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ī¸đŸŽžī¸ Subs AI 🎞ī¸

Subtitles generation tool (Web-UI + CLI + Python package) powered by OpenAI's Whisper and its variants <br/>

<p align="center"> <img src="./assets/demo/demo.gif"> </p> <!-- TOC --> <!-- TOC -->

Features

Installation

Quoted from the official openai/whisper installation

It requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:

# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# on Arch Linux
sudo pacman -S ffmpeg

# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg

# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg

# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg

You may need rust installed as well, in case tokenizers does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH". If the installation fails with No module named 'setuptools_rust', you need to install setuptools_rust, e.g. by running:

pip install setuptools-rust

pip install git+https://github.com/abdeladim-s/subsai

Usage

Web-UI

To use the web-UI, run the following command on the terminal

subsai-webui

And a web page will open on your default browser, otherwise navigate to the links provided by the command

You can also run the Web-UI using Docker.

CLI

usage: subsai [-h] [--version] [-m MODEL] [-mc MODEL_CONFIGS] [-f FORMAT] [-df DESTINATION_FOLDER] [-tm TRANSLATION_MODEL]
              [-tc TRANSLATION_CONFIGS] [-tsl TRANSLATION_SOURCE_LANG] [-ttl TRANSLATION_TARGET_LANG]
              media_file [media_file ...]

positional arguments:
  media_file            The path of the media file, a list of files, or a text file containing paths for batch processing.

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -m MODEL, --model MODEL
                        The transcription AI models. Available models: ['openai/whisper', 'linto-ai/whisper-timestamped']
  -mc MODEL_CONFIGS, --model-configs MODEL_CONFIGS
                        JSON configuration (path to a json file or a direct string)
  -f FORMAT, --format FORMAT, --subtitles-format FORMAT
                        Output subtitles format, available formats ['.srt', '.ass', '.ssa', '.sub', '.json', '.txt', '.vtt']
  -df DESTINATION_FOLDER, --destination-folder DESTINATION_FOLDER
                        The directory where the subtitles will be stored, default to the same folder where the media file(s) is stored.
  -tm TRANSLATION_MODEL, --translation-model TRANSLATION_MODEL
                        Translate subtitles using AI models, available models: ['facebook/m2m100_418M', 'facebook/m2m100_1.2B',
                        'facebook/mbart-large-50-many-to-many-mmt']
  -tc TRANSLATION_CONFIGS, --translation-configs TRANSLATION_CONFIGS
                        JSON configuration (path to a json file or a direct string)
  -tsl TRANSLATION_SOURCE_LANG, --translation-source-lang TRANSLATION_SOURCE_LANG
                        Source language of the subtitles
  -ttl TRANSLATION_TARGET_LANG, --translation-target-lang TRANSLATION_TARGET_LANG
                        Target language of the subtitles

Example of a simple usage

subsai ./assets/test1.mp4 --model openai/whisper --model-configs '{"model_type": "small"}' --format srt

Note: For Windows CMD, You will need to use the following : subsai ./assets/test1.mp4 --model openai/whisper --model-configs "{\"model_type\": \"small\"}" --format srt

You can also provide a simple text file for batch processing (Every line should contain the absolute path to a single media file)

subsai media.txt --model openai/whisper --format srt

From Python

from subsai import SubsAI

file = './assets/test1.mp4'
subs_ai = SubsAI()
model = subs_ai.create_model('openai/whisper', {'model_type': 'base'})
subs = subs_ai.transcribe(file, model)
subs.save('test1.srt')

For more advanced usage, read the documentation.

Examples

Simple examples can be found in the examples folder

Docker

Notes

Contributing

If you find a bug, have a suggestion or feedback, please open an issue for discussion.

License

This project is licensed under the GNU General Licence version 3 or later. You can modify or redistribute it under the conditions of these licences (See LICENSE for more information).