🐍 Back-end Configuration

📝 Overview

This document outlines the configuration of the TAP back-end, which provides API endpoints for speech transcription and speaker identification. It is built using Django + Django Rest Framework (DRF), and supports asynchronous task processing using Celery and Redis.

📑 Table of Contents

1. Project Structure
2. Installation
3. How to Run
4. API Endpoints
5. Technologies Used
6. Database Setup
7. Asynchronous Tasks
8. Testing

📁 Project Structure

transcription_project/
│
├── manage.py                    # Django management command
├── requirements.txt             # Backend dependencies
├── .env                         # Environment variables file
├── .gitignore                   # Git ignore file
│
├── config/                      # Project settings and configuration
│   ├── __init__.py
│   ├── settings.py              # Django global settings (includes database settings)
│   ├── urls.py                  # Main entry for routing API endpoints
│   ├── asgi.py                  # ASGI for asynchronous support
│   └── wsgi.py                  # WSGI for deployment
│
├── core/                        # Core application (generic logic and shared utilities)
│   ├── __init__.py
│   ├── models.py                # Generic models, if needed
│   ├── views.py                 # Core API views logic (if necessary)
│   ├── urls.py                  # Core app API routes
│   └── serializers.py           # Data serialization for generic core responses
│
├── transcription/               # Transcription app (handles transcription processes)
│   ├── __init__.py
│   ├── models.py                # Transcription-related data models
│   ├── views.py                 # Transcription API views logic
│   ├── urls.py                  # Transcription app API routes
│   ├── tasks.py                 # Asynchronous tasks (e.g., calling external speech recognition APIs)
│   ├── transcribe_service.py    # Logic for integrating external transcription services
│   └── serializers.py           # Transcription data serialization
│
├── speaker_identify/            # Speaker identification app
│   ├── __init__.py
│   ├── models.py                # Speaker identification-related data models
│   ├── views.py                 # Speaker identification API views logic
│   ├── urls.py                  # Speaker identification API routes
│   ├── identify_service.py      # Logic for identifying speakers in audio files
│   └── serializers.py           # Speaker identification data serialization
│
├── api/                         # General API (for non-specific or cross-app APIs)
│   ├── __init__.py
│   ├── urls.py                  # API global routes (including versioning if needed)
│   └── views.py                 # General API views
│
└── tests/                       # Unit and integration tests
    ├── test_core.py             # Core app tests
    ├── test_transcription.py    # Transcription app tests
    └── test_speaker_identify.py # Speaker identification app tests

🛠️ Installation

1. Clone the Repository

First, clone the project to your local machine:

git clone https://github.com/yourusername/transcription_project.git
cd transcription_project

2. Set up a Python Virtual Environment

Create a Python virtual environment to install dependencies:

python3 -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate

3. Install Dependencies

Use `pip` to install the project dependencies listed in `requirements.txt`:

pip install -r requirements.txt

4. Environment Variables

Create a `.env` file in the root of your project and add the following environment variables:

SECRET_KEY=your_secret_key_here
DEBUG=True
DATABASE_URL=your_database_url_here  # or use settings from 'settings.py'
CELERY_BROKER_URL=redis://localhost:6379/0  # For Redis

▶️ How to Run

1. Database Setup

Before running the application, you need to configure the database and apply migrations.

PostgreSQL (Recommended)

Make sure you have PostgreSQL installed and running. Create a database named `transcription_db` or configure your database settings in `config/settings.py`.

Then, run the following commands:

python manage.py makemigrations
python manage.py migrate

2. Run Redis for Celery (Asynchronous Tasks)

For background tasks, you need to have Redis running:

redis-server

3. Run Celery Worker

In a separate terminal, run the Celery worker to handle asynchronous tasks:

celery -A config worker --loglevel=info

4. Run the Django Server

You can now run the Django development server:

python manage.py runserver

Access the server at `http://127.0.0.1:8000/\`.

🔌 API Endpoints

Transcription API

• POST `/api/transcription/`: Upload an audio file and receive transcription text.
• GET `/api/transcription//`: Get the transcription result for a specific transcription.

Speaker Identification API

• POST `/api/speaker-identify/`: Upload an audio file and identify the speaker.
• GET `/api/speaker-identify//`: Get the speaker identification result.

⚙️ Technologies Used

• Django: Web framework for building the backend API.
• Django REST Framework (DRF): For creating RESTful APIs.
• Celery: For asynchronous task management (e.g., speech transcription, speaker identification).
• Redis: As a message broker for Celery.
• PostgreSQL: For database management.
• Google Speech-to-Text (or other services): For handling transcription (if using an external service).
• pyAudioAnalysis: For speaker identification (optional, if integrated).

🗃️ Database Setup

Database Configuration

The project uses Django's ORM (Object-Relational Mapping) for managing the database. By default, it is configured to use PostgreSQL, but you can change this in `config/settings.py` if needed.

Example for PostgreSQL:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'transcription_db',
        'USER': 'your_db_user',
        'PASSWORD': 'your_db_password',
        'HOST': 'localhost',
        'PORT': '5432',
    }
}

If you are using another database, such as SQLite, you can update the configuration accordingly.

Running Migrations

After setting up your database connection, apply migrations to create the required tables:

python manage.py makemigrations
python manage.py migrate

🔄 Asynchronous Tasks

To handle long-running tasks such as speech transcription and speaker identification, we use Celery.

Steps to Set Up Celery:

1. Make sure Redis is installed and running as the message broker.
2. Configure Celery in `config/celery.py`.
3. Start the Celery worker:

   celery -A config worker --loglevel=info
   

Celery tasks are defined in the `tasks.py` file of each app (e.g., `transcription/tasks.py`).

🧪 Testing

To run the project's test cases, use the following command:

python manage.py test

Test cases are located in the `tests/` directory, with separate tests for transcription and speaker identification functionalities.