οΈπ Subtitles generation tool (Web-UI + CLI + Python package) powered by OpenAI's Whisper and its variants ποΈ
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Supported Models
- openai/whisper
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Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
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- linto-ai/whisper-timestamped
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Multilingual Automatic Speech Recognition with word-level timestamps and confidence
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- ggerganov/whisper.cpp (Soon)
- openai/whisper
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Web UI
- Fully offline, no third party services
- Works on Linux, Mac and Windows
- Lightweight and easy to use
- Supports subtitle modification
- Integrated tools:
- Translation using xhluca/dl-translate:
- Supported models:
- Auto-sync using smacke/ffsubsync
- Translation using xhluca/dl-translate:
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Command Line Interface
- For simple or batch processing
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Python package
- In case you want to develop your own scripts
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Supports different subtitle formats thanks to tkarabela/pysubs2
- SubRip
- WebVTT
- substation alpha
- MicroDVD
- MPL2
- TMP
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Supports audio and video files
- Install ffmpeg
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 ffmpegYou 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 thepip install
command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure thePATH
environment variable, e.g.export PATH="$HOME/.cargo/bin:$PATH"
. If the installation fails withNo module named 'setuptools_rust'
, you need to installsetuptools_rust
, e.g. by running:pip install setuptools-rust
- Once ffmpeg is installed, install
subsai
pip install git+https://github.com/abdeladim-s/subsai
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
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 --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 /home/user/media.txt --model openai/whisper --format srt
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.
- If you have an NVIDIA graphics card, you may need to install cuda to use the GPU capabilities.
- Transcription time is shown on the terminal, keep an eye on it while running the web UI.
- If you didn't like Dark mode web UI, you can switch to Light mode from
settings > Theme > Light
.
If you find a bug, have a suggestion or feedback, please open an issue for discussion.
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).