Metadata-Version: 2.4
Name: talks-reducer
Version: 0.3.3
Summary: CLI for speeding up long-form talks by removing silence
Author: Talks Reducer Maintainers
License-Expression: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: audiotsm>=0.1.2
Requires-Dist: scipy>=1.10.0
Requires-Dist: numpy>=1.22.0
Requires-Dist: tqdm>=4.65.0
Requires-Dist: tkinterdnd2>=0.3.0
Requires-Dist: Pillow>=9.0.0
Requires-Dist: imageio-ffmpeg>=0.4.8
Provides-Extra: dev
Requires-Dist: build>=1.0.0; extra == "dev"
Requires-Dist: twine>=4.0.0; extra == "dev"
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: black>=23.0.0; extra == "dev"
Requires-Dist: isort>=5.12.0; extra == "dev"
Requires-Dist: pyinstaller>=6.0.0; extra == "dev"
Dynamic: license-file

# Talks Reducer
Talks Reducer shortens long-form presentations by removing silent gaps and optionally re-encoding them to smaller files. The
project was renamed from **jumpcutter** to emphasize its focus on conference talks and screencasts.

![Main demo](docs/assets/screencast-main.gif)

## Example
- 1h 37m, 571 MB — Original OBS video recording
- 1h 19m, 751 MB — Talks Reducer
- 1h 19m, 171 MB — Talks Reducer `--small`

## Changelog

See [CHANGELOG.md](CHANGELOG.md).

## Install GUI (Windows, macOS)
Go to the [releases page](https://github.com/popstas/talks-reducer/releases) and download the appropriate artifact:

- **Windows** — `talks-reducer-windows.zip`
- **macOS** — `talks-reducer.app.zip` (but it doesn't work for me)

## Install CLI (Linux, Windows, macOS)
```
pip install talks-reducer
```

**Note:** FFmpeg is now bundled automatically with the package, so you don't need to install it separately. You you need, don't know actually.

The `--small` preset applies a 720p video scale and 128 kbps audio bitrate, making it useful for sharing talks over constrained
connections. Without `--small`, the script aims to preserve original quality while removing silence.

Example CLI usage:

```sh
talks-reducer --small input.mp4
```

When CUDA-capable hardware is available the pipeline leans on GPU encoders to keep export times low, but it still runs great on
CPUs.

## Contributing
See `CONTRIBUTION.md` for development setup details and guidance on sharing improvements.

## License
Talks Reducer is released under the MIT License. See `LICENSE` for the full text.
