README.md

🛡️ Police Bot Runtime — AI Voice Assistant for Bengaluru Police
This is the runtime layer of an AI-powered mental wellness assistant built for frontline Bengaluru Police officers. The assistant runs locally and privately using an LLM (via Ollama) and a fine-tuned voice cloning TTS model (via XTTS/F5-TTS), producing empathetic voice replies in real-time based on officer input.

💬 Text In → 🤖 LLM Reply → 🗣️ Voice Cloned Output

📌 Project Goals
Create a voice-first wellness chatbot for police personnel

Fully private, runs entirely offline on powerful local machines

Replies are generated by an open-source LLM (police-bot) via Ollama

Responses are spoken aloud using a fine-tuned XTTS voice cloned from a real speaker

Easily extendable to integrate into a React-based web interface later

Future support for Kannada via multi-lingual XTTS fine-tuning

🧠 System Overview
This repo powers the runtime experience.

Ollama runs an LLM (LLaMA 3, Mistral etc.) via the police-bot model

Python script police_runtime.py communicates with Ollama (localhost:11434)

LLM reply is sent to police_bot_voice.py

XTTS reads a voice reference and generates a realistic audio response (output.wav)

Audio is played back to the officer

Example flow:

Officer: I'm feeling low today
Assistant: [spoken aloud] Namaskara! I’m here to support you. You're a valued member of the force...

📂 Folder Structure
Your folder layout should look like:

police-bot-runtime/
│
├── police_runtime.py # Main loop: user input → LLM → voice
├── police_bot_voice.py # Loads XTTS model, speaks response
│
├── my_finetuned_model/ # XTTS fine-tuned model files
│ ├── config.json
│ ├── dvae.ptj
│ ├── mel_stats.pth
│ ├── model.pth
│ └── vocab.json
│
├── datasets-1/
│ └── wavs/
│ └── 0029.wav # Reference voice clip used for inference
│
├── venv/ # Python virtual environment
└── requirements.txt # (Optional) Dependency list

⚙️ Setup Instructions
🧩 Prerequisites:

Windows 10/11, 64-bit

Python 3.11 (recommended)

Ollama installed: https://ollama.com

Trained XTTS voice model (via F5-TTS or Coqui)

Clone or copy this folder as police-bot-runtime

Create a virtual environment:

bash
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python -m venv venv
venv\Scripts\activate
Install dependencies:

bash
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pip install TTS requests
Start the Ollama LLM (separate terminal):

bash
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ollama run police-bot
Run the voice assistant:

bash
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python police_runtime.py
You'll be prompted for input. Replies will be printed and voiced out.

🧪 Testing Voice Model
Your test_voice.py should look like:

python
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from TTS.api import TTS

tts = TTS(
    model_path="my_finetuned_model/",
    config_path="my_finetuned_model/config.json",
    gpu=True
)

tts.tts_to_file(
    text="Hey officer, everything okay?",
    file_path="output.wav",
    speaker_wav="datasets-1/wavs/0029.wav",
    language="en"
)
🧠 How It Works
Ollama LLM (LLaMA3) is launched with ollama run police-bot

Text prompt is sent to http://localhost:11434/api/generate

Response is sent to XTTS model for synthesis

XTTS clones voice using speaker_wav file

Audio saved to output.wav and played using playsound or equivalent

🛠️ Future Enhancements
 Integrate directly with the frontend (React + ShadCN + Web Speech API)

 Support both English and Kannada voice cloning

 Add emotional sentiment analysis (LLaMA + classifier)

 Handle stress journaling and daily wellness logging

 Auto-log sessions to Supabase or local DB

 Deploy offline on a rugged laptop for field use

🙏 Acknowledgements
Voice cloning: F5-TTS / Coqui XTTS

LLM inference: Ollama + LLaMA3 8B

Frontend inspiration: GovSchemes project

This is a labor of respect and service. Designed to support those who protect us.

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