batchalign_app

A front-end web app for Batchalign2, with enhancements for use by the School of Psychology at Trinity College Dublin.

Project Overview

This is a Django web application that provides a user-friendly interface for the Batchalign library, specifically tailored for the School of Psychology's needs. The app allows users to upload audio files (both single and batch uploads) and processes them using Batchalign for transcription and diarization with CHAT format support.

Current Implementation

Core Features

• Single file audio upload functionality
• Batch folder upload support
• Audio file processing using Batchalign
• Transcript generation in both raw and CHAT formats
• Interactive speaker mapping interface
• Support for CHAT format headers and metadata
• Automatic speaker role assignment
• Speaker diarization with customizable mapping
• Export functionality for CHAT format files
• Basic error handling and validation

Project Structure

• batch_processor/: Main Django application
  • models.py: Database models for AudioFile, Transcript, and SpeakerMap
  • views.py: View logic for file handling, processing, and CHAT format generation
  • urls.py: URL routing
  • templates/: HTML templates for user interface
  • tests.py: Test cases for functionality

Features

Audio Processing

• Support for various audio file formats
• Automatic speaker diarization
• Transcript generation with speaker identification

CHAT Format Support

• Automatic CHAT format generation
• Customizable speaker role mapping
• CHAT header metadata generation
• Support for participant information
• Export to .cha files

User Interface

• Interactive file upload interface
• Real-time speaker mapping controls
• Toggle between raw and CHAT formats
• Batch processing status indicators
• Error feedback and validation

Installation and Setup

Prerequisites

• Python 3.8 or higher
• Django 3.2 or higher
• Batchalign library
• Rev.ai API key for speech recognition

Installation Steps

1. Clone the repository: bash git clone [repository-url]

2. Install dependencies: bash pip install -r requirements.txt

3. Configure environment variables:

   • Set up Rev.ai API key
   • Configure Django settings

4. Run migrations: bash python manage.py migrate

5. Start the development server: bash python manage.py runserver

How to Run This Project

Quick Start Guide

To get the application running quickly:

1. Create a virtual environment (recommended): bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

2. Install dependencies: bash pip install -r requirements.txt

3. Run the development server: bash python manage.py runserver This will start the application usuallyon http://127.0.0.1:8000/

Project Structure Explained

The project follows Django's standard structure: - batchalign_app/: The main Django project container with settings and configuration - batch_processor/: The actual Django app that implements the functionality - media/: Directory where uploaded audio files are stored - staticfiles/: Compiled static files for production

Accessing the Application

After starting the server, you can access: - Home page/Upload: http://127.0.0.1:8000/ - File list: http://127.0.0.1:8000/list/ - Transcript viewer: http://127.0.0.1:8000/transcript/{transcript_id}/

Common Issues

• If audio files fail to process, verify your Rev.ai API key is correctly configured
• For large audio files, processing may take several minutes
• Ensure your Python environment has all dependencies from requirements.txt installed

Usage

1. Upload Audio Files:

   • Use single file upload for individual files
   • Use batch upload for multiple files
   • Supported formats: MP3, WAV

2. Process Files:

   • Files are automatically processed using Batchalign
   • Speaker diarization is performed
   • Transcripts are generated in both raw and CHAT formats

3. Map Speakers:

   • Assign roles to identified speakers
   • Use standard CHAT format roles (e.g., MOT, CHI)
   • Update speaker mappings as needed

4. Export Results:

   • Download transcripts in CHAT format
   • Files are saved with .cha extension
   • Contains proper CHAT headers and metadata

Development

Setting Up Development Environment

1. Install development dependencies
2. Configure local settings
3. Set up test database

Running Tests

bash python manage.py test batch_processor

Future Improvements

Priority Features

1. Enhanced User Interface:

   • Progress indicators for file processing
   • Advanced error messaging
   • Improved responsive design

2. Processing Enhancements:

   • Additional audio format support
   • Multi-language support
   • Processing queue optimization

3. Data Management:

   • Advanced transcript editing
   • Bulk file operations
   • Enhanced export options

Technical Improvements

1. Backend Optimization:

   • Advanced error handling
   • Expanded test coverage
   • Background task processing

2. Frontend Enhancement:

   • Modern UI framework integration
   • Real-time updates
   • Advanced validation

License

This project is licensed under the BSD-3-Clause license - see the LICENSE file for details.

Attribution

This web application was developed by Haozhe Ma under the supervision of Dr. Jean Quigley at the School of Psychology, Trinity College Dublin. It is based on Batchalign2 by TalkBank, developed by Brian MacWhinney (Carnegie Mellon University) and Houjun Liu (Stanford University).

Citation

If you use this software in your research, please cite both this application and the original Batchalign2 software. The preferred citation for Batchalign2 is:

Liu, H., MacWhinney, B., Fromm, D., & Lanzi, A. (2023). Automation of Language Sample Analysis. Journal of Speech, Language, and Hearing Research, 66(7), 2421-2433. DOI: 10.1044/2023_JSLHR-22-00642

For more information, see the CITATION.cff file.