LeGen is a fast, AI-powered subtitle studio that runs right on your machine. It taps into Whisper and WhisperX to transcribe speech, translates the results into the language you need, then exports polished .srt/.txt files, muxes them into MP4 containers, or even burns them straight into the video. LeGen also speaks fluent yt-dlp, pulling remote videos or playlists and embedding every subtitle track it can find before the pipeline kicks in.

This is very useful for making it available in another language, or even just subtitling any video that belongs to you or that you have the proper authorization to do so, be it a film, lecture, course, presentation, interview, etc.

LeGen works on Google Colab, using their computing power to do the work. Aceess the link to run on Google Colab

Install uv by following the official installation guide. Once uv is available, install the latest LeGen release from PyPI with:

uv tool install legen

This command downloads the published wheel via uv's pip-compatible resolver and creates an isolated environment that exposes a legen launcher on your PATH. Keep FFmpeg installed on the host so the CLI can access it (see "From source" below for platform-specific tips).

To update an existing installation to the newest version, run:

uv tool upgrade legen

Run the CLI just like any other command-line tool:

legen -i /path/to/video.mp4

If your shell cannot find the command, ensure uv's tool shims directory (usually ~/.local/bin) is on your PATH, or invoke the tool through uv directly with uv tool run legen -i /path/to/video.mp4.

Install the published package directly from PyPI:

pip install legen

The legen console script will be added to your PATH and mirrors all CLI options documented below.

Install FFMpeg from FFMPeg Oficial Site or from your linux package manager. If using windows, prefer gyan_dev release full choco install ffmpeg-full

Install Git

Install Python Recomended version: 3.12.x (LeGen currently supports CPython 3.9 up to 3.12). If using windows, select "Add to PATH" option when installing

Clone LeGen using git

git clone https://github.com/matheusbach/legen.git
cd legen

Install requirements using pip. Is recommended to create a virtual environment (venv) as a good practice

pip3 install -r requirements.txt --upgrade

Ensure the yt-dlp command is available in your shell so LeGen can fetch remote videos. The provided requirements install yt-dlp for convenience, and LeGen will use it to embed all subtitle tracks it can find for each item into the MP4 container.

If having troubles with GPU compatibility, get PyTorch for your GPU.

And done. Now you can use LeGen

If you installed the packaged CLI with uv, use uv tool upgrade legen as shown above.

For pip-based environments:

git fetch && git reset --hard origin/main && git pull
pip3 install -r requirements.txt --upgrade --force-reinstall

To use LeGen, run the following command:

The minimum comand line is:

legen -i [input_path]

If you installed from source without uv, replace the command with:

python3 legen.py -i [input_path]

Users could for example also translate generated subtitles for other language like portuguese (pt) adding --translate pt to the command line

Full options list are described bellow:

• -i, --input_path: Specifies the path to the media files or a direct video/playlist URL. The CLI will download URLs with yt-dlp before processing. Example: LeGen -i /path/to/media/files or LeGen -i https://www.youtube.com/watch?v=….

• --norm: Normalizes folder times and runs vidqa on the input path before starting to process files. Useful for synchronizing timestamps across multiple media files.

• -ts:e, --transcription_engine: Specifies the transcription engine to use. Possible values are "whisperx" and "whisper". Default is "whisperx".

• -ts:m, --transcription_model: Specifies the path or name of the Whisper transcription model. A larger model will consume more resources and be slower, but with better transcription quality. Possible values: tiny, base, small, medium, large, large-v3, turbo, large-v3-turbo (default)...

• -ts:d, --transcription_device: Specifies the device to run the transcription through Whisper. Possible values: auto (default), cpu, cuda.

• -ts:c, --transcription_compute_type: Specifies the quantization for the neural network. Possible values: auto (default), int8, int8_float32, int8_float16, int8_bfloat16, int16, float16, bfloat16, float32.

• -ts:v, --transcription_vad: Selects the voice-activity detector used by WhisperX. Options: silero (default), pyannote.

• -ts:b, --transcription_batch: Specifies the number of simultaneous segments being transcribed. Higher values will speed up processing. If you have low RAM/VRAM, long duration media files or have buggy subtitles, reduce this value to avoid issues. Only works using transcription_engine whisperx. Default is 4.

• --translate: Translates subtitles to a language code if they are not the same as the original. The language code should be specified after the equals sign. For example, LeGen --translate=fr would translate the subtitles to French.

• --input_lang: Indicates (forces) the language of the voice in the input media. Default is "auto".

• -c:v, --codec_video: Specifies the target video codec. Can be used to set acceleration via GPU or another video API [codec_api], if supported (ffmpeg -encoders). Examples include h264, libx264, h264_vaapi, h264_nvenc, hevc, libx265 hevc_vaapi, hevc_nvenc, hevc_cuvid, hevc_qsv, hevc_amf. Default is h264.

• -c:a, --codec_audio: Specifies the target audio codec. Default is aac. Examples include aac, libopus, mp3, vorbis.

• -o:s, --output_softsubs: Specifies the path to the folder or output file for the video files with embedded softsub (embedded in the mp4 container and .srt files). Default is "softsubs_" followed by the input path.

• -o:h, --output_hardsubs: Specifies the output folder path for video files with burned-in captions and embedded in the mp4 container. Default is "hardsubs_" followed by the input path.

• -o:d, --output_downloads: Overrides the folder used to store media downloaded from URL inputs. Default is ./downloads when -i receives a URL.

• --overwrite: Overwrites existing files in output directories. By default, this option is false.

• -dl:rs, --download_remote_subs: When supplied alongside a URL input, instructs yt-dlp to download and embed every subtitle track it can find into the downloaded MP4. By default, remote subtitles are not fetched.

• --subtitle_formats: Specifies which subtitle formats should be exported. Separate multiple values with comma or space. Supported formats: srt, txt. Example: --subtitle_formats srt,txt.

• --disable_srt: Disables .srt file generation and doesn't insert subtitles in the mp4 container of output_softsubs. Equivalent to removing srt from --subtitle_formats. By default, this option is false.

• --disable_softsubs: Doesn't insert subtitles in the mp4 container of output_softsubs. This option continues generating .srt files. By default, this option is false.

• --disable_hardsubs: Disables subtitle burn in output_hardsubs. By default, this option is false.

• --copy_files: Copies other (non-video) files present in the input directory to output directories. Only generates the subtitles and videos. By default, this option is false.

• --translate_engine: Selects the translation engine. Possible values: google (default), gemini.

• --gemini_api_key: Gemini API key used for translation when --translate_engine gemini. Repeat the flag or separate keys with commas/line breaks to supply multiple keys (useful for rotating free-tier quotas). Get your keys at https://aistudio.google.com/apikey

Each of these options provides control over various aspects of the video processing workflow. Make sure to refer to the documentation or help message (LeGen --help) for more details on each optionSource 0Source 2.

When you pass a HTTP(S) URL to -i, LeGen will:

• Invoke yt-dlp to download the target video, playlist, or batch feed.
• Embed every subtitle track the platform exposes directly into the downloaded media only when --download_remote_subs is provided.
• Force mp4 output with the best available video and audio combination.
• Store the media under ./downloads or the path provided through --output_downloads.
• Continue the normal transcription/translation pipeline on the freshly downloaded files with no additional steps from you.

If the value supplied to -i is neither a reachable URL nor a valid local file/folder, LeGen will abort with a clear error message so you can correct the input.

You can run LeGen inside a container, keeping the host Python environment clean while still persisting downloads and outputs on disk.

1. Build the image with docker compose build (or docker compose pull once a registry image is available).
2. Place the media you want to process inside ./data or mount a different host folder to /data when invoking Docker.
3. Run LeGen through Compose: docker compose run --rm legen -i /data/my-video.mp4 --translate pt. All generated downloads and subtitles stay under the mapped downloads, softsubs_m, and hardsubs_m directories.

To use a GPU-enabled Docker setup, add --gpus all to the compose command (Docker Engine 19.03+ with the NVIDIA Container Toolkit). LeGen will detect the GPU automatically, but you can still override it with --transcription_device if needed.

• Compose command arguments override the default --help. Example: docker compose run --rm legen -i /data/my-video.mp4 --translate pt --download_remote_subs.
• Provide Gemini keys when translating with Gemini: docker compose run --rm legen --gemini_api_key YOUR_KEY -i /data/file.mp4 --translate_engine gemini --translate en.
• Forward environment variables if you prefer: docker compose run --rm --env GEMINI_API_KEY=YOUR_KEY legen --gemini_api_key "$GEMINI_API_KEY" -i /data/file.mp4.
• Run the raw image without Compose: docker run --rm -it -v "$PWD/data:/data" -v "$PWD/downloads:/app/downloads" legen:local -i /data/input.mp4 --disable_hardsubs.
• Keep the container running interactively for multiple executions by starting a shell: docker compose run --rm --entrypoint /bin/bash legen.
• List all CLI options from inside the container: docker compose run --rm legen --help.

LeGen automatically selects the best accelerator at runtime (cuda > mps > cpu). When a compatible GPU is available, transcription and alignment transparently run on it; otherwise the pipeline falls back to the CPU. You can still force a specific backend with --transcription_device.

With Docker, the image installs the CUDA-enabled PyTorch wheels by default. Expose GPUs to the container using the NVIDIA Container Toolkit (docker compose run --rm --gpus all legen ...). If you need a CPU-only image, build with docker compose build --build-arg PYTORCH_INSTALL_CUDA=false.

LeGen requires the following pip dependencies to be installed:

• deep_translator
• ffmpeg_progress_yield
• openai_whisper
• pysrt
• torch
• tqdm
• whisper
• vidqa
• whisperx-legen-fork (legen flavored fork from m-bain/whisperx)
• gemini-srt-translator
• yt-dlp

This dependencies can be installed and updated with pip install -r requirements.txt --upgrade

LeGen requires the yt-dlp CLI on your system to download remote content automatically.

You also need to install FFmpeg

Contributions are welcome. Submit your pull request ❤️

Not being able to use the software, or encountering an error? open an issue

Welcome and don't be a sick. We are brazilian, but you can write in other language if you want. https://t.me/+c0VRonlcd9Q2YTAx

[PT-BR] [SEMI-OUTDATED] Tutorial - LeGen no Google Colab

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This project is licensed under the terms of the GNU GPLv3.