semvec/pypi-packages huggingface dataset:
pyvoice 0.1.1
A real-time speech-to-text transcription tool using machine learning (NumPy), PyQt6, and faster-whisper.
Requires Python: >=3.7

# PyVoice

PyVoice is a real-time speech-to-text transcription tool using machine learning. It provides a graphical user interface for recording audio and transcribing it into text.

## Features

- Real-time audio recording  
- Live audio waveform visualization  
- Speech-to-text transcription using the Whisper model  
- User-friendly GUI built with PyQt6  

## Requirements

The script automatically checks for and installs the following required packages:

- `numpy`  
- `sounddevice`  
- `librosa`  
- `faster_whisper`  
- `PyQt6`  
- `pyqtgraph`  

## Installation

1. Ensure you have Python installed on your system.  
2. Download the PyVoice script file.  
3. Run the script. It will automatically check for and install required packages.  

## Usage

1. Run the PyVoice script to launch the application.  
2. Click **"Start Recording"** to begin audio capture.  
3. Speak into your microphone.  
4. Click **"Stop Recording"** when you're done speaking.  
5. PyVoice will process the audio and display the transcription.  

## Key Components

- **AudioRecorder**: Handles real-time audio recording.  
- **TranscriptionWorker**: Processes recorded audio and generates transcriptions.  
- **MainWindow**: Manages the graphical user interface and coordinates recording and transcription processes.  

## Notes

- PyVoice uses the **"small" Whisper model** for transcription.  
- Audio is processed at a **16kHz sample rate**.  
- The waveform display updates in real-time during recording.
lmeval-framework 0.11.4
LMEval: Large Model Evaluation Framework
Requires Python: >=3.10

# LMEval: Large Model Evaluation Framework

## Installation

```bash
pip install lmeval-framework
```

### Dev setup

LMeval use `uv` that you need to install then

```bash
uv venv
source .venv/bin/activate  # or open a new term in vscode after accepting the new venv
uv pip install -e .
```

## Disclaimer

- This is not a Google product.

- When using private datasets, remember that questions may be sent to your chosen model providers for evaluation if the requested results aren't already available. Ensure this aligns with your data policies regarding external processing of sensitive information.
andylib 0.1.0
A library for image processing functions.
Requires Python: >=3.6

# andylib

A library for image processing functions.

## Installation

```bash
pip install andylib
```

## Features

- Read TIFF image files

## Usage

```python
import numpy as np
from andylib import read_tiff

# Read a TIFF image
image = read_tiff('path/to/your/image.tiff')
print(f"Image shape: {image.shape}")
```

## Development

### Setup

1. Clone the repository:

```bash
git clone https://github.com/yourusername/andylib.git
cd andylib
```

2. Create a virtual environment and install development dependencies:

```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"
```

### Testing

Run tests with pytest:

```bash
pytest
```

### Code Formatting and Linting

This project uses pre-commit hooks to ensure code quality. After installing the development dependencies, set up the pre-commit hooks:

```bash
pre-commit install
```

This will automatically format your code with Black and check it with Flake8 before each commit. You can also run the hooks manually:

```bash
pre-commit run --all-files
```

### Building the package

```bash
python -m build
```

## CI/CD

This project uses GitHub Actions for:

- Running tests on multiple Python versions
- Linting the code
- Building and publishing the package to PyPI when a new version tag is pushed

## License

MIT License