semvec/pypi-packages huggingface dataset:
simple-auto-subtitle 0.1.0
Automatic subtitle generation using Gemini AI
Requires Python: >=3.8
Keywords: ai, captions, gemini, srt, subtitle, transcription, video

# Simple Auto Subtitle (Coauthor: Claude)

Automatic subtitle generation tool that extracts audio from video files and generates subtitles using Google's Gemini AI models.

## Features

- **Audio Extraction**: Automatically extracts audio from video files using ffmpeg
- **AI Transcription**: Uses Gemini 2.5 models (Flash or Pro) for accurate speech-to-text with precise timestamps
- **Multiple Output Formats**:
  - `.srt` subtitle files
  - Videos with embedded subtitles (toggleable)
  - Videos with burnt-in subtitles (permanent)
  - Extracted audio files (`.wav`)
- **Model Selection**: Choose between Gemini Flash 2.5 (faster) or Pro 2.5 (more accurate)
- **Structured Output**: Uses Pydantic models for reliable JSON parsing
- **Environment Variables**: Supports `.env` files for API key management

## Installation

### From PyPI (Recommended)
```bash
pip install simple-auto-subtitle
```

### From Source
```bash
git clone https://github.com/yunfanye/auto_subtitle.git
cd auto_subtitle
pip install -e .
```

### Development Installation
```bash
git clone https://github.com/yunfanye/auto_subtitle.git
cd auto_subtitle
pip install -r requirements.txt
pip install -e .
```

## Setup

1. **Install ffmpeg** (required for audio/video processing):
   - macOS: `brew install ffmpeg`
   - Ubuntu: `sudo apt install ffmpeg`
   - Windows: Download from [ffmpeg.org](https://ffmpeg.org/download.html)

2. **Set up Gemini API Key**:
   Create a `.env` file in your working directory:
   ```
   GEMINI_API_KEY=your_api_key_here
   ```

## Usage

### Basic Usage
```bash
# Process default video (test.mp4) with Flash model
simple-auto-subtitle

# Process specific video file  
simple-auto-subtitle my_video.mp4

# Use Pro model for better accuracy
simple-auto-subtitle my_video.mp4 --model pro

# Alternative command
auto-subtitle my_video.mp4
```

### Command Line Options
```bash
simple-auto-subtitle [video_file] [options]

Arguments:
  video_file              Video file to process (default: test.mp4)

Options:
  --api-key API_KEY       Gemini API key (or set GEMINI_API_KEY env var)
  --output, -o OUTPUT     Output SRT file path (default: video_name.srt)
  --model {flash,pro}     Gemini model: flash (faster) or pro (more accurate)
```

## Output Files

For input video `my_video.mp4`, the tool generates:

- `my_video.srt` - Standard subtitle file
- `my_video.wav` - Extracted audio file
- `my_video_embedded.mp4` - Video with embedded subtitles (can be toggled on/off)
- `my_video_captioned.mp4` - Video with burnt-in subtitles (always visible)

## Model Comparison

| Model | Speed | Accuracy | Cost | Best For |
|-------|-------|----------|------|----------|
| Flash | Fast | Good | Lower | Quick processing, bulk videos |
| Pro | Slower | Better | Higher | High-quality transcription, important content |

## Requirements

- Python 3.8+
- ffmpeg
- Google Gemini API key
- Required Python packages: `google-genai`, `pydantic`, `python-dotenv`
DPAD 0.0.9
Python implementation for DPAD (dissociative and prioritized analysis of dynamics)
Requires Python: >=3.11

# Publication:
The following paper introduces and provides results of DPAD (**dissociative and prioritized analysis of dynamics**) in multiple real neural datasets.

Omid G. Sani, Bijan Pesaran, Maryam M. Shanechi. *Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks*. ***Nature Neuroscience*** (2024). https://doi.org/10.1038/s41593-024-01731-2

Original preprint: https://doi.org/10.1101/2021.09.03.458628


# Usage examples
The following notebook contains usage examples of DPAD for several use-cases:
[source/DPAD/example/DPAD_tutorial.ipynb](https://github.dev/ShanechiLab/DPAD/blob/main/source/DPAD/example/DPAD_tutorial.ipynb). 

An HTML version of the notebook is also available next to it in the same directory.

# Usage examples
The following documents explain the formulation of the key classes that are used to implement DPAD (the code for these key classes is also available in the same directory):

- [source/DPAD/DPADModelDoc.md](./source/DPAD/DPADModelDoc.md): The formulation implemented by the `DPADModel` class, which performs the overall 4-step DPAD modeling.

-  [source/DPAD/RNNModelDoc.md](./source/DPAD/RNNModelDoc.md): The formulation implemented by the custom `RNNModel` class, which implements the RNNs that are trained in steps 1 and 3 of DPAD. 

-  [source/DPAD/RegressionModelDoc.md](./source/DPAD/RegressionModelDoc.md): The formulation implemented by the `RegressionModel` class, which `RNNModel` and `DPADModel` both internally use to build the general multilayer feed-forward neural networks that are used to implement each model parameter. 

We are working on various improvements to the DPAD codebase. Stay tuned!

# Change Log
You can see the change log in [ChangeLog.md](./ChangeLog.md)  

# License
Copyright (c) 2024 University of Southern California  
See full notice in [LICENSE.md](./LICENSE.md)  
Omid G. Sani and Maryam M. Shanechi  
Shanechi Lab, University of Southern California
etformat 1.2.0
A Python package for eye-tracking data processing
Requires Python: >=3.6

# etformat - Eye-Tracking Data Processing

etformat is a Python package designed to simplify the extraction, conversion, and analysis of eye-tracking data from EDF files. It provides tools for exporting data, analyzing fixations and saccades, validating calibration, and visualizing gaze movements. Whether you're conducting research in psychology, neuroscience, or usability testing, etformat makes handling eye-tracking data efficient and accessible.


# Important Note before using etformat

Before using the package it is neccessary to follow this steps:
(©️ Eylink Compnay & Alexander (Sasha) Pastukhov)

Install SR Research EyeLink Developers Kit
This package relies on edfapi library that is as part of the EyeLink Developers Kit, which can be downloaded from www.sr-research.com/support website. Note that you need to register and wait for your account to be activated. Next, follow instructions to install EyeLink Developers Kit for your platform. The forum thread should be under SR Support Forum › Downloads › EyeLink Developers Kit / API › Download: EyeLink Developers Kit / API Downloads (Windows, macOS, Linux).

Please note that this package will not work without Eyelink Developers Kit!

Specify location of the edfapi library
The package looks for edfapi either in the global environment (i.e., the folder is added to the PATH) or in a typical path for the OS. The typical locations are:

- For Windows: c:/Program Files (x86)/SR Research/EyeLink/libs/x64
- For Mac OSX: /Library/Frameworks
- For Linux: edpapi library is install in /usr/lib, so is in the global path.
If you installed EyeLink Developers Kit using defaults, the typical paths should work. However, you may have used a different folder for installation (relevant primarily for Windows) or it is possible that SR Research changed the defaults. In this case, you can specify path to the library as a parameter or set EDFAPI_LIB environment variable.

For detailed usage instructions and API reference, visit the **full documentation** here:  
📖 **[etformat Documentation](https://ahsankhodami.github.io/etformat/)**
VNP09GA-002 1.2.1
VIIRS/NPP Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid Search and Download Utility
Requires Python: >=3.10

# VIIRS/NPP Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid Search and Download Utility

This tool uses the `earthaccess` library to search and download VNP09GA collection 2 surface reflectance remote sensing data products from the Visible Infrared Imaging Radiometer Suite (VIIRS).

[Gregory H. Halverson](https://github.com/gregory-halverson-jpl) (they/them)<br>
[gregory.h.halverson@jpl.nasa.gov](mailto:gregory.h.halverson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

## Installation

Install the `VNP09GA-002` package with a dash in the name from PyPi using pip:

```
pip install VNP09GA-002
```

## Usage

Import the `VNP09GA_002` package with an underscore in the name:

```
import VNP09GA_002
```

See the [example notebook](Searching%20VNP09GA.002%20with%20earthaccess.ipynb) for usage.

## References

NASA Land Processes Distributed Active Archive Center (LP DAAC). 2021. VIIRS/NPP Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid V002. NASA EOSDIS Land Processes DAAC. doi:[10.5067/VIIRS/VNP09GA.002](https://doi.org/10.5067/VIIRS/VNP09GA.002).