AI-Powered Voice Processing & Case Prediction

This project is an advanced AI-driven solution for voice processing and case prediction. It enables automated transcription, translation, and case classification to enhance efficiency in call management systems.

Features

• Voice Recognition: Converts speech to text using AI-driven speech-to-text models.
• Translation: Translates transcribed text into English to support multilingual users.
• NLP-Based Case Prediction: Uses Natural Language Processing (NLP) to classify cases and predict outcomes.
• Workflow Automation: Automates processing using Celery and other orchestration tools.
• Data Storage & Visualization: Stores processed data in MinIO/S3 and provides visual analytics.

AI Trainer

We use an AI Trainer to fine-tune our models for transcription, translation, and case prediction.
🔗 AI Trainer

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Repository Structure

1. Core Components

📂 data_pipeline/

Handles the full data processing workflow:

• ingestion/ – Fetches and prepares raw voice data.
• transcription/ – Converts speech into text.
• translation/ – Translates non-English text.
• nlp/ – Applies NLP models for classification.
• orchestration/ – Manages pipeline tasks using Celery.
• storage/ – Handles MinIO/S3 data storage.

📂 models/

AI models used for voice processing:

• voice_recognition/ – Speech-to-text models.
• translation/ – AI translation models.
• case_prediction/ – NLP models for case classification.

📂 backend/

Handles API and backend operations:

• api/ – Exposes REST APIs for model access.
• authentication/ – Manages user roles and security.
• logging/ – Tracks system events and errors.

📂 frontend/

User interface for case management dashboards.

📂 infrastructure/

Configuration files for deployment and scaling:

• docker/ – Docker setup.
• k8s/ – Kubernetes configurations.
• ci_cd/ – CI/CD pipeline setup.

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📖 Documentation

• 📜 Project Charter – Defines project objectives.
• 📚 Data Pipeline – Overview of data flow and preprocessing.
• 🏗 Architecture – Technical structure of the system.
• 🔐 Security Guide – Security best practices.
• 📜 Governance – Project management and leadership.
• 📋 Testing Strategy – Testing approach for AI models.
• 🚀 Deployment Guide – Instructions for deploying the system.
• 🛣 Roadmap – Planned project enhancements.

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🚀 Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.11+
• Node.js 18+
• Docker (for containerization)
• MinIO/S3 (for object storage)
• Celery & Redis (for task scheduling)

Installation

1. Clone the repository:

   git clone https://github.com/your-repo-name.git
   cd your-repo-name