captionalchemy 1.1.1

A Python package to create closed captions with face detection and recognition.

CaptionAlchemy

A Python package for creating intelligent closed captions with face detection and speaker recognition.

Features

• Audio Transcription: Powered by OpenAI Whisper for high-quality speech-to-text
• Speaker Diarization: Identifies different speakers in audio
• Face Recognition: Links speakers to known faces for character identification
• Multiple Output Formats: Supports SRT, VTT, and SAMI caption formats
• Voice Activity Detection: Intelligently detects speech vs non-speech segments
• GPU Acceleration: Automatic CUDA support when available

Installation

pip install captionalchemy

If you have a GPU and want to use hardware acceleration:

pip install captionalchemy[cuda]

Prerequisites

• Python 3.10+
• FFmpeg (for video/audio processing)
• CUDA-capable GPU (optional, for acceleration but is highly recommended for the diarization)
• Whisper.cpp capable (optional on MacOS)

If using Whisper.cpp on MacOS, follow installation instructions [here] Clone the whisper repo into your working directory.

Quick Start

1. Set up environment variables (create .env file):

   HF_AUTH_TOKEN=your_huggingface_token_here
   

2. Prepare known faces (optional, for speaker identification): Create known_faces.json:

   [
     {
       "name": "Speaker Name",
       "image_path": "path/to/speaker/photo.jpg"
     }
   ]
   

3. Generate captions:

captionalchemy video.mp4 -f srt -o my_captions

or in a python script

from dotenv import load_dotenv
from captionalchemy import caption

load_dotenv()

caption.run_pipeline(
    video_url_or_path="path/to/your/video.mp4",         # this can be a video URL or local file
    character_identification=False,                      # True by default
    known_faces_json="path/to/known_faces.json",
    embed_faces_json="path/to/embed_faces.json",        # name of the output file
    caption_output_path="my_captions/output",           # will write output to output.srt (or .vtt/.smi)
    caption_format="srt"
)

Usage

Basic Usage

# Generate SRT captions from video file
captionalchemy video.mp4

# Generate VTT captions from YouTube URL
captionalchemy "https://youtube.com/watch?v=VIDEO_ID" -f vtt -o output

# Disable face recognition
captionalchemy video.mp4 --no-face-id

Command Line Options

captionalchemy VIDEO [OPTIONS]

Arguments:
  VIDEO                Video file path or URL

Options:
  -f, --format         Caption format: srt, vtt, smi (default: srt)
  -o, --output         Output file base name (default: output_captions)
  --no-face-id         Disable face recognition
  --known-faces-json   Path to known faces JSON (default: example/known_faces.json)
  --embed-faces-json   Path to face embeddings JSON (default: example/embed_faces.json)
  -v, --verbose        Enable debug logging

How It Works

1. Face Embedding: Pre-processes known faces into embeddings
2. Audio Extraction: Extracts audio from video files
3. Voice Activity Detection: Identifies speech segments
4. Speaker Diarization: Separates different speakers
5. Transcription: Converts speech to text using Whisper
6. Face Recognition: Matches speakers to known faces (if enabled)
7. Caption Generation: Creates timestamped captions with speaker names

Configuration

Known Faces Setup

Create a known_faces.json file with speaker information:

[
  {
    "name": "John Doe",
    "image_path": "photos/john_doe.jpg"
  },
  {
    "name": "Jane Smith",
    "image_path": "photos/jane_smith.png"
  }
]

Environment Variables

• HF_AUTH_TOKEN: Hugging Face token for accessing pyannote models

Output Examples

SRT Format

1
00:00:03,254 --> 00:00:06,890
John Doe: Welcome to our presentation on quantum computing.

2
00:00:07,120 --> 00:00:10,456
Jane Smith: Thanks John. Let's start with the basics.

VTT Format

WEBVTT

00:03.254 --> 00:06.890
John Doe: Welcome to our presentation on quantum computing.

00:07.120 --> 00:10.456
Jane Smith: Thanks John. Let's start with the basics.

Development and Contributing

Setup Development Environment

# Install in development mode
pip install -e ".[dev]"

Running Tests

pytest

Code Quality

# Linting
flake8

# Code formatting
black src/ tests/

Requirements

See requirements.txt for the complete list of dependencies. Key packages include:

• openai-whisper: Speech transcription
• pyannote.audio: Speaker diarization
• opencv-python: Computer vision
• insightface: Face recognition
• torch: Deep learning framework

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

Troubleshooting

Common Issues

• CUDA out of memory: Use CPU-only mode or reduce batch sizes
• Missing models: Ensure whisper.cpp models are downloaded
• Face recognition errors: Verify image paths in known_faces.json
• Audio extraction fails: Check that FFmpeg is installed

Getting Help

• Check the logs with -v flag for detailed error information
• Ensure all dependencies are properly installed
• Verify video file format compatibility