semvec/pypi-packages huggingface dataset:
vesdk 1.0.1
火山引擎SDK，支持大模型文本生成、视觉模型文生图、语音合成、视频生成等API
Requires Python: >=3.7

# vesdk

火山引擎SDK，支持以下功能：

- 大模型文本生成(chat接口)
- 视觉模型文生图接口
- 视觉模式生成视频video接口
- 会话记忆相关接口
- 知识库相关接口

## 安装

```bash
pip install vesdk
```

## 使用示例

```python
from vesdk import VolcEngineClient

# 初始化客户端
client = VolcEngineClient(
    access_key_id="your_access_key_id",
    secret_access_key="your_secret_access_key",
    api_key="your_api_key"
)


# 文本生成示例
response = client.text_generation.chat(
    model="doubao-1-5-pro-32k-250115",
    messages=[
        {"role": "user", "content": "你好！"}
    ]
)

print(response)
```

## 文档

请参考[官方文档](https://www.volcengine.com/docs)获取更多信息。
fing-agent-api 1.0.3
Fing Agent local API library.
Requires Python: >=3.11.1

# Fing Agent API

**FingAgent** is a Python library for interfacing with the local APIs of the Fingbox. It allows you to easily interact with the devices and contacts managed by your Fingbox.

## Requirements

- Python 3.11.1 or higher
- [httpx](https://www.python-httpx.org/) (for asynchronous HTTP requests)

## Example

Here is a complete example of using the library:

```python
from fing_agent_api import FingAgent
import asyncio

async def main():
    # Configure the Fing agent
    agent = FingAgent(ip='your_agent_ip', port=49090, key='your_api_key')
        
    # Get devices
    devices = await agent.get_devices()
    
    # Get contacts (only available for desktops)
    contacts = await agent.get_contacts()
    
    # Get agent (only available for fingboxes and agents)
    agent_info = await agent.get_agent_info()


if __name__ == "__main__":
    asyncio.run(main())
```
auditory-models 0.1.1
Computation of auditory models
Requires Python: >=3.12
Keywords: audio, quality, speech, perception, intelligibility, model, auditory, stoi, gpsm, snr

# auditory_models

## Description

This repository provides multiple packages to compute auditory models, including
- Short term Objective Intelligibility ([STOI](https://ieeexplore.ieee.org/abstract/document/5713237))
- Generalized Power Spectrum Model for audio quality ([GPSMq](https://ieeexplore.ieee.org/abstract/document/8708700))


## Installation
`pip install auditory_models`

## Usage
```
from auditory_models import STOI, GPSMq
import soundfile as sf

reference, fs_ref = sf.read("reference.wav")
degraded, fs_dgr = sf.read("degraded.wav")
if fs_ref != fs_dgr:
    raise ValueError("Sample rates must be equal!")

stoi = STOI()
gpsmq = GPSMq(binaural=False)

stoi.process(reference, degraded, fs_ref)
gpsmq.process(reference, degraded, fs_ref)

```

## Support
Regarding issues please feel free to contact me via 
<a href="mailto:max.zimmermann@tugraz.at">max.zimmermann@tugraz.at</a>

## Contributing
Any contribution is welcome. 

## Authors and acknowledgment
Author: Max Zimmermann\
Credits to: 
- The developers of the original Matlab implementations
    - STOI: Cees Taal 
    - GPSMq: Thomas Biberger and Jan-Hendrik Fleßner
- Manuel Pariente for the original Python implementation of STOI

## License
This project is licensed under the GNU General Public License v3 (GPLv3). For further info see file `COPYING`.
documentation-generator 0.1.0
Add your description here
Requires Python: >=3.13

# documentation-generator

Tiny, composable Markdown document builder with optional HTML/PDF export.

Features
- Build documents by chaining: paragraphs, tables, lists, code blocks, and remarks.
- Render to Markdown; convert to HTML (Markdown lib) or PDF (WeasyPrint).
- Tables render in GitHub‑style Markdown (via tabulate).

Requirements
- Python 3.13+

Installation
```bash
pip install -e .           # install project (editable)
# or just runtime deps
pip install tabulate Markdown
# to enable PDF export
pip install weasyprint
# dev/test extras
pip install -e .[dev]
```

Quick Start
```python
from document import Document
import pandas as pd

doc = (
    Document()
      .add_title("Sample Doc")
      .add_paragraph("Intro text", heading="Introduction")
      .add_list(["alpha", "beta", "gamma", "delta"], heading="Things")
      .add_codeblock("print('hello')", language="python")
      .add_remark("Remember to pin versions.", kind="TIP")
)

# Tables accept pandas or polars DataFrame
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
doc.add_table(df, heading="Data", footer="auto‑generated")

# Render as Markdown string
markdown_text = doc.render()
print(markdown_text)

# Save to files
doc.save_as("md")    # writes document.md
doc.save_as("html")  # writes document.html
# Requires: pip install weasyprint
doc.save_as("pdf")   # writes document.pdf
```

Remarks (Admonitions)
- `add_remark(text, kind)` supports: `INFO`, `WARNING`, `DANGER`, `TIP`.
- Uses Markdown admonition syntax, e.g. `!!! warning`.
- HTML conversion enables `admonition`, `tables`, and `fenced_code` extensions.

Testing
```bash
pip install -e .[dev]
pytest -q
```