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
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.
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.
Getting Started
Prerequisites
Make sure the following are installed on your machine:
- Python 3.11+
- Node.js 18+
- Docker (for containerization)
- MinIO/S3 (for object storage)
- Celery & Redis (for task scheduling)
Installation
bash
git clone https://github.com/your-repo-name.git
cd your-repo-name