semvec/pypi-packages huggingface dataset:
yoloplayer 0.1.1
An OpenCV video player wrapper with YOLO detection support
Requires Python: >=3.7

YOLOPlayer 🎥

A OpenCV wrapper for video playback, detection overlays, and future extensions (YOLO, DeepSORT, SAM, Faster-RCNN).    #right now only tested on YOLO


✨ Features:
✅ Replace cv2.imshow with a more usable player
⏯️ Pause/Play with Spacebar
🔁 Forward/Rewind with Arrow Keys
🖥️ Toggle Fullscreen with F
🚪 Quit with Q
🤖 (Optional) YOLO integration for human detection
📦 Easy to extend with tracking (DeepSORT), segmentation (SAM), Faster-RCNN, etc.



⌨️ Controls

| Key     | Action            |
| ------- | ----------------- |
| `SPACE` | Pause / Play      |
| `Q`     | Quit              |
| `F`     | Fullscreen toggle |
| `→`     | Forward 1 seconds |
| `←`     | Rewind 1 seconds  |
    



Installation-

Clone the repo:

git clone https://github.com/raunitsingh/cvplayer.git
cd yoloplayer


create and activate a virtual environment:

python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows


install dependencies:

pip install -r requirements.txt





🚀 Usage:

Basic video player:

from yoloplayer import YOLOPlayer

# Play a video file
player = YOLOPlayer("video.mp4")
player.run()



for webcam:

player = YOLOPlayer(0)
player.run()



For YOLO detection:

from yoloplayer import YOLOPlayer

player = YOLOPlayer("video.mp4", use_yolo=True, model_path="yolov8n.pt")
player.run()
macscribe 0.1.2
Add your description here
Requires Python: >=3.12

# Macscribe

Macscribe is a command-line tool for transcribing audio from YouTube videos, Apple Podcast episodes, and local audio/video files. It downloads audio from URLs or processes local files, transcribes them using a state-of-the-art ML model, and copies the transcription directly to your clipboard for easy use.

## Features

- **Multi-Platform Support:** Accepts YouTube, Apple Podcast, and X URLs.
- **Local File Support:** Transcribes local audio files (MP3, WAV, FLAC, M4A, OGG, WMA) and video files (MP4, MOV, AVI, MKV, WEBM, M4V, WMV).
- **Automated Audio Processing:** Downloads high-quality audio from URLs or processes local files directly.
- **State-of-the-Art Transcription:** Utilizes `mlx-whisper` for accurate and fast transcription.
- **Clipboard Integration:** Automatically copies the transcript to your clipboard.
- **Customizable Models:** Option to specify a different Hugging Face model for transcription.
- **Simple CLI Interface:** Easy-to-use command-line interface built with Typer.

## Installation

Macscribe can be installed using `pip`. Ensure you have Python 3.12 or later installed, then run:

```bash
pip install macscribe
```

This will install Macscribe along with its dependencies, including `yt-dlp`, `mlx-whisper`, and `typer`.

## Usage

Once installed, you can run Macscribe directly from the command line. The basic usage is:

```bash
macscribe <INPUT> [--model MODEL]
```

**Arguments:**

- `<INPUT>`: Either a URL (YouTube, Apple Podcast, X) or path to a local audio/video file.
- `--model MODEL`: *(Optional)* The Hugging Face model to use for transcription.  
  Defaults to `"mlx-community/whisper-large-v3-mlx"` if not specified.

**Examples:**

1. Transcribe a YouTube video using the default model:

   ```bash
   macscribe https://www.youtube.com/watch?v=dQw4w9WgXcQ
   ```

2. Transcribe an Apple Podcast episode with a specific model:

   ```bash
   macscribe https://podcasts.apple.com/us/podcast/example-episode-url --model some/alternative-model
   ```

3. Transcribe a local audio file:

   ```bash
   macscribe /path/to/your/audio.mp3
   ```

4. Transcribe a local video file:

   ```bash
   macscribe /path/to/your/video.mp4
   ```

After transcription, the resulting text is automatically copied to your clipboard.

## Contributing

Contributions are welcome! If you'd like to contribute to Macscribe, please follow these steps:

1. **Fork the repository** on GitHub.
2. **Clone your fork** and create a new branch for your feature or bugfix.
3. Make your changes, ensuring code quality and consistency.
4. **Test** your changes thoroughly.
5. Submit a **pull request** describing your changes and why they should be merged.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
```

This README provides an introduction, key features, installation instructions, usage examples, contribution guidelines, and licensing information, offering a comprehensive guide to using and contributing to Macscribe.