| Name | mythic-lite JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A lightweight, local AI chatbot system with text-to-speech capabilities |
| upload_time | 2025-09-05 15:46:03 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
ai
chatbot
llm
tts
asr
local
privacy
offline
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Mythic-Lite
## 🪄 What is Mythic? (the non-boring version)
Mythic is my playground for weird AI ideas.
Think: Roblox clowns, cursed TikTok experiments, and NPCs with emotional damage.
Yes, it’s half-baked. But half-baked on purpose.
Runs locally, talks back, and might roast you if you let it.
Right now it’s roleplaying as a 19th-century mercenary who talks funny.
## 🚀 Features
- **Local AI Processing**: Run completely offline using local LLM models
- **Text-to-Speech**: Natural voice synthesis with customizable voices
- **Speech Recognition**: Lightweight offline ASR system using Vosk for voice input
- **Conversation Memory**: Intelligent conversation management with automatic summarization
- **Beautiful CLI Interface**: Modern, intuitive command-line interface with rich output
- **Modular Architecture**: Separate workers for LLM, TTS, ASR, and summarization tasks
- **Rich Logging**: Comprehensive logging with configurable output formats
- **Environment Configuration**: Flexible configuration via environment variables
- **Automated Setup**: One-click environment setup with virtual environment and dependencies
## 🏗️ Architecture
Mythic-Lite uses a modular architecture with specialized workers:
- **Chatbot Orchestrator**: Coordinates all components and manages conversation flow
- **LLM Worker**: Handles language model inference and text generation
- **TTS Worker**: Manages text-to-speech synthesis
- **ASR Worker**: Handles automatic speech recognition for voice input
- **Summarization Worker**: Handles conversation summarization for memory management
- **Conversation Worker**: Manages conversation state and memory
## 📋 Requirements
- Python 3.8+
- Windows 10/11, Linux (Ubuntu 18.04+), or macOS 10.15+
- At least 8GB RAM (16GB+ recommended)
- Sufficient storage for model files (~4-8GB)
- Audio input/output capabilities
## 📁 Project Structure
```
mythic-lite/
├── src/mythic_lite/ # Main package source code
│ ├── core/ # Core components (orchestrator, config, etc.)
│ ├── workers/ # Specialized AI workers (LLM, TTS, ASR, etc.)
│ ├── utils/ # Utilities (CLI, logging, etc.)
│ └── scripts/ # Setup and utility scripts
├── tests/ # Test suite
├── docs/ # Documentation
├── examples/ # Usage examples
├── scripts/ # Installation and startup scripts
├── pyproject.toml # Modern Python packaging configuration
├── requirements.txt # Runtime dependencies
├── requirements-dev.txt # Development dependencies
└── README.md # This file
```
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"description": "# Mythic-Lite\n\n## \ud83e\ude84 What is Mythic? (the non-boring version)\n\nMythic is my playground for weird AI ideas. \nThink: Roblox clowns, cursed TikTok experiments, and NPCs with emotional damage. \n\nYes, it\u2019s half-baked. But half-baked on purpose. \nRuns locally, talks back, and might roast you if you let it. \n\nRight now it\u2019s roleplaying as a 19th-century mercenary who talks funny.\n\n## \ud83d\ude80 Features\n\n- **Local AI Processing**: Run completely offline using local LLM models\n- **Text-to-Speech**: Natural voice synthesis with customizable voices\n- **Speech Recognition**: Lightweight offline ASR system using Vosk for voice input\n- **Conversation Memory**: Intelligent conversation management with automatic summarization\n- **Beautiful CLI Interface**: Modern, intuitive command-line interface with rich output\n- **Modular Architecture**: Separate workers for LLM, TTS, ASR, and summarization tasks\n- **Rich Logging**: Comprehensive logging with configurable output formats\n- **Environment Configuration**: Flexible configuration via environment variables\n- **Automated Setup**: One-click environment setup with virtual environment and dependencies\n\n## \ud83c\udfd7\ufe0f Architecture\n\nMythic-Lite uses a modular architecture with specialized workers:\n\n- **Chatbot Orchestrator**: Coordinates all components and manages conversation flow\n- **LLM Worker**: Handles language model inference and text generation\n- **TTS Worker**: Manages text-to-speech synthesis\n- **ASR Worker**: Handles automatic speech recognition for voice input\n- **Summarization Worker**: Handles conversation summarization for memory management\n- **Conversation Worker**: Manages conversation state and memory\n\n## \ud83d\udccb Requirements\n\n- Python 3.8+\n- Windows 10/11, Linux (Ubuntu 18.04+), or macOS 10.15+\n- At least 8GB RAM (16GB+ recommended)\n- Sufficient storage for model files (~4-8GB)\n- Audio input/output capabilities\n\n\n## \ud83d\udcc1 Project Structure\n\n```\nmythic-lite/\n\u251c\u2500\u2500 src/mythic_lite/ # Main package source code\n\u2502 \u251c\u2500\u2500 core/ # Core components (orchestrator, config, etc.)\n\u2502 \u251c\u2500\u2500 workers/ # Specialized AI workers (LLM, TTS, ASR, etc.)\n\u2502 \u251c\u2500\u2500 utils/ # Utilities (CLI, logging, etc.)\n\u2502 \u2514\u2500\u2500 scripts/ # Setup and utility scripts\n\u251c\u2500\u2500 tests/ # Test suite\n\u251c\u2500\u2500 docs/ # Documentation\n\u251c\u2500\u2500 examples/ # Usage examples\n\u251c\u2500\u2500 scripts/ # Installation and startup scripts\n\u251c\u2500\u2500 pyproject.toml # Modern Python packaging configuration\n\u251c\u2500\u2500 requirements.txt # Runtime dependencies\n\u251c\u2500\u2500 requirements-dev.txt # Development dependencies\n\u2514\u2500\u2500 README.md # This file\n```\n",
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