semvec/pypi-packages huggingface dataset:
UnityPyLive2DExtractor 0.2.2
General purpose, single-file Unity Live2D Asset recovery tool
Requires Python: >=3.10

# UnityPyLive2DExtractor
[![Windows Build](https://github.com/mos9527/UnityPyLive2DExtractor/actions/workflows/python-publish.yml/badge.svg)](https://github.com/mos9527/UnityPyLive2DExtractor/blob/main/.github/workflows/python-publish.yml) 
[![Releases](https://img.shields.io/github/downloads/mos9527/UnityPyLive2DExtractor/total.svg)](https://GitHub.com/mos9527/UnityPyLive2DExtractor/releases/) 
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) 

General purpose [Live2D](https://www.live2d.com/) Asset recovery tool built w/ [UnityPy](https://github.com/K0lb3/UnityPy) and [sssekai](https://github.com/mos9527/sssekai)

As the name suggests, this project is heavily inspired by [Perfare/UnityLive2DExtractor](https://github.com/Perfare/UnityLive2DExtractor). With a few key differences:
- All Live2D types are implemented with [dumped TypeTree](https://github.com/mos9527/UnityPyLive2DExtractor/blob/main/external/typetree_cubism.json) and [generated types](https://github.com/mos9527/UnityPyLive2DExtractor/blob/main/typetree_codegen.py). This should help with compatibility issues.
    - Do note, however, that you may need to update the TypeTree if the Live2D version changes.
    - Generate the TypeTree with [UnityPyTypetreeCodegen](https://github.com/mos9527/UnityPyTypetreeCodegen) and replace the existing TypeTree at `UnityPyLive2DExtractor/generated`
    - A valid command could be something like
        ```bash
        UnityPyTypetreeCodegen --unity-version 2019.4.34f1 --asm-dir Game/Managed --outdir .\UnityPyLive2DExtractor\generated
        ```
- New (not necessarily better) asset discovery method. Though proven to be more reliable in some cases.

## Installation
- Install the script from PyPI
```bash
pip install UnityPyLive2DExtractor
```
- Or, you can use the pre-built executables for Windows from [Releases](https://github.com/mos9527/UnityPyLive2DExtractor/releases/).
## Usage
```bash
UnityPyLive2DExtractor <input> <output>
```
Where `<input>` is the path to your game's path, and `<output>` is the directory to extract the Live2D assets to.
## References
- https://github.com/Perfare/UnityLive2DExtractor
- https://github.com/K0lb3/TypeTreeGenerator
- https://github.com/K0lb3/UnityPy
lsp-sim 0.1.1
Locally stationary process simulation
Requires Python: >=3.11

# lsp-sim

**Locally Stationary Process Simulation in Python**

This package provides tools to simulate *locally stationary processes (LSPs)* in Silverman’s sense with a flexible covariance structure: stationary correlation part $r(\tau)$ and time-varying power part $q(\eta)$, both chosen as Gaussian functions. 

It includes the functions:

-`lsp_f0_sim` for simulating LSP realizations

-`wv_lsp` for calculating the Wigner-Ville distribution of the LSP


## Jupyter Demo

Use the simple Jupyter notebook demo `demo_lsp_sim.ipynb` for exploring parameter effects.  
Reproducible simulations can be obtained by fixing random seeds.  
Full source code and notebook demo are available on GitHub https://github.com/RacheleAnderson/lsp-sim/

The demo visualizes:

- Simulated realizations showing different behaviors of the realizations when changing the parameters (3 cases)
- Covariance matrices for the 3 cases of different parameter configurations
- Wigner-Ville distribution for the 3 cases of different parameter configurations

## Background 

The model is presented in the research paper

Anderson, R., Sandsten, M. Time-frequency feature extraction for classification of episodic memory. EURASIP J. Adv. Signal Process. 2020, 19 (2020).

available online (Open Access) at: https://doi.org/10.1186/s13634-020-00681-8

Previous Matlab code is available for repeating the study, see https://github.com/RacheleAnderson/lsp-time-frequency. 

This Python package is a simpler version with the limited scope of simulating LSP realizations according to the model.  

## License  
MIT License — see [LICENSE](LICENSE) for details.
quantportfolio 0.0.3
A toolkit for portfolio optimization
Requires Python: >=3.9

# quantportfolio
A simple toolkit for portfolio optimization (Sharpe ratio, efficient frontier).

## Installation
```bash
pip install quantportfolio
```
## Example
```bash
from quantportfolio import get_portfolio_returns, max_sharpe_ratio, portfolio_performance

# Define stock pool
tickers = ["NVDA", "AMZN", "META"]

# Download returns
df = get_portfolio_returns(tickers, start="2024-01-01")

# Compute annualized mean returns & covariance
returns = df.drop(columns=["Date"])
mean_returns = returns.mean() * 252
cov_matrix = returns.cov() * 252
rf = 0.036  # risk-free rate

# Optimize for max Sharpe Ratio
result = max_sharpe_ratio(mean_returns.values, cov_matrix.values, rf)
weights = result.x

# Evaluate performance
port_return, port_vol, sharpe = portfolio_performance(
    weights, mean_returns.values, cov_matrix.values, rf
)

print("Weights:", dict(zip(tickers, weights.round(4))))
print(f"Expected Annual Return: {port_return:.2%}")
print(f"Expected Volatility: {port_vol:.2%}")
print(f"Sharpe Ratio: {sharpe:.2f}")
```
## License
MIT License - see the [LICENSE](LICENSE) file for details.
tts-webui-extension.seamless-m4t 0.0.7
SeamlessM4T is a multilingual and multimodal translation model supporting text and speech

# Extension adapter for SeamlessM4T

License - the source code within this repository is licensed under the MIT license.

This extension provides a multilingual and multimodal translation model that supports text and speech translation across many languages.

## Features

- Text-to-Speech translation: Translate text from one language to speech in another language
- Audio-to-Speech translation: Translate speech from one language to speech in another language
- Support for over 100 languages for text input
- Support for over 35 languages for speech output
- High-quality speech synthesis

## Usage

### Text to Speech Translation
1. Enter your text in the input field
2. Select the source language of your text
3. Select the target language for the speech output
4. Click "Translate Text to Speech" to generate the translated speech

### Audio to Speech Translation
1. Upload an audio file containing speech
2. Select the target language for the speech output
3. Click "Translate Audio to Speech" to generate the translated speech

This extension uses the [SeamlessM4Tv2Model](https://huggingface.co/facebook/seamless-m4t-v2-large) model from HuggingFace.
tts-webui-extension.mimo-audio 0.0.5
A template extension for TTS Generation WebUI

# Mimo audio

A template extension for [TTS WebUI](https://github.com/rsxdalv/tts-webui).

## Description

This is a template extension. Replace this content with your extension's description.

## Installation

```bash
pip install git+https://github.com/rsxdalv/tts_webui_extension.mimo_audio@main
```

## Usage

1. Install the extension
2. Restart TTS Generation WebUI
3. Navigate to the Mimo audio tab

## Development

To run the extension standalone:

```bash
cd tts_webui_extension/mimo_audio
python main.py
```

## License

MIT License