subtitle-engine 0.1.3

Generate SRT subtitles from audio/video files using WhisperX

subtitle-engine

Generate .srt subtitle files from audio or video files using WhisperX. Optionally generate a caption from the transcript with a local Ollama LLM.

Installation

Requires Python 3.12 or newer.

pip install subtitle-engine

Or install from source:

git clone https://github.com/leevipuntanen/subtitle-engine.git
cd subtitle-engine
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Usage

# Basic usage — writes <input>.srt next to the source file
subeng video.mp4

# Specify output file
subeng video.mp4 --output subtitles.srt

# Use a different model or language
subeng video.mp4 --model medium --language fi

# Force CPU / CUDA
subeng video.mp4 --device cpu

# Speaker diarization (requires a Hugging Face token)
subeng video.mp4 --diarize --hf-token $HF_TOKEN

# Generate a caption from the transcript using Ollama
subeng video.mp4 --caption --ollama-model qwen3.5:0.6b

# Short-form subtitles (2-5 words per line, default)
subeng video.mp4 --preset shortform

# Long-form subtitles (10-14 words per line)
subeng video.mp4 --preset longform

Options

Option	Description
--output, -o	Output SRT file path
--model, -m	WhisperX model: tiny, base, small (default), medium, large-v2, large-v3
--language, -l	ISO language code, e.g. en, fi. Auto-detected if omitted.
--device, -d	cpu or cuda. Auto-detected if omitted.
--batch-size, -b	Inference batch size (default: 16)
--compute-type, -c	int8 or float16. Auto-selected if omitted.
--diarize	Enable speaker diarization
--hf-token	Hugging Face token for diarization (or set HF_TOKEN env var)
--caption	Generate a caption from the transcript via Ollama
--ollama-model	Ollama model name (required with --caption)
--ollama-host	Ollama API host (default: http://localhost:11434)
--preset, -p	Subtitle style: shortform (2-5 words, default) or longform (10-14 words)

Development

Run the test suite:

pytest

License

MIT