@ -87,7 +113,7 @@ def _silent_shutdown_hook(exc_type, exc_value, exc_tb):
 sys.excepthook = _silent_shutdown_hook
 
 class DownloadWorker(QThread):
-    """Background worker for model downloads."""
+    """Background worker for model downloads with REAL progress."""
     progress = Signal(int)
     finished = Signal()
     error = Signal(str)
@@ -98,33 +124,144 @@ class DownloadWorker(QThread):
 
     def run(self):
         try:
-            from faster_whisper import download_model
+            import requests
+            from tqdm import tqdm
             model_path = get_models_path()
-            # Download to a specific subdirectory to keep things clean and predictable
+            # Determine what to download
-            # This matches the logic in transcriber.py which looks for this specific path
             dest_dir = model_path / f"faster-whisper-{self.model_name}"
-            logging.info(f"Downloading Model '{self.model_name}' to {dest_dir}...")
+            repo_id = f"Systran/faster-whisper-{self.model_name}"
+            files = ["config.json", "model.bin", "tokenizer.json", "vocabulary.json"]
+            base_url = f"https://huggingface.co/{repo_id}/resolve/main"
+
+            dest_dir.mkdir(parents=True, exist_ok=True)
+            logging.info(f"Downloading {self.model_name} to {dest_dir}...")
             
-            # Ensure parent exists
+            # 1. Calculate Total Size
-            model_path.mkdir(parents=True, exist_ok=True)
+            total_size = 0
+            file_sizes = {}
             
-            # output_dir in download_model specifies where the model files are saved
+            with requests.Session() as s:
-            download_model(self.model_name, output_dir=str(dest_dir))
+                for fname in files:
+                    url = f"{base_url}/{fname}"
+                    head = s.head(url, allow_redirects=True)
+                    if head.status_code == 200:
+                        size = int(head.headers.get('content-length', 0))
+                        file_sizes[fname] = size
+                        total_size += size
+                    else:
+                        # Fallback for vocabulary.json vs vocabulary.txt
+                        if fname == "vocabulary.json":
+                             # Try .txt? Or just skip if not found? 
+                             # Faster-whisper usually has vocabulary.json
+                             pass
             
+            # 2. Download loop
+            downloaded_bytes = 0
+            
+            with requests.Session() as s:
+                for fname in files:
+                    if fname not in file_sizes: continue
+                    
+                    url = f"{base_url}/{fname}"
+                    dest_file = dest_dir / fname
+                    
+                    # Resume check? 
+                    # Simpler to just overwrite for reliability unless we want complex resume logic.
+                    # We'll overwrite.
+                    
+                    resp = s.get(url, stream=True)
+                    resp.raise_for_status()
+                    
+                    with open(dest_file, 'wb') as f:
+                        for chunk in resp.iter_content(chunk_size=8192):
+                            if chunk:
+                                f.write(chunk)
+                                downloaded_bytes += len(chunk)
+                                
+                                # Emit Progress
+                                if total_size > 0:
+                                    pct = int((downloaded_bytes / total_size) * 100)
+                                    self.progress.emit(pct)
+
             self.finished.emit()
+            
         except Exception as e:
             logging.error(f"Download failed: {e}")
             self.error.emit(str(e))
 
+class LLMDownloadWorker(QThread):
+    progress = Signal(int)
+    finished = Signal()
+    error = Signal(str)
+
+    def __init__(self, parent=None):
+        super().__init__(parent)
+
+    def run(self):
+        try:
+            import requests
+            # Support one model for now
+            url = "https://huggingface.co/hugging-quants/Llama-3.2-1B-Instruct-Q4_K_M-GGUF/resolve/main/llama-3.2-1b-instruct-q4_k_m.gguf?download=true"
+            fname = "llama-3.2-1b-instruct-q4_k_m.gguf"
+            
+            model_path = get_models_path() / "llm" / "llama-3.2-1b-instruct"
+            model_path.mkdir(parents=True, exist_ok=True)
+            dest_file = model_path / fname
+            
+            # Simple check if exists and > 0 size? 
+            # We assume if the user clicked download, they want to download it.
+            
+            with requests.Session() as s:
+                head = s.head(url, allow_redirects=True)
+                total_size = int(head.headers.get('content-length', 0))
+                
+                resp = s.get(url, stream=True)
+                resp.raise_for_status()
+                
+                downloaded = 0
+                with open(dest_file, 'wb') as f:
+                    for chunk in resp.iter_content(chunk_size=8192):
+                        if chunk:
+                            f.write(chunk)
+                            downloaded += len(chunk)
+                            if total_size > 0:
+                                pct = int((downloaded / total_size) * 100)
+                                self.progress.emit(pct)
+                                
+            self.finished.emit()
+            
+        except Exception as e:
+            logging.error(f"LLM Download failed: {e}")
+            self.error.emit(str(e))
+
+class LLMWorker(QThread):
+    finished = Signal(str)
+    
+    def __init__(self, llm_engine, text, mode, parent=None):
+        super().__init__(parent)
+        self.llm_engine = llm_engine
+        self.text = text
+        self.mode = mode
+        
+    def run(self):
+        try:
+            corrected = self.llm_engine.correct_text(self.text, self.mode)
+            self.finished.emit(corrected)
+        except Exception as e:
+            logging.error(f"LLMWorker crashed: {e}")
+            self.finished.emit(self.text) # Fail safe: return original text
+
+
 class TranscriptionWorker(QThread):
     finished = Signal(str)
-    def __init__(self, transcriber, audio_data, is_file=False, parent=None):
+    def __init__(self, transcriber, audio_data, is_file=False, parent=None, task_override=None):
         super().__init__(parent)
         self.transcriber = transcriber
         self.audio_data = audio_data
         self.is_file = is_file
+        self.task_override = task_override
     def run(self):
-        text = self.transcriber.transcribe(self.audio_data, is_file=self.is_file)
+        text = self.transcriber.transcribe(self.audio_data, is_file=self.is_file, task=self.task_override)
         self.finished.emit(text)
 
 class WhisperApp(QObject):
@@ -156,6 +293,7 @@ class WhisperApp(QObject):
         self.bridge.settingChanged.connect(self.on_settings_changed)
         self.bridge.hotkeysEnabledChanged.connect(self.on_hotkeys_enabled_toggle)
         self.bridge.downloadRequested.connect(self.on_download_requested)
+        self.bridge.llmDownloadRequested.connect(self.on_llm_download_requested)
         
         self.engine.rootContext().setContextProperty("ui", self.bridge)
         
@@ -166,13 +304,20 @@ class WhisperApp(QObject):
         self.tray.transcribe_file_requested.connect(self.transcribe_file)
         
         # Init Tooltip
-        hotkey = self.config.get("hotkey")
+        from src.utils.formatters import format_hotkey
-        self.tray.setToolTip(f"Whisper Voice - Press {hotkey} to Record")
+        self.format_hotkey = format_hotkey # Store ref
+        
+        hk1 = self.format_hotkey(self.config.get("hotkey"))
+        hk2 = self.format_hotkey(self.config.get("hotkey_translate"))
+        self.tray.setToolTip(f"Whisper Voice\nTranscribe: {hk1}\nTranslate: {hk2}")
         
         # 3. Logic Components Placeholders
         self.audio_engine = None
         self.transcriber = None
-        self.hotkey_manager = None
+        self.llm_engine = None
+        self.hk_transcribe = None
+        self.hk_correct = None
+        self.hk_translate = None
         self.overlay_root = None
         
         # 4. Start Loader
@@ -222,12 +367,23 @@ class WhisperApp(QObject):
             self.settings_root.setVisible(False)
         
         # Install Low-Level Window Hook for Transparent Hit Test
-        # We must keep a reference to 'self.hook' so it isn't GC'd
+        try:
-        # scale = self.overlay_root.devicePixelRatio()
+            from src.utils.window_hook import WindowHook
-        # self.hook = WindowHook(int(self.overlay_root.winId()), 500, 300, scale)
+            hwnd = self.overlay_root.winId()
-        # self.hook.install()
+            # Initial scale from config
-        
+            scale = float(self.config.get("ui_scale"))
-        # NOTE: HitTest hook will be installed here later
+            
+            # Current Overlay Dimensions
+            win_w = int(460 * scale)
+            win_h = int(180 * scale)
+            
+            self.window_hook = WindowHook(hwnd, win_w, win_h, initial_scale=scale)
+            self.window_hook.install()
+            
+            # Initial state: Disabled because we start inactive
+            self.window_hook.set_enabled(False)
+        except Exception as e:
+            logging.error(f"Failed to install WindowHook: {e}")
 
     def center_overlay(self):
         """Calculates and sets the Overlay position above the taskbar."""
@@ -255,14 +411,77 @@ class WhisperApp(QObject):
         self.audio_engine.set_visualizer_callback(self.bridge.update_amplitude)
         self.audio_engine.set_silence_callback(self.on_silence_detected)
         self.transcriber = WhisperTranscriber()
-        self.hotkey_manager = HotkeyManager()
+        self.llm_engine = LLMEngine()
-        self.hotkey_manager.triggered.connect(self.toggle_recording)
+        
-        self.hotkey_manager.start()
+        # Dual Hotkey Managers
+        self.hk_transcribe = HotkeyManager(config_key="hotkey")
+        self.hk_transcribe.triggered.connect(lambda: self.toggle_recording(task_override="transcribe", task_mode="standard"))
+        self.hk_transcribe.start()
+        
+        self.hk_correct = HotkeyManager(config_key="hotkey_correct")
+        self.hk_correct.triggered.connect(lambda: self.toggle_recording(task_override="transcribe", task_mode="correct"))
+        self.hk_correct.start()
+        
+        self.hk_translate = HotkeyManager(config_key="hotkey_translate")
+        self.hk_translate.triggered.connect(lambda: self.toggle_recording(task_override="translate", task_mode="standard"))
+        self.hk_translate.start()
+        
         self.bridge.update_status("Ready")
 
     def run(self):
         sys.exit(self.qt_app.exec())
 
+    @Slot(str, str)
+    @Slot(str)
+    def toggle_recording(self, task_override=None, task_mode="standard"):
+        """
+        task_override: 'transcribe' or 'translate' (passed to whisper)
+        task_mode: 'standard' or 'correct' (determines post-processing)
+        """
+        if task_mode == "correct":
+            self.current_task_requires_llm = True
+        elif task_mode == "standard":
+            self.current_task_requires_llm = False # Explicit reset
+            
+        # Actual Logic
+        if self.bridge.isRecording:
+            logging.info("Stopping recording...")
+            # stop_recording returns the numpy array directly
+            audio_data = self.audio_engine.stop_recording()
+            
+            self.bridge.isRecording = False
+            self.bridge.update_status("Processing...")
+            self.bridge.isProcessing = True
+            
+            # Save task override for processing
+            self.last_task_override = task_override
+            
+            if audio_data is not None and len(audio_data) > 0:
+                # Use the task that started this session, or the override if provided
+                final_task = getattr(self, "current_recording_task", self.config.get("task"))
+                if task_override: final_task = task_override
+                
+                self.worker = TranscriptionWorker(self.transcriber, audio_data, parent=self, task_override=final_task)
+                self.worker.finished.connect(self.on_transcription_done)
+                self.worker.start()
+            else:
+                self.bridge.update_status("Ready")
+                self.bridge.isProcessing = False
+                
+        else:
+            # START RECORDING
+            if self.bridge.isProcessing:
+                logging.warning("Ignored toggle request: Transcription in progress.")
+                return
+
+            intended_task = task_override if task_override else self.config.get("task")
+            self.current_recording_task = intended_task
+            
+            logging.info(f"Starting recording... (Task: {intended_task}, Mode: {task_mode})")
+            self.audio_engine.start_recording()
+            self.bridge.isRecording = True
+            self.bridge.update_status(f"Recording ({intended_task})...")
+
     @Slot()
     def quit_app(self):
         logging.info("Shutting down...")
@@ -275,7 +494,8 @@ class WhisperApp(QObject):
             except: pass
             self.bridge.stats_worker.stop()
         
-        if self.hotkey_manager: self.hotkey_manager.stop()
+        if self.hk_transcribe: self.hk_transcribe.stop()
+        if self.hk_translate: self.hk_translate.stop()
         
         # Close all QML windows to ensure bindings stop before Python objects die
         if self.overlay_root: 
@@ -350,10 +570,16 @@ class WhisperApp(QObject):
         print(f"Setting Changed: {key} = {value}")
         
         # 1. Hotkey Reload
-        if key == "hotkey":
+        if key in ["hotkey", "hotkey_translate", "hotkey_correct"]:
-            if self.hotkey_manager: self.hotkey_manager.reload_hotkey()
+            if self.hk_transcribe: self.hk_transcribe.reload_hotkey()
+            if self.hk_correct: self.hk_correct.reload_hotkey()
+            if self.hk_translate: self.hk_translate.reload_hotkey()
+            
             if self.tray:
-                self.tray.setToolTip(f"Whisper Voice - Press {value} to Record")
+                hk1 = self.format_hotkey(self.config.get("hotkey"))
+                hk3 = self.format_hotkey(self.config.get("hotkey_correct"))
+                hk2 = self.format_hotkey(self.config.get("hotkey_translate"))
+                self.tray.setToolTip(f"Whisper Voice\nTranscribe: {hk1}\nCorrect: {hk3}\nTranslate: {hk2}")
 
         # 2. AI Model Reload (Heavy)
         if key in ["model_size", "compute_device", "compute_type"]:
@@ -456,6 +682,8 @@ class WhisperApp(QObject):
         file_path, _ = QFileDialog.getOpenFileName(None, "Select Audio", "", "Audio (*.mp3 *.wav *.flac *.m4a *.ogg)")
         if file_path:
             self.bridge.update_status("Thinking...")
+            # Files use the default configured task usually, or we could ask? 
+            # Default to config setting for files.
             self.worker = TranscriptionWorker(self.transcriber, file_path, is_file=True, parent=self)
             self.worker.finished.connect(self.on_transcription_done)
             self.worker.start()
@@ -463,48 +691,73 @@ class WhisperApp(QObject):
     @Slot()
     def on_silence_detected(self):
         from PySide6.QtCore import QMetaObject, Qt
+        # Silence detection always triggers the task that was active? 
+        # Since silence stops recording, it just calls toggle_recording with no arg, using the stored current_task?
+        # Let's ensure toggle_recording handles no arg calls by stopping the CURRENT task.
         QMetaObject.invokeMethod(self, "toggle_recording", Qt.QueuedConnection)
 
-    @Slot()
-    def toggle_recording(self):
-        if not self.audio_engine: return
-        
-        # Prevent starting a new recording while we are still transcribing the last one
-        if self.bridge.isProcessing:
-            logging.warning("Ignored toggle request: Transcription in progress.")
-            return
 
-        if self.audio_engine.recording:
-            self.bridge.update_status("Thinking...")
-            self.bridge.isRecording = False
-            self.bridge.isProcessing = True # Start Processing
-            audio_data = self.audio_engine.stop_recording()
-            self.worker = TranscriptionWorker(self.transcriber, audio_data, parent=self)
-            self.worker.finished.connect(self.on_transcription_done)
-            self.worker.start()
-        else:
-            self.bridge.update_status("Recording")
-            self.bridge.isRecording = True
-            self.audio_engine.start_recording()
 
     @Slot(bool)
     def on_ui_toggle_request(self, state):
         if state != self.audio_engine.recording:
-            self.toggle_recording()
+            self.toggle_recording() # Default behavior for UI clicks
 
     @Slot(str)
     def on_transcription_done(self, text: str):
         self.bridge.update_status("Ready")
-        self.bridge.isProcessing = False # End Processing
+        self.bridge.isProcessing = False # Temporarily false? No, keep it true if we chain.
+        
+        # Check LLM Settings -> AND check if the current task requested it
+        llm_enabled = self.config.get("llm_enabled")
+        requires_llm = getattr(self, "current_task_requires_llm", False)
+        
+        # We only correct if:
+        # 1. LLM is globally enabled (safety switch)
+        # 2. current_task_requires_llm is True (triggered by Correct hotkey)
+        # OR 3. Maybe user WANTS global correction? Ideally user uses separate hotkey. 
+        # Let's say: If "Correction" is enabled in settings, does it apply to ALL? 
+        # The user's feedback suggests they DON'T want it on regular hotkey.
+        # So we enforce: Correct Hotkey -> Corrects. Regular Hotkey -> Raw.
+        # BUT we must handle the case where user expects the old behavior?
+        # Let's make it strict: Only correct if triggered by correct hotkey OR if we add a "Correct All" toggle later.
+        # For now, let's respect the flag. But wait, if llm_enabled is OFF, we shouldn't run it even if hotkey pressed?
+        # Yes, safety switch.
+        
+        if text and llm_enabled and requires_llm:
+            # Chain to LLM
+            self.bridge.isProcessing = True
+            self.bridge.update_status("Correcting...")
+            mode = self.config.get("llm_mode")
+            self.llm_worker = LLMWorker(self.llm_engine, text, mode, parent=self)
+            self.llm_worker.finished.connect(self.on_llm_done)
+            self.llm_worker.start()
+            return
+
+        self.bridge.isProcessing = False
         if text:
             method = self.config.get("input_method")
             speed = int(self.config.get("typing_speed"))
             InputInjector.inject_text(text, method, speed)
+            
+    @Slot(str)
+    def on_llm_done(self, text: str):
+        self.bridge.update_status("Ready")
+        self.bridge.isProcessing = False
+        if text:
+            method = self.config.get("input_method")
+            speed = int(self.config.get("typing_speed"))
+            InputInjector.inject_text(text, method, speed)
+        
+        # Cleanup
+        if hasattr(self, 'llm_worker') and self.llm_worker:
+            self.llm_worker.deleteLater()
+            self.llm_worker = None
 
     @Slot(bool)
     def on_hotkeys_enabled_toggle(self, state):
-        if self.hotkey_manager:
+        if self.hk_transcribe: self.hk_transcribe.set_enabled(state)
-            self.hotkey_manager.set_enabled(state)
+        if self.hk_translate: self.hk_translate.set_enabled(state)
 
     @Slot(str)
     def on_download_requested(self, size):
@@ -519,6 +772,19 @@ class WhisperApp(QObject):
         self.download_worker.error.connect(self.on_download_error)
         self.download_worker.start()
 
+    @Slot()
+    def on_llm_download_requested(self):
+        if self.bridge.isDownloading: return
+        
+        self.bridge.update_status("Downloading LLM...")
+        self.bridge.isDownloading = True
+        
+        self.llm_dl_worker = LLMDownloadWorker(parent=self)
+        self.llm_dl_worker.progress.connect(self.on_loader_progress) # Reuse existing progress slot? Yes.
+        self.llm_dl_worker.finished.connect(self.on_download_finished) # Reuses same cleanup
+        self.llm_dl_worker.error.connect(self.on_download_error)
+        self.llm_dl_worker.start()
+
     def on_download_finished(self):
         self.bridge.isDownloading = False
         self.bridge.update_status("Ready")
@@ -531,6 +797,25 @@ class WhisperApp(QObject):
         self.bridge.update_status("Error")
         logging.error(f"Download Error: {err}")
 
+    @Slot(bool)
+    def on_ui_toggle_request(self, is_recording):
+        """Called when recording state changes."""
+        # Update Window Hook to allow clicking if active
+        is_active = is_recording or self.bridge.isProcessing
+        if hasattr(self, 'window_hook'):
+            self.window_hook.set_enabled(is_active)
+            
+    @Slot(bool)
+    def on_processing_changed(self, is_processing):
+        is_active = self.bridge.isRecording or is_processing
+        if hasattr(self, 'window_hook'):
+            self.window_hook.set_enabled(is_active)
+
 if __name__ == "__main__":
+    import sys
     app = WhisperApp()
-    app.run()
+    
+    # Connect extra signal for processing state
+    app.bridge.isProcessingChanged.connect(app.on_processing_changed)
+    
+    sys.exit(app.run())

portable_build.py

@@ -39,39 +39,37 @@ def build_portable():
     print("⏳ This may take 5-10 minutes...")
     
     PyInstaller.__main__.run([
-        "main.py",                       # Entry point
+        "bootstrapper.py",               # Entry point (Tiny Installer)
         "--name=WhisperVoice",           # EXE name
-        "--onefile",                     # Single EXE (slower startup but portable)
+        "--onefile",                     # Single EXE
         "--noconsole",                   # No terminal window
         "--clean",                       # Clean cache
-        *add_data_args,                  # Bundled assets
         
-        # Heavy libraries that need special collection
+        # Bundle the app source to be extracted by bootstrapper
-        "--collect-all", "faster_whisper",
+        # The bootstrapper expects 'app_source' folder in bundled resources
-        "--collect-all", "ctranslate2",
+        "--add-data", f"src{os.pathsep}app_source/src",
-        "--collect-all", "PySide6",
+        "--add-data", f"main.py{os.pathsep}app_source",
-        "--collect-all", "torch",
+        "--add-data", f"requirements.txt{os.pathsep}app_source",
-        "--collect-all", "numpy",
         
-        # Hidden imports (modules imported dynamically)
+        # Add assets
-        "--hidden-import", "keyboard",
+        "--add-data", f"src/ui/qml{os.pathsep}app_source/src/ui/qml",
-        "--hidden-import", "pyperclip",
+        "--add-data", f"assets{os.pathsep}app_source/assets",
-        "--hidden-import", "psutil",
-        "--hidden-import", "pynvml",
-        "--hidden-import", "sounddevice",
-        "--hidden-import", "scipy",
-        "--hidden-import", "scipy.signal",
-        "--hidden-import", "huggingface_hub",
-        "--hidden-import", "tokenizers",
         
-        # Qt plugins
+        # No heavy collections! 
-        "--hidden-import", "PySide6.QtQuickControls2",
+        # The bootstrapper uses internal pip to install everything.
-        "--hidden-import", "PySide6.QtQuick.Controls",
         
-        # Icon (convert to .ico for Windows)
+        # Exclude heavy modules to ensure this exe stays tiny
-        # "--icon=icon.ico",  # Uncomment if you have a .ico file
+        "--exclude-module", "faster_whisper",
+        "--exclude-module", "torch",
+        "--exclude-module", "PySide6",
+        "--exclude-module", "llama_cpp",
+
+        
+        # Icon
+        # "--icon=icon.ico",
     ])
 
+
     print("\n" + "="*60)
     print("✅ BUILD COMPLETE!")
     print("="*60)

publish_release.py

@@ -0,0 +1,73 @@
+import os
+import requests
+import mimetypes
+
+# Configuration
+API_URL = "https://git.lashman.live/api/v1"
+OWNER = "lashman"
+REPO = "whisper_voice"
+TAG = "v1.0.4"
+TOKEN = "6153890332afff2d725aaf4729bc54b5030d5700" # Extracted from git config
+EXE_PATH = r"dist\WhisperVoice.exe"
+
+headers = {
+    "Authorization": f"token {TOKEN}",
+    "Accept": "application/json"
+}
+
+def create_release():
+    print(f"Creating release {TAG}...")
+    
+    # Read Release Notes
+    with open("RELEASE_NOTES.md", "r", encoding="utf-8") as f:
+        notes = f.read()
+    
+    # Create Release
+    payload = {
+        "tag_name": TAG,
+        "name": TAG,
+        "body": notes,
+        "draft": False,
+        "prerelease": False
+    }
+    
+    url = f"{API_URL}/repos/{OWNER}/{REPO}/releases"
+    resp = requests.post(url, json=payload, headers=headers)
+    
+    if resp.status_code == 201:
+        print("Release created successfully!")
+        return resp.json()
+    elif resp.status_code == 409:
+        print("Release already exists. Fetching it...")
+        # Get by tag
+        resp = requests.get(f"{API_URL}/repos/{OWNER}/{REPO}/releases/tags/{TAG}", headers=headers)
+        if resp.status_code == 200:
+            return resp.json()
+            
+    print(f"Failed to create release: {resp.status_code} - {resp.text}")
+    return None
+
+def upload_asset(release_id, file_path):
+    print(f"Uploading asset: {file_path}...")
+    filename = os.path.basename(file_path)
+    
+    with open(file_path, "rb") as f:
+        data = f.read()
+        
+    url = f"{API_URL}/repos/{OWNER}/{REPO}/releases/{release_id}/assets?name={filename}"
+    
+    # Gitea API expects raw body
+    resp = requests.post(url, data=data, headers=headers)
+    
+    if resp.status_code == 201:
+        print(f"Uploaded {filename} successfully!")
+    else:
+        print(f"Failed to upload asset: {resp.status_code} - {resp.text}")
+
+def main():
+    release = create_release()
+    if release:
+        upload_asset(release["id"], EXE_PATH)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -5,6 +5,7 @@
 faster-whisper>=1.0.0
 torch>=2.0.0
 
+
 # UI Framework
 PySide6>=6.6.0
 
@@ -28,3 +29,6 @@ huggingface-hub>=0.20.0
 pystray>=0.19.0
 Pillow>=10.0.0
 darkdetect>=0.8.0
+
+# LLM / Correction
+llama-cpp-python>=0.2.20

src/core/config.py

@@ -16,6 +16,8 @@ from src.core.paths import get_base_path
 # Default Configuration
 DEFAULT_SETTINGS = {
     "hotkey": "f8",
+    "hotkey_translate": "f10",
+    "hotkey_correct": "f9",     # New: Transcribe + Correct
     "model_size": "small",
     "input_device": None,       # Device ID (int) or Name (str), None = Default
     "save_recordings": False,   # Save .wav files for debugging
@@ -38,13 +40,25 @@ DEFAULT_SETTINGS = {
     
     # AI - Advanced
     "language": "auto",         # "auto" or ISO code
+    "task": "transcribe",       # "transcribe" or "translate" (to English)
     "compute_device": "auto",   # "auto", "cuda", "cpu"
     "compute_type": "int8",     # "int8", "float16", "float32"
     "beam_size": 5,
     "best_of": 5,
     "vad_filter": True,
     "no_repeat_ngram_size": 0,
-    "condition_on_previous_text": True
+    "condition_on_previous_text": True,
+    "initial_prompt": "Mm-hmm. Okay, let's go. I speak in full sentences.", # Default: Forces punctuation
+    
+    # LLM Correction
+    "llm_enabled": False,
+    "llm_mode": "Standard", # "Grammar", "Standard", "Rewrite"
+    "llm_model_name": "llama-3.2-1b-instruct",
+    
+
+    
+    # Low VRAM Mode
+    "unload_models_after_use": False # If True, models are unloaded immediately to free VRAM
 }
 
 class ConfigManager:
@@ -94,9 +108,9 @@ class ConfigManager:
         except Exception as e:
             logging.error(f"Failed to save settings: {e}")
 
-    def get(self, key: str) -> Any:
+    def get(self, key: str, default: Any = None) -> Any:
         """Get a setting value."""
-        return self.data.get(key, DEFAULT_SETTINGS.get(key))
+        return self.data.get(key, DEFAULT_SETTINGS.get(key, default))
 
 
 

src/core/hotkey_manager.py

@@ -30,15 +30,16 @@ class HotkeyManager(QObject):
     
     triggered = Signal()
 
-    def __init__(self, hotkey: str = "f8"):
+    def __init__(self, config_key: str = "hotkey"):
         """
         Initialize the HotkeyManager.
 
         Args:
-            hotkey (str): The global hotkey string description. Default: "f8".
+            config_key (str): The configuration key to look up (e.g. "hotkey").
         """
         super().__init__()
-        self.hotkey = hotkey
+        self.config_key = config_key
+        self.hotkey = "f8" # Placeholder
         self.is_listening = False
         self._enabled = True
 
@@ -58,9 +59,9 @@ class HotkeyManager(QObject):
             
         from src.core.config import ConfigManager
         config = ConfigManager()
-        self.hotkey = config.get("hotkey")
+        self.hotkey = config.get(self.config_key)
         
-        logging.info(f"Registering global hotkey: {self.hotkey}")
+        logging.info(f"Registering global hotkey ({self.config_key}): {self.hotkey}")
         try:
             # We don't suppress=True here because we want the app to see keys during recording 
             # (Wait, actually if we are recording we WANT keyboard to see it, 

src/core/languages.py

@@ -0,0 +1,120 @@
+"""
+Supported Languages Module
+==========================
+Full list of languages supported by OpenAI Whisper.
+Maps ISO codes to display names.
+"""
+
+LANGUAGES = {
+    "auto": "Auto Detect",
+    "af": "Afrikaans",
+    "sq": "Albanian",
+    "am": "Amharic",
+    "ar": "Arabic",
+    "hy": "Armenian",
+    "as": "Assamese",
+    "az": "Azerbaijani",
+    "ba": "Bashkir",
+    "eu": "Basque",
+    "be": "Belarusian",
+    "bn": "Bengali",
+    "bs": "Bosnian",
+    "br": "Breton",
+    "bg": "Bulgarian",
+    "my": "Burmese",
+    "ca": "Catalan",
+    "zh": "Chinese",
+    "hr": "Croatian",
+    "cs": "Czech",
+    "da": "Danish",
+    "nl": "Dutch",
+    "en": "English",
+    "et": "Estonian",
+    "fo": "Faroese",
+    "fi": "Finnish",
+    "fr": "French",
+    "gl": "Galician",
+    "ka": "Georgian",
+    "de": "German",
+    "el": "Greek",
+    "gu": "Gujarati",
+    "ht": "Haitian",
+    "ha": "Hausa",
+    "haw": "Hawaiian",
+    "he": "Hebrew",
+    "hi": "Hindi",
+    "hu": "Hungarian",
+    "is": "Icelandic",
+    "id": "Indonesian",
+    "it": "Italian",
+    "ja": "Japanese",
+    "jw": "Javanese",
+    "kn": "Kannada",
+    "kk": "Kazakh",
+    "km": "Khmer",
+    "ko": "Korean",
+    "lo": "Lao",
+    "la": "Latin",
+    "lv": "Latvian",
+    "ln": "Lingala",
+    "lt": "Lithuanian",
+    "lb": "Luxembourgish",
+    "mk": "Macedonian",
+    "mg": "Malagasy",
+    "ms": "Malay",
+    "ml": "Malayalam",
+    "mt": "Maltese",
+    "mi": "Maori",
+    "mr": "Marathi",
+    "mn": "Mongolian",
+    "ne": "Nepali",
+    "no": "Norwegian",
+    "oc": "Occitan",
+    "pa": "Punjabi",
+    "ps": "Pashto",
+    "fa": "Persian",
+    "pl": "Polish",
+    "pt": "Portuguese",
+    "ro": "Romanian",
+    "ru": "Russian",
+    "sa": "Sanskrit",
+    "sr": "Serbian",
+    "sn": "Shona",
+    "sd": "Sindhi",
+    "si": "Sinhala",
+    "sk": "Slovak",
+    "sl": "Slovenian",
+    "so": "Somali",
+    "es": "Spanish",
+    "su": "Sundanese",
+    "sw": "Swahili",
+    "sv": "Swedish",
+    "tl": "Tagalog",
+    "tg": "Tajik",
+    "ta": "Tamil",
+    "tt": "Tatar",
+    "te": "Telugu",
+    "th": "Thai",
+    "bo": "Tibetan",
+    "tr": "Turkish",
+    "tk": "Turkmen",
+    "uk": "Ukrainian",
+    "ur": "Urdu",
+    "uz": "Uzbek",
+    "vi": "Vietnamese",
+    "cy": "Welsh",
+    "yi": "Yiddish",
+    "yo": "Yoruba",
+}
+
+def get_language_names():
+    return list(LANGUAGES.values())
+
+def get_code_by_name(name):
+    for code, lang in LANGUAGES.items():
+        if lang == name:
+            return code
+    return "auto"
+
+def get_name_by_code(code):
+    return LANGUAGES.get(code, "Auto Detect")

src/core/llm_engine.py

@@ -0,0 +1,185 @@
+"""
+LLM Engine Module.
+==================
+
+Handles interaction with the local Llama 3.2 1B model for transcription correction.
+Uses llama-cpp-python for efficient local inference.
+"""
+
+import os
+import logging
+from typing import Optional
+from src.core.paths import get_models_path
+from src.core.config import ConfigManager
+
+try:
+    from llama_cpp import Llama
+except ImportError:
+    Llama = None
+
+class LLMEngine:
+    """
+    Manages the Llama model and performs text correction/rewriting.
+    """
+    def __init__(self):
+        self.config = ConfigManager()
+        self.model = None
+        self.current_model_path = None
+        
+        # --- Mode 1: Grammar Only (Strict) ---
+        self.prompt_grammar = (
+            "You are a text correction tool. "
+            "Correct the grammar/spelling. Do not change punctuation or capitalization styles. "
+            "Do not remove any words (including profanity). Output ONLY the result."
+            "\n\nExample:\nInput: 'damn it works'\nOutput: 'damn it works'"
+        )
+
+        # --- Mode 2: Standard (Grammar + Punctuation + Caps) ---
+        self.prompt_standard = (
+            "You are a text correction tool. "
+            "Standardize the grammar, punctuation, and capitalization. "
+            "Do not remove any words (including profanity). Output ONLY the result."
+            "\n\nExample:\nInput: 'damn it works'\nOutput: 'Damn it works.'"
+        )
+
+        # --- Mode 3: Rewrite (Tone-Aware Polish) ---
+        self.prompt_rewrite = (
+            "You are a text rewriting tool. Improve flow/clarity but keep the exact tone and vocabulary. "
+            "Do not remove any words (including profanity). Output ONLY the result."
+            "\n\nExample:\nInput: 'damn it works'\nOutput: 'Damn, it works.'"
+        )
+
+    def load_model(self) -> bool:
+        """
+        Loads the LLM model if it exists.
+        Returns True if successful, False otherwise.
+        """
+        if Llama is None:
+            logging.error("llama-cpp-python not installed.")
+            return False
+
+        model_name = self.config.get("llm_model_name", "llama-3.2-1b-instruct")
+        model_dir = get_models_path() / "llm" / model_name
+        model_file = model_dir / "llama-3.2-1b-instruct-q4_k_m.gguf"
+        
+        if not model_file.exists():
+            logging.warning(f"LLM Model not found at: {model_file}")
+            return False
+
+        if self.model and self.current_model_path == str(model_file):
+            return True
+
+        try:
+            logging.info(f"Loading LLM from {model_file}...")
+            n_gpu_layers = 0
+            try:
+                import torch
+                if torch.cuda.is_available():
+                    n_gpu_layers = -1 
+            except:
+                pass
+
+            self.model = Llama(
+                model_path=str(model_file),
+                n_gpu_layers=n_gpu_layers,
+                n_ctx=2048, 
+                verbose=False
+            )
+            self.current_model_path = str(model_file)
+            logging.info("LLM loaded successfully.")
+            return True
+        except Exception as e:
+            logging.error(f"Failed to load LLM: {e}")
+            self.model = None
+            return False
+
+    def correct_text(self, text: str, mode: str = "Standard") -> str:
+        """Corrects or rewrites the provided text."""
+        if not text or not text.strip():
+            return text
+
+        if not self.model:
+            if not self.load_model():
+                return text 
+
+        logging.info(f"LLM Processing ({mode}): '{text}'")
+
+        system_prompt = self.prompt_standard 
+        if mode == "Grammar": system_prompt = self.prompt_grammar
+        elif mode == "Rewrite": system_prompt = self.prompt_rewrite
+        
+        # PREFIX INJECTION TECHNIQUE
+        # We end the prompt with the start of the assistant's answer specifically phrased to force compliance.
+        # "Here is the processed output:" forces it into a completion mode rather than a refusal mode.
+        prefix_injection = "Here is the processed output:\n"
+
+        prompt = (
+            f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|>"
+            f"<|start_header_id|>user<|end_header_id|>\n\nProcess this input:\n{text}<|eot_id|>"
+            f"<|start_header_id|>assistant<|end_header_id|>\n\n{prefix_injection}"
+        )
+
+        try:
+            output = self.model(
+                prompt,
+                max_tokens=512, 
+                stop=["<|eot_id|>"],
+                echo=False,
+                temperature=0.1 
+            )
+            
+            result = output['choices'][0]['text'].strip()
+            
+            # 1. Fallback: If result is empty, it might have just outputted nothing because we prefilled?
+            # Actually llama-cpp-python usually returns the *continuation*.
+            # So if it outputted "My corrected text.", the full logical response is "Here is...: My corrected text."
+            # We just want the result.
+            
+            # Refusal Detection (Safety Net)
+            refusal_triggers = [
+                "I cannot", "I can't", "I am unable", "I apologize", "sorry", 
+                "As an AI", "explicit content", "harmful content", "safety guidelines"
+            ]
+            lower_res = result.lower()
+            if any(trig in lower_res for trig in refusal_triggers) and len(result) < 150:
+                logging.warning(f"LLM Refusal Detected: '{result}'. Falling back to original.")
+                return text # Return original text on refusal!
+            
+            # --- Robust Post-Processing ---
+            
+            # 1. Strip quotes 
+            if result.startswith('"') and result.endswith('"') and len(result) > 2 and '"' not in result[1:-1]:
+                 result = result[1:-1]
+            if result.startswith("'") and result.endswith("'") and len(result) > 2 and "'" not in result[1:-1]:
+                 result = result[1:-1]
+                 
+            # 2. Split by newline
+            if "\n" in result:
+                lines = result.split('\n')
+                clean_lines = [l.strip() for l in lines if l.strip()]
+                if clean_lines:
+                    result = clean_lines[0]
+                    
+            # 3. Aggressive Preamble Stripping (Updates for new prefix)
+            import re
+            prefixes = [
+                r"^Here is the processed output:?\s*", # The one we injected
+                r"^Here is the corrected text:?\s*",
+                r"^Here is the rewritten text:?\s*",
+                r"^Here's the result:?\s*",
+                r"^Sure,? here is regex.*:?\s*",
+                r"^Output:?\s*",
+                r"^Processing result:?\s*",
+            ]
+            
+            for p in prefixes:
+                result = re.sub(p, "", result, flags=re.IGNORECASE).strip()
+            
+            if result.startswith('"') and result.endswith('"') and len(result) > 2 and '"' not in result[1:-1]:
+                 result = result[1:-1]
+                 
+            logging.info(f"LLM Result: '{result}'")
+            return result
+        except Exception as e:
+            logging.error(f"LLM inference failed: {e}")
+            return text # Fail safe logic

src/core/transcriber.py

@@ -15,8 +15,13 @@ import numpy as np
 from src.core.config import ConfigManager
 from src.core.paths import get_models_path
 
+try:
+    import torch
+except ImportError:
+    torch = None
+
 # Import directly - valid since we are now running in the full environment
-from faster_whisper import WhisperModel
+
 
 class WhisperTranscriber:
     """
@@ -57,13 +62,32 @@ class WhisperTranscriber:
             # Force offline if path exists to avoid HF errors
             local_only = new_path.exists()
 
-            self.model = WhisperModel(
+            try:
-                model_input, 
+                from faster_whisper import WhisperModel
-                device=device, 
+                self.model = WhisperModel(
-                compute_type=compute,
+                    model_input, 
-                download_root=str(get_models_path()),
+                    device=device, 
-                local_files_only=local_only
+                    compute_type=compute,
-            )
+                    download_root=str(get_models_path()),
+                    local_files_only=local_only
+                )
+            except Exception as load_err:
+                # CRITICAL FALLBACK: If CUDA/cublas fails (AMD/Intel users), fallback to CPU
+                err_str = str(load_err).lower()
+                if "cublas" in err_str or "cudnn" in err_str or "library" in err_str or "device" in err_str:
+                    logging.warning(f"CUDA Init Failed ({load_err}). Falling back to CPU...")
+                    self.config.set("compute_device", "cpu") # Update config for persistence/UI
+                    self.current_compute_device = "cpu"
+                    
+                    self.model = WhisperModel(
+                        model_input, 
+                        device="cpu", 
+                        compute_type="int8", # CPU usually handles int8 well with newer extensions, or standard
+                        download_root=str(get_models_path()),
+                        local_files_only=local_only
+                    )
+                else:
+                    raise load_err
             
             self.current_model_size = size
             self.current_compute_device = device
@@ -73,42 +97,120 @@ class WhisperTranscriber:
         except Exception as e:
             logging.error(f"Failed to load model: {e}")
             self.model = None
+            
+            # Auto-Repair: Detect vocabulary/corrupt errors
+            err_str = str(e).lower()
+            if "vocabulary" in err_str or "tokenizer" in err_str or "config.json" in err_str:
+                # ... existing auto-repair logic ...
+                logging.warning("Corrupt model detected on load. Attempting to delete and reset...")
+                try:
+                    import shutil
+                    # Differentiate between simple path and HF path
+                    new_path = get_models_path() / f"faster-whisper-{size}"
+                    if new_path.exists():
+                        shutil.rmtree(new_path)
+                        logging.info(f"Deleted corrupt model at {new_path}")
+                    else:
+                        # Try legacy HF path
+                        hf_path = get_models_path() / f"models--Systran--faster-whisper-{size}"
+                        if hf_path.exists():
+                            shutil.rmtree(hf_path)
+                            logging.info(f"Deleted corrupt HF model at {hf_path}")
+                            
+                    # Notify UI to refresh state (will show 'Download' button now)
+                    # We can't reach bridge easily here without passing it in, 
+                    # but the UI polls or listens to logs. 
+                    # The user will simply see "Model Missing" in settings after this.
+                except Exception as del_err:
+                    logging.error(f"Failed to delete corrupt model: {del_err}")
 
-    def transcribe(self, audio_data, is_file: bool = False) -> str:
+    def transcribe(self, audio_data, is_file: bool = False, task: Optional[str] = None) -> str:
         """
         Transcribe audio data.
         """
-        logging.info(f"Starting transcription... (is_file={is_file})")
+        logging.info(f"Starting transcription... (is_file={is_file}, task={task})")
         
         # Ensure model is loaded
         if not self.model:
             self.load_model()
             if not self.model:
-                return "Error: Model failed to load."
+                return "Error: Model failed to load. Please check Settings -> Model Info."
 
         try:
             # Config
             beam_size = int(self.config.get("beam_size"))
             best_of = int(self.config.get("best_of"))
             vad = False if is_file else self.config.get("vad_filter")
+            language = self.config.get("language")
+            
+            # Use task override if provided, otherwise config
+            # Ensure safe string and lowercase ("transcribe" vs "Transcribe")
+            raw_task = task if task else self.config.get("task")
+            final_task = str(raw_task).strip().lower() if raw_task else "transcribe"
+            
+            # Sanity check for valid Whisper tasks
+            if final_task not in ["transcribe", "translate"]:
+                logging.warning(f"Invalid task '{final_task}' detected. Defaulting to 'transcribe'.")
+                final_task = "transcribe"
+            
+            # Language handling
+            final_language = language if language != "auto" else None
+            
+            # Anti-Hallucination: Force condition_on_previous_text=False for translation
+            condition_prev = self.config.get("condition_on_previous_text")
+            
+            # Helper options for Translation Stability
+            initial_prompt = self.config.get("initial_prompt")
+            
+            if final_task == "translate":
+                condition_prev = False
+                # Force beam search if user has set it to greedy (1)
+                # Translation requires more search breadth to find the English mapping
+                if beam_size < 5:
+                    logging.info("Forcing beam_size=5 for Translation task.")
+                    beam_size = 5
+                    
+                # Inject guidance prompt if none exists
+                if not initial_prompt:
+                    initial_prompt = "Translate this to English."
+
+            logging.info(f"Model Dispatch: Task='{final_task}', Language='{final_language}', ConditionPrev={condition_prev}, Beam={beam_size}")
+            
+            # Build arguments dynamically to avoid passing None if that's the issue
+            transcribe_opts = {
+                "beam_size": beam_size,
+                "best_of": best_of,
+                "vad_filter": vad,
+                "task": final_task,
+                "vad_parameters": dict(min_silence_duration_ms=500),
+                "condition_on_previous_text": condition_prev,
+                "without_timestamps": True
+            }
+            
+            if initial_prompt:
+                 transcribe_opts["initial_prompt"] = initial_prompt
+            
+            # Only add language if it's explicitly set (not None/Auto)
+            # This avoids potentially confusing the model with explicit None
+            if final_language:
+                transcribe_opts["language"] = final_language
             
             # Transcribe
-            segments, info = self.model.transcribe(
+            segments, info = self.model.transcribe(audio_data, **transcribe_opts)
-                audio_data,
-                beam_size=beam_size,
-                best_of=best_of,
-                vad_filter=vad,
-                vad_parameters=dict(min_silence_duration_ms=500),
-                condition_on_previous_text=self.config.get("condition_on_previous_text"),
-                without_timestamps=True
-            )
             
             # Aggregate text
             text_result = ""
             for segment in segments:
                 text_result += segment.text + " "
                 
-            return text_result.strip()
+            text_result = text_result.strip()
+            
+            # Low VRAM Mode: Unload Whisper Model immediately
+            if self.config.get("unload_models_after_use"):
+                self.unload_model()
+
+            logging.info(f"Final Transcription Output: '{text_result}'")
+            return text_result
             
         except Exception as e:
             logging.error(f"Transcription failed: {e}")
@@ -117,8 +219,11 @@ class WhisperTranscriber:
     def model_exists(self, size: str) -> bool:
         """Checks if a model size is already downloaded."""
         new_path = get_models_path() / f"faster-whisper-{size}"
-        if (new_path / "config.json").exists():
+        if new_path.exists():
-            return True
+            # Strict check
+            required = ["config.json", "model.bin", "vocabulary.json"]
+            if all((new_path / f).exists() for f in required):
+                return True
             
         # Legacy HF cache check
         folder_name = f"models--Systran--faster-whisper-{size}"
@@ -127,3 +232,21 @@ class WhisperTranscriber:
             return True
             
         return False
+
+    def unload_model(self):
+        """
+        Unloads model to free memory.
+        """
+        if self.model:
+            del self.model
+        
+        self.model = None
+        self.current_model_size = None
+        
+        # Force garbage collection
+        import gc
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            
+        logging.info("Whisper Model unloaded (Low VRAM Mode).")

src/ui/bridge.py

@@ -110,6 +110,7 @@ class UIBridge(QObject):
     logAppended = Signal(str) # Emits new log line
     settingChanged = Signal(str, 'QVariant')
     modelStatesChanged = Signal() # Notify UI to re-check isModelDownloaded
+    llmDownloadRequested = Signal()
 
     def __init__(self, parent=None):
         super().__init__(parent)
@@ -245,6 +246,26 @@ class UIBridge(QObject):
 
     # --- Methods called from QML ---
     
+    @Slot(result=list)
+    def get_supported_languages(self):
+        from src.core.languages import get_language_names
+        return get_language_names()
+
+    @Slot(str)
+    def set_language_by_name(self, name):
+        from src.core.languages import get_code_by_name
+        from src.core.config import ConfigManager
+        code = get_code_by_name(name)
+        ConfigManager().set("language", code)
+        self.settingChanged.emit("language", code)
+        
+    @Slot(result=str)
+    def get_current_language_name(self):
+        from src.core.languages import get_name_by_code
+        from src.core.config import ConfigManager
+        code = ConfigManager().get("language")
+        return get_name_by_code(code)
+
     @Slot(str, result='QVariant')
     def getSetting(self, key):
         from src.core.config import ConfigManager
@@ -336,11 +357,7 @@ class UIBridge(QObject):
         except Exception as e:
             logging.error(f"Failed to preload audio devices: {e}")
 
-    @Slot()
+
-    def toggle_recording(self):
-        """Called by UI elements to trigger the app's recording logic."""
-        # This will be connected to the main app's toggle logic
-        pass 
     @Property(bool, notify=isDownloadingChanged)
     def isDownloading(self): return self._is_downloading
 
@@ -356,27 +373,39 @@ class UIBridge(QObject):
         
         try:
             from src.core.paths import get_models_path
+            
+
+
             # Check new simple format used by DownloadWorker
             path_simple = get_models_path() / f"faster-whisper-{size}"
-            if path_simple.exists() and any(path_simple.iterdir()):
+            if path_simple.exists():
-                return True
+                # Strict check: Ensure all critical files exist
+                required = ["config.json", "model.bin", "vocabulary.json"]
+                if all((path_simple / f).exists() for f in required):
+                    return True
             
             # Check HF Cache format (legacy/default)
             folder_name = f"models--Systran--faster-whisper-{size}"
             path_hf = get_models_path() / folder_name
             snapshots = path_hf / "snapshots"
             if snapshots.exists() and any(snapshots.iterdir()):
-                return True
+                return True # Legacy cache structure is complex, assume valid if present
-                
-            # Check direct folder (simple)
-            path_direct = get_models_path() / size
-            if (path_direct / "config.json").exists():
-                return True
                 
+            return False
+            
         except Exception as e:
             logging.error(f"Error checking model status: {e}")
-            
+            return False
-        return False
+
+    @Slot(result=bool)
+    def isLLMModelDownloaded(self):
+        try:
+            from src.core.paths import get_models_path
+            # Hardcoded check for the 1B model we support
+            model_file = get_models_path() / "llm" / "llama-3.2-1b-instruct" / "llama-3.2-1b-instruct-q4_k_m.gguf"
+            return model_file.exists()
+        except:
+            return False
 
     @Slot(str)
     def downloadModel(self, size):
@@ -385,3 +414,7 @@ class UIBridge(QObject):
     @Slot()
     def notifyModelStatesChanged(self):
         self.modelStatesChanged.emit()
+
+    @Slot()
+    def downloadLLM(self):
+        self.llmDownloadRequested.emit()

src/ui/qml/ModernComboBox.qml

@@ -100,7 +100,7 @@ ComboBox {
     popup: Popup {
         y: control.height - 1
         width: control.width
-        implicitHeight: contentItem.implicitHeight
+        implicitHeight: Math.min(contentItem.implicitHeight, 300)
         padding: 5
 
         contentItem: ListView {

src/ui/qml/ModernKeySequenceRecorder.qml

@@ -25,7 +25,7 @@ Rectangle {
     
     Text {
         anchors.centerIn: parent
-        text: control.recording ? "Listening..." : (control.currentSequence || "None")
+        text: control.recording ? "Listening..." : (formatSequence(control.currentSequence) || "None")
         color: control.recording ? SettingsStyle.accent : (control.currentSequence ? "#ffffff" : "#808080")
         font.family: "JetBrains Mono"
         font.pixelSize: 13
@@ -72,6 +72,23 @@ Rectangle {
         if (!activeFocus) control.recording = false
     }
 
+    function formatSequence(seq) {
+        if (!seq) return ""
+        var parts = seq.split("+")
+        for (var i = 0; i < parts.length; i++) {
+            var p = parts[i]
+            // Standardize modifiers
+            if (p === "ctrl") parts[i] = "Ctrl"
+            else if (p === "alt") parts[i] = "Alt"
+            else if (p === "shift") parts[i] = "Shift"
+            else if (p === "win") parts[i] = "Win"
+            else if (p === "esc") parts[i] = "Esc"
+            // Capitalize F-keys and others (e.g. f8 -> F8, space -> Space)
+            else parts[i] = p.charAt(0).toUpperCase() + p.slice(1)
+        }
+        return parts.join(" + ")
+    }
+
     function getKeyName(key, text) {
         // F-Keys
         if (key >= Qt.Key_F1 && key <= Qt.Key_F35) return "f" + (key - Qt.Key_F1 + 1)

src/ui/qml/Settings.qml

@@ -314,14 +314,34 @@ Window {
                                     spacing: 0
                                     
                                     ModernSettingsItem {
-                                        label: "Global Hotkey"
+                                        label: "Global Hotkey (Transcribe)"
-                                        description: "Press to record a new shortcut (e.g. Ctrl+Space)"
+                                        description: "Standard: Raw transcription"
                                         control: ModernKeySequenceRecorder {
-                                            Layout.preferredWidth: 200
+                                            implicitWidth: 240
                                             currentSequence: ui.getSetting("hotkey")
                                             onSequenceChanged: (seq) => ui.setSetting("hotkey", seq)
                                         }
                                     }
+
+                                    ModernSettingsItem {
+                                        label: "Global Hotkey (Correct)"
+                                        description: "Enhanced: Transcribe + AI Correction"
+                                        control: ModernKeySequenceRecorder {
+                                            implicitWidth: 240
+                                            currentSequence: ui.getSetting("hotkey_correct")
+                                            onSequenceChanged: (seq) => ui.setSetting("hotkey_correct", seq)
+                                        }
+                                    }
+
+                                    ModernSettingsItem {
+                                        label: "Global Hotkey (Translate)"
+                                        description: "Press to record a new shortcut (e.g. F10)"
+                                        control: ModernKeySequenceRecorder {
+                                            implicitWidth: 240
+                                            currentSequence: ui.getSetting("hotkey_translate")
+                                            onSequenceChanged: (seq) => ui.setSetting("hotkey_translate", seq)
+                                        }
+                                    }
                                     
                                     ModernSettingsItem {
                                         label: "Run on Startup"
@@ -349,8 +369,8 @@ Window {
                                         showSeparator: false
                                         control: ModernSlider {
                                             Layout.preferredWidth: 200
-                                            from: 10; to: 6000
+                                            from: 10; to: 20000
-                                            stepSize: 10
+                                            stepSize: 100
                                             snapMode: Slider.SnapAlways
                                             value: ui.getSetting("typing_speed")
                                             onMoved: ui.setSetting("typing_speed", value)
@@ -577,6 +597,53 @@ Window {
                                 Text { text: "Model configuration and performance"; color: SettingsStyle.textSecondary; font.family: mainFont; font.pixelSize: 14 }
                             }
 
+                                    ModernSettingsSection {
+                                title: "Style & Prompting"
+                                Layout.margins: 32
+                                Layout.topMargin: 0
+                                
+                                content: ColumnLayout {
+                                    width: parent.width
+                                    spacing: 0
+                                    
+                                    ModernSettingsItem {
+                                        label: "Punctuation Style"
+                                        description: "Hint for how to format text"
+                                        control: ModernComboBox {
+                                            id: styleCombo
+                                            width: 180
+                                            model: ["Standard (Proper)", "Casual (Lowercase)", "Custom"]
+                                            
+                                            // Logic to determine initial index based on config string
+                                            Component.onCompleted: {
+                                                let current = ui.getSetting("initial_prompt")
+                                                if (current === "Mm-hmm. Okay, let's go. I speak in full sentences.") currentIndex = 0
+                                                else if (current === "um, okay... i guess so.") currentIndex = 1
+                                                else currentIndex = 2
+                                            }
+
+                                            onActivated: {
+                                                if (index === 0) ui.setSetting("initial_prompt", "Mm-hmm. Okay, let's go. I speak in full sentences.")
+                                                else if (index === 1) ui.setSetting("initial_prompt", "um, okay... i guess so.")
+                                                // Custom: Don't change string immediately, let user type
+                                            }
+                                        }
+                                    }
+
+                                    ModernSettingsItem {
+                                        label: "Custom Prompt"
+                                        description: "Advanced: Define your own style hint"
+                                        visible: styleCombo.currentIndex === 2
+                                        control: ModernTextField {
+                                            Layout.preferredWidth: 280
+                                            placeholderText: "e.g. 'Hello, World.'"
+                                            text: ui.getSetting("initial_prompt") || ""
+                                            onEditingFinished: ui.setSetting("initial_prompt", text === "" ? null : text)
+                                        }
+                                    }
+                                }
+                            }
+
                             ModernSettingsSection {
                                 title: "Model Config"
                                 Layout.margins: 32
@@ -742,15 +809,17 @@ Window {
                                     
                                     ModernSettingsItem {
                                         label: "Language"
-                                        description: "Force language or Auto-detect"
+                                        description: "Spoken language to transcribe"
                                         control: ModernComboBox {
-                                            width: 140
+                                            Layout.preferredWidth: 200
-                                            model: ["auto", "en", "fr", "de", "es", "it", "ja", "zh", "ru"]
+                                            model: ui.get_supported_languages()
-                                            currentIndex: model.indexOf(ui.getSetting("language"))
+                                            currentIndex: model.indexOf(ui.get_current_language_name())
-                                            onActivated: ui.setSetting("language", currentText)
+                                            onActivated: (index) => ui.set_language_by_name(currentText)
                                         }
                                     }
-
+                            
+                                    // Task selector removed as per user request (Hotkeys handle this now)
+                            
                                     ModernSettingsItem {
                                         label: "Compute Device"
                                         description: "Hardware acceleration (CUDA requires NVidia GPU)"
@@ -773,6 +842,147 @@ Window {
                                             onActivated: ui.setSetting("compute_type", currentText)
                                         }
                                     }
+                                    
+                                    ModernSettingsItem {
+                                        label: "Low VRAM Mode"
+                                        description: "Unload models immediately after use (Saves VRAM, Adds Delay)"
+                                        showSeparator: false
+                                        control: ModernSwitch {
+                                            checked: ui.getSetting("unload_models_after_use")
+                                            onToggled: ui.setSetting("unload_models_after_use", checked)
+                                        }
+                                    }
+                                }
+                            }
+
+                            ModernSettingsSection {
+                                title: "Correction & Rewriting"
+                                Layout.margins: 32
+                                Layout.topMargin: 0
+                                
+                                content: ColumnLayout {
+                                    width: parent.width
+                                    spacing: 0
+                                    
+                                    ModernSettingsItem {
+                                        label: "Enable Correction"
+                                        description: "Post-process text with Llama 3.2 1B (Adds latency)"
+                                        control: ModernSwitch {
+                                            checked: ui.getSetting("llm_enabled")
+                                            onToggled: ui.setSetting("llm_enabled", checked)
+                                        }
+                                    }
+
+                                    ModernSettingsItem {
+                                        label: "Correction Mode"
+                                        description: "Grammar Fix vs. Complete Rewrite"
+                                        visible: ui.getSetting("llm_enabled")
+                                        control: ModernComboBox {
+                                            width: 140
+                                            model: ["Grammar", "Standard", "Rewrite"]
+                                            currentIndex: model.indexOf(ui.getSetting("llm_mode"))
+                                            onActivated: ui.setSetting("llm_mode", currentText)
+                                        }
+                                    }
+
+                                    // LLM Model Status Card
+                                    Rectangle {
+                                        Layout.fillWidth: true
+                                        Layout.margins: 12
+                                        Layout.topMargin: 0
+                                        Layout.bottomMargin: 16
+                                        height: 54
+                                        color: "#0a0a0f"
+                                        visible: ui.getSetting("llm_enabled")
+                                        radius: 6
+                                        border.color: SettingsStyle.borderSubtle
+                                        border.width: 1
+
+                                        property bool isDownloaded: false
+                                        property bool isDownloading: ui.isDownloading && ui.statusText.indexOf("LLM") !== -1
+
+                                        Timer {
+                                            interval: 2000
+                                            running: visible
+                                            repeat: true
+                                            onTriggered: parent.checkStatus()
+                                        }
+                                        
+                                        function checkStatus() {
+                                            isDownloaded = ui.isLLMModelDownloaded()
+                                        }
+                                        
+                                        Component.onCompleted: checkStatus()
+                                        
+                                        Connections {
+                                            target: ui
+                                            function onModelStatesChanged() { parent.checkStatus() }
+                                            function onIsDownloadingChanged() { parent.checkStatus() } 
+                                        }
+
+                                        RowLayout {
+                                            anchors.fill: parent
+                                            anchors.leftMargin: 12
+                                            anchors.rightMargin: 12
+                                            spacing: 12
+                                            
+                                            Image {
+                                                source: "smart_toy.svg"
+                                                sourceSize: Qt.size(16, 16)
+                                                layer.enabled: true
+                                                layer.effect: MultiEffect {
+                                                    colorization: 1.0
+                                                    colorizationColor: parent.parent.isDownloaded ? SettingsStyle.accent : "#808080"
+                                                }
+                                            }
+
+                                            ColumnLayout {
+                                                Layout.fillWidth: true
+                                                spacing: 2
+                                                Text {
+                                                    text: "Llama 3.2 1B (Instruct)"
+                                                    color: "#ffffff"
+                                                    font.family: "JetBrains Mono"; font.bold: true
+                                                    font.pixelSize: 11
+                                                }
+                                                Text {
+                                                    text: parent.parent.isDownloaded ? "Ready." : "Model missing (~1.2GB)"
+                                                    color: SettingsStyle.textSecondary
+                                                    font.family: "JetBrains Mono"; font.pixelSize: 10
+                                                }
+                                            }
+                                            
+                                            Button {
+                                                id: dlBtn
+                                                text: "Download"
+                                                visible: !parent.parent.isDownloaded && !parent.parent.isDownloading
+                                                Layout.preferredHeight: 24
+                                                Layout.preferredWidth: 80
+                                                
+                                                contentItem: Text {
+                                                    text: "DOWNLOAD"
+                                                    font.pixelSize: 10; font.bold: true; color: "#000000"; horizontalAlignment: Text.AlignHCenter; verticalAlignment: Text.AlignVCenter
+                                                }
+                                                background: Rectangle {
+                                                    color: dlBtn.hovered ? "#ffffff" : SettingsStyle.accent; radius: 4
+                                                }
+                                                onClicked: ui.downloadLLM()
+                                            }
+
+                                            // Progress Bar
+                                            Rectangle {
+                                                visible: parent.parent.isDownloading
+                                                Layout.fillWidth: true
+                                                height: 4
+                                                color: "#30ffffff"
+                                                Rectangle {
+                                                    width: parent.width * (ui.downloadProgress / 100)
+                                                    height: parent.height
+                                                    color: SettingsStyle.accent
+                                                }
+                                            }
+                                        }
+                                    }
                                 }
                             }
 

src/utils/formatters.py

@@ -0,0 +1,32 @@
+"""
+Formatter Utilities
+===================
+Helper functions for text formatting.
+"""
+
+def format_hotkey(sequence: str) -> str:
+    """
+    Formats a hotkey sequence string (e.g. 'ctrl+alt+f9') 
+    into a pretty readable string (e.g. 'Ctrl + Alt + F9').
+    """
+    if not sequence:
+        return "None"
+    
+    parts = sequence.split('+')
+    formatted_parts = []
+    
+    for p in parts:
+        p = p.strip().lower()
+        if p == 'ctrl': formatted_parts.append('Ctrl')
+        elif p == 'alt': formatted_parts.append('Alt')
+        elif p == 'shift': formatted_parts.append('Shift')
+        elif p == 'win': formatted_parts.append('Win')
+        elif p == 'esc': formatted_parts.append('Esc')
+        else:
+            # Capitalize first letter
+            if len(p) > 0:
+                formatted_parts.append(p[0].upper() + p[1:])
+            else:
+                formatted_parts.append(p)
+                
+    return " + ".join(formatted_parts)

src/utils/window_hook.py

@@ -55,6 +55,10 @@ except AttributeError:
 def LOWORD(l): return l & 0xffff
 def HIWORD(l): return (l >> 16) & 0xffff
 
+GWL_EXSTYLE = -20
+WS_EX_TRANSPARENT = 0x00000020
+WS_EX_LAYERED = 0x00080000
+
 class WindowHook:
     def __init__(self, hwnd, width, height, initial_scale=1.0):
         self.hwnd = hwnd
@@ -65,6 +69,34 @@ class WindowHook:
         # (Window 420x140, Pill 380x100)
         self.logical_rect = [20, 20, 20+380, 20+100]
         self.current_scale = initial_scale
+        self.enabled = True # New flag
+
+    def set_enabled(self, enabled):
+        """
+        Enables or disables interaction.
+        When disabled, we set WS_EX_TRANSPARENT so clicks pass through physically.
+        """
+        if self.enabled == enabled:
+            return
+            
+        self.enabled = enabled
+        
+        # Get current styles
+        style = user32.GetWindowLongW(self.hwnd, GWL_EXSTYLE)
+        
+        if not enabled:
+            # Enable Click-Through (Add Transparent)
+            # We also ensure Layered is set (Qt usually sets it, but good to be sure)
+            new_style = style | WS_EX_TRANSPARENT | WS_EX_LAYERED
+        else:
+            # Disable Click-Through (Remove Transparent)
+            new_style = style & ~WS_EX_TRANSPARENT
+            
+        if new_style != style:
+            SetWindowLongPtr(self.hwnd, GWL_EXSTYLE, new_style)
+            
+        # Force a redraw/frame update just in case
+        user32.SetWindowPos(self.hwnd, 0, 0, 0, 0, 0, 0x0027) # SWP_NOMOVE | SWP_NOSIZE | SWP_NOZORDER | SWP_FRAMECHANGED
 
     def install(self):
         proc_address = ctypes.cast(self.new_wnd_proc, ctypes.c_void_p)
@@ -73,6 +105,10 @@ class WindowHook:
     def wnd_proc_callback(self, hwnd, msg, wParam, lParam):
         try:
             if msg == WM_NCHITTEST:
+                # If disabled (invisible/inactive), let clicks pass through (HTTRANSPARENT)
+                if not self.enabled:
+                    return HTTRANSPARENT
+                    
                 res = self.on_nchittest(lParam)
                 if res != 0: 
                     return res

test_m2m.py

@@ -0,0 +1,38 @@
+
+import sys
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+
+def test_m2m():
+    model_name = "facebook/m2m100_418M"
+    print(f"Loading {model_name}...")
+    
+    tokenizer = M2M100Tokenizer.from_pretrained(model_name)
+    model = M2M100ForConditionalGeneration.from_pretrained(model_name)
+    
+    # Test cases: (Language Code, Input)
+    test_cases = [
+        ("en", "he go to school yesterday"),
+        ("pl", "on iść do szkoła wczoraj"), # Intentional broken grammar in Polish
+    ]
+    
+    print("\nStarting M2M Tests (Self-Translation):\n")
+    
+    for lang, input_text in test_cases:
+        tokenizer.src_lang = lang
+        encoded = tokenizer(input_text, return_tensors="pt")
+        
+        # Translate to SAME language
+        generated_tokens = model.generate(
+            **encoded, 
+            forced_bos_token_id=tokenizer.get_lang_id(lang)
+        )
+        
+        corrected = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+        
+        print(f"[{lang}]")
+        print(f"Input:    {input_text}")
+        print(f"Output:   {corrected}")
+        print("-" * 20)
+
+if __name__ == "__main__":
+    test_m2m()

test_mt0.py

@@ -0,0 +1,40 @@
+
+import sys
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+def test_mt0():
+    model_name = "bigscience/mt0-base"
+    print(f"Loading {model_name}...")
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    
+    # Test cases: (Language, Prompt, Input)
+    # MT0 is instruction tuned, so we should prompt it in the target language or English.
+    # Cross-lingual prompting (English prompt -> Target tasks) is usually supported.
+    
+    test_cases = [
+        ("English", "Correct grammar:", "he go to school yesterday"),
+        ("Polish", "Popraw gramatykę:", "to jest testowe zdanie bez kropki"),
+        ("Finnish", "Korjaa kielioppi:", "tämä on testilause ilman pistettä"),
+        ("Russian", "Исправь грамматику:", "это тестовое предложение без точки"),
+        ("Japanese", "文法を直してください:", "これは点のないテスト文です"),
+        ("Spanish", "Corrige la gramática:", "esta es una oración de prueba sin punto"),
+    ]
+    
+    print("\nStarting MT0 Tests:\n")
+    
+    for lang, prompt_text, input_text in test_cases:
+        full_input = f"{prompt_text} {input_text}"
+        inputs = tokenizer(full_input, return_tensors="pt")
+        
+        outputs = model.generate(inputs.input_ids, max_length=128)
+        corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        print(f"[{lang}]")
+        print(f"Input:    {full_input}")
+        print(f"Output:   {corrected}")
+        print("-" * 20)
+
+if __name__ == "__main__":
+    test_mt0()

test_punctuation.py

@@ -0,0 +1,34 @@
+
+import sys
+import os
+
+# Add src to path
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from src.core.grammar_assistant import GrammarAssistant
+
+def test_punctuation():
+    assistant = GrammarAssistant()
+    assistant.load_model()
+    
+    samples = [
+        # User's example (verbatim)
+        "If the voice recognition doesn't recognize that I like stopped Or something would that would it also correct that",
+        
+        # Generic run-on
+        "hello how are you doing today i am doing fine thanks for asking",
+        
+        # Missing commas/periods
+        "well i think its valid however we should probably check the logs first"
+    ]
+    
+    print("\nStarting Punctuation Tests:\n")
+    
+    for sample in samples:
+        print(f"Original: {sample}")
+        corrected = assistant.correct(sample)
+        print(f"Corrected: {corrected}")
+        print("-" * 20)
+
+if __name__ == "__main__":
+    test_punctuation()