Call Quality Rate

Performing Call Quality Rate (CQR) evaluation on customer call transcripts.

Table of Contents

• Description
• Features
• Getting Started
  • Prerequisites
  • Installation
  • Usage
• Project Structure
• Contributing
• License

Description

This project aims to evaluate the Call Quality Rate (CQR) of customer call transcripts based on emotional responsiveness, effectiveness of problem-solving, professionalism, and awareness.

Features

• Transcribe audio recordings into text using the Vosk model.
• Perform diarization to distinguish speakers using pyannote.audio.
• Analyze sentiment of text using the SentenceTransformer model.
• Export results to Google Sheets using the Google Sheets API.
• Visualize results using a Streamlit web app.

Getting Started

Prerequisites

• Docker: Install Docker for containerization.

Installation

1. Clone the repository:

   git clone https://github.com/Bdata0/call_quality_rate.git
   cd call_quality_rate

2. Build the Docker container:

   docker build -t call_quality_rate .

Usage

1. Place your audio file (in MP3 format) in the root directory.

2. Run the Docker container:

   docker run -it -v $(pwd):/app call_quality_rate

3. Follow the instructions in the terminal to analyze the call transcript and view the results using the Streamlit web app.

Project Structure

The project follows a structured organization based on Domain-Driven Design (DDD) principles.

# project/
├── app.py
├── data/
│   ├── processed/
│   ├── raw/
│   │   ├── audio.mp3
│   │   └── ...
├── domain/
│   ├── __init__.py
│   ├── transcription/
│   │   ├── __init__.py
│   │   ├── models/
│   │   │   ├── vosk-model-ru-0.42.zip
│   │   │   └── ...
│   │   ├── transcription.py
│   │   └── ...
│   ├── diarization/
│   │   ├── __init__.py
│   │   ├── diarization.py
│   │   └── ...
│   ├── sentiment_analysis/
│   │   ├── __init__.py
│   │   ├── sentiment_analysis.py
│   │   └── ...
│   ├── google_sheets/
│   │   ├── __init__.py
│   │   ├── google_sheets.py
│   │   └── ...
├── tests/
│   ├── __init__.py
│   ├── test_transcription.py
│   ├── test_diarization.py
│   ├── test_sentiment_analysis.py
│   ├── test_google_sheets.py
│   ├── test_integration.py
│   └── ...
├── Dockerfile
├── requirements.txt
└── ...

Contributing

Contributions are welcome! Please follow these guidelines when contributing:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Make your changes and commit with descriptive messages.
• Submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file.