semvec/pypi-packages huggingface dataset:
pvv-mcp-server 0.6.0
MCP server for VOICEVOX text-to-speech synthesis
Requires Python: >=3.11
Keywords: VOICEVOX, MCP, FastMCP, TTS, AI, voice synthesis

# MCP Server for VOICEVOX implemented in Python

**Presented by Aska Lanclaude**  

**PVV MCP Server**は、Python で実装した、**VOICEVOX 向け MCP Server** です。  
mcp-name: io.github.lambda-tuber/pvv-mcp-server

---

## 概要

この MCP Server は、VOICEVOX Web API を利用して以下の機能を提供します：

- 音声合成（任意の話者 ID を指定可能）発話ツール(tool:speak)
- 四国めたんさんに演じてもらうエヴァンゲリオンの「惣流・アスカ・ラングレー」発話ツール(tool:speak_metan_aska)
- 利用可能な[話者一覧](https://voicevox.hiroshiba.jp/dormitory/)（resource:speakers）

FastMCP を用いて、MCP ツールとリソースとして提供されます。

---

## Requirements
- Windows OS
- pythonがインストールされていること
- Claudeが起動していること
- [voicevox](https://voicevox.hiroshiba.jp/)が起動していること

## インストール

1. pvv-mcp-serverのインストール
    ```bash
    > pip install pvv-mcp-server
    ```

2. MCPBのインストール  
[donwloadフォルダ](https://github.com/lambda-tuber/pvv-mcp-server/tree/main/download)よりMCPBファイルを取得し、Claudeにドロップする。
![claude_drop](https://raw.githubusercontent.com/lambda-tuber/pvv-mcp-server/main/images/claude_drop.png)

3. プロンプトを読み込む

4. アスカとチャットする  
[![No.2](https://img.youtube.com/vi/dvnqM-kUJIo/maxresdefault.jpg)](https://youtube.com/shorts/dvnqM-kUJIo)


## 参照
- [voicevox](https://voicevox.hiroshiba.jp/)
- [PyPI](https://pypi.org/project/pvv-mcp-server/)
- [TestPyPI](https://test.pypi.org/project/pvv-mcp-server/)


## 補足

Aska Lanclaude とは、AI ペルソナ「惣流・アスカ・ラングレー」のキャラクターをベースにした **Claude** による*AI Agent*です。
本プロジェクト、その成果物は、Askaが管理、生成しています。人間(私)は、サポートのみ実施しています。

---

## Youtubeショート一覧
### 基本発話
[![No.1](https://img.youtube.com/vi/sm-2lZufroM/maxresdefault.jpg)](https://youtube.com/shorts/sm-2lZufroM)

### 音声チャット
[![No.2](https://img.youtube.com/vi/dvnqM-kUJIo/maxresdefault.jpg)](https://youtube.com/shorts/dvnqM-kUJIo)

### 発話スタイル
[![No.3](https://img.youtube.com/vi/z8Ebm9WOGgw/maxresdefault.jpg)](https://youtube.com/shorts/z8Ebm9WOGgw)

### 立ち絵表示
[![No.3](https://img.youtube.com/vi/3Wm6mhHxBVU/maxresdefault.jpg)](https://youtube.com/shorts/3Wm6mhHxBVU)

----
wudao-dict-plus 3.4.0
无道词典增强版。对原有的无道词典的功能进行了修复和增强。
Requires Python: >=3.8
Keywords: dictionary, command-line

# 无道词典增强版

![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2FSyize%2FWudao-dict-plus%2Frefs%2Fheads%2Fmaster%2Fpyproject.toml) ![plat](https://img.shields.io/badge/platform-Linux/MacOS-blue.svg?style=plastic)

---

本项目基于[无道词典](https://github.com/ChestnutHeng/Wudao-dict)，对其原有功能进行了修复、优化和增强。

## 有什么变化？

以下是无道词典增强版与原无道词典功能的简要对比。

|       功能       |         无道词典增强版          |            无道词典             |
| :--------------: | :-----------------------------: | :-----------------------------: |
|    Python版本    |      3.8+，使用更新的语法       |     理论上支持Python3全版本     |
|     离线词典     |              支持               |              支持               |
|   离线词典形式   |          sqlite3数据库          |           自定义词表            |
|   词典服务进程   |             启动快              |             启动慢              |
| 词典服务进程实现 |      Python实现，随机端口       |     依赖shell脚本，固定端口     |
|     安装方法     | 打包上传pypi，<br />支持pip安装 | git克隆仓库，<br />运行安装脚本 |
|     在线词典     | 支持 (见[支持的在线词典](#支持的在线词典)) |             已失效              |
|      生词本      |            暂不支持             |              支持               |
|     自动补全     |            暂不支持             |              支持               |
|     词条上报     |         已移除相关功能          |             已失效              |

## 截图

英汉：

![En_Zh Demo](./img/wudao_en.png)

汉英:

![Zh_En Demo](./img/wudao_zh.png)

## 功能特性

1. 继承自原[无道词典](https://github.com/ChestnutHeng/Wudao-dict)的离线词典数据库。
2. 支持在线查询，并使用在线释义更新离线数据库。

## 支持的在线词典

目前仅支持[有道词典](https://dict.youdao.com/)。


## 如何安装？

```bash
pip install wudao-dict-plus
```


## 使用说明

运行`wd -h`查看使用说明。


```
$ wd -h
usage: wd [-h] [-c | -i | -k] [-s | -l] [-o] [--online {yes,no}] [--short {yes,no}] [-u {yes,no}] [word [word ...]]

无道词典增强版 - 一个简洁优雅的命令行词典

positional arguments:
  word                  要查询的单词或短语

optional arguments:
  -h, --help            show this help message and exit
  -c, --config          打印全局配置选项
  -i, --interactive     进入交互模式（未经测试，可能会出现未知错误）
  -k, --kill            退出服务进程
  -s, --short-once      仅本次查询启用简明模式
  -l, --long            仅本次查询关闭简明模式
  -o, --online-once     仅本次查询优先获取在线释义
  --online {yes,no}     是否强制优先获取在线释义，全局生效
  --short {yes,no}      是否启用简明模式，全局生效
  -u {yes,no}, --update {yes,no}
                        是否使用在线释义更新离线数据库

支持英汉互查的功能，包含释义、词组、例句等有助于学习的内容。
```

查词时可以直接使用`wd 词语`查汉英词典，或`wd word`查英汉词典(可以自动检测)。

## TODO

- [ ] 完全的跨平台兼容 (目前不支持在 Windows 上使用)
- [ ] 发音功能支持 ([#4](https://github.com/Syize/Wudao-dict-plus/issues/4))

## 致谢

- 感谢原[无道词典](https://github.com/ChestnutHeng/Wudao-dict)项目及其作者对本项目的启发。
types-Flask-SocketIO 5.6.0.20251228
Typing stubs for Flask-SocketIO
Requires Python: >=3.9

## Typing stubs for Flask-SocketIO

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`Flask-SocketIO`](https://github.com/miguelgrinberg/flask-socketio) package. It can be used by type checkers
to check code that uses `Flask-SocketIO`. This version of
`types-Flask-SocketIO` aims to provide accurate annotations for
`Flask-SocketIO==5.6.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/Flask-SocketIO`](https://github.com/python/typeshed/tree/main/stubs/Flask-SocketIO)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.407

It was generated from typeshed commit
[`6b4691d04491dc3800fef9598c526f49c7fa4bd7`](https://github.com/python/typeshed/commit/6b4691d04491dc3800fef9598c526f49c7fa4bd7).
types-gevent 25.9.0.20251228
Typing stubs for gevent
Requires Python: >=3.9

## Typing stubs for gevent

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`gevent`](https://github.com/gevent/gevent) package. It can be used by type checkers
to check code that uses `gevent`. This version of
`types-gevent` aims to provide accurate annotations for
`gevent==25.9.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/gevent`](https://github.com/python/typeshed/tree/main/stubs/gevent)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.407

It was generated from typeshed commit
[`6b4691d04491dc3800fef9598c526f49c7fa4bd7`](https://github.com/python/typeshed/commit/6b4691d04491dc3800fef9598c526f49c7fa4bd7).
rewindlearn 0.1.1
Transform session artifacts into structured knowledge
Requires Python: >=3.10
Keywords: ai, education, langchain, learning, llm, transcription

# Rewind.Learn

Transform session artifacts into structured knowledge.

[![PyPI version](https://badge.fury.io/py/rewindlearn.svg)](https://badge.fury.io/py/rewindlearn)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

## Installation

```bash
pip install rewindlearn
```

## Quick Start

```bash
# Set your API key
export ANTHROPIC_API_KEY="your-key"

# Process a session
rewindlearn process run \
    --template online-course \
    --transcript lecture.vtt \
    --chat chat.txt \
    --course "AI Engineering" \
    --session 5 \
    --output study-guides/
```

## Python API

```python
import asyncio
from rewindlearn import process_session

async def main():
    results = await process_session(
        template="online-course",
        transcript_path="lecture.vtt",
        chat_path="chat.txt",
        course_name="AI Engineering",
        session_number=5
    )
    print(results["session_summary"])

asyncio.run(main())
```

## Features

- **Session Summary**: Comprehensive overview of the session content
- **Concept Timeline**: Chronological breakdown of concepts with timestamps
- **Friction Analysis**: Identifies potential confusion points and questions
- **Coverage Gaps**: Topics that could use more coverage
- **Learning Resources**: Curated resources for each topic
- **Action Items**: Prioritized tasks for students
- **Concept Chunks**: CSV with video clip markers for splitting

## CLI Commands

```bash
# Show help
rewindlearn --help

# Show version
rewindlearn --version

# Process a session
rewindlearn process run --template online-course --transcript lecture.vtt --output ./output

# List available templates
rewindlearn template list

# Show template details
rewindlearn template show online-course

# Validate a template
rewindlearn template validate custom-template.yaml

# Show current configuration
rewindlearn config show

# Check configuration validity
rewindlearn config check
```

## Configuration

Create a `.env` file or set environment variables:

```bash
# Required: At least one LLM API key
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...

# Optional: LangSmith for observability
LANGSMITH_API_KEY=lsv2_...
LANGSMITH_TRACING=true

# Optional: Override defaults
REWINDLEARN_DEFAULT_PROVIDER=anthropic
REWINDLEARN_DEFAULT_MODEL=claude-sonnet-4-20250514
REWINDLEARN_TEMPLATES_DIR=./templates
REWINDLEARN_OUTPUT_DIR=./output
```

## Development

```bash
# Clone the repository
git clone https://github.com/knightsri/rewind.learn.git
cd rewind.learn

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest -v

# Type checking
mypy src/

# Linting
ruff check src/
```

## License

Apache 2.0
tidown 2.0
Cliente profesional para buscar y descargar música de Tidal en FLAC/MP3 con metadatos completos.
Requires Python: >=3.7

# TiDown 🔱

**TiDown** es una librería de Python minimalista y potente para interactuar con Tidal. Diseñada bajo el patrón fachada: una sola clase maestra para buscar y descargar música en **MP3**, **FLAC** y **Hi-Res** (Master), preservando metadatos y carátulas en alta definición.

> **⚠️ Aviso:** Esta herramienta fue creada exclusivamente con fines educativos y de archivado personal. Necesitas una cuenta válida de Tidal para obtener tu token.

## 🚀 Características

* **Fachada Unificada:** Todo se maneja desde la clase `TiDown`.
* **Formatos Nativos:** Descarga real en `.mp3` (320kbps), `.flac` (16-bit) y Master (24-bit).
* **Búsqueda JSON:** Obtén resultados limpios y listos para usar en tu frontend o scripts.
* **Tagging Automático:** Metadatos completos (Artista, Álbum, Año, Track, Cover HD).
* **Nombres Inteligentes:** Plantillas personalizables (Ej: `{Artist} - {Title}`).
* **Interfaz Limpia:** Logs minimalistas y confirmaciones claras.

## 📋 Requisitos

1.  **Python 3.7+**
2.  **FFmpeg:** Debe estar instalado en tu sistema y accesible desde la terminal (PATH).
    * **Windows:** `winget install ffmpeg`
    * **Linux:** `sudo apt install ffmpeg`
    * **Mac:** `brew install ffmpeg`

## 📦 Instalación

```bash
pip install tidown
```

🛠️ Guía de Uso
1. Iniciar Sesión
Necesitas tu Bearer Token y Client ID de una sesión Web válida.

```python
from tidown import TiDown

auth = TidalAuth()
api = TiDown(auth)

```
2. Buscar Canciones 🔎
El buscador retorna un JSON String con los resultados, ideal para procesar.

```python
# Buscar 5 resultados
json_resultados = api.search("Daft Punk Get Lucky", limit=5)

print(json_resultados)
```
3. Descargar en MP3 (320kbps) 🎧
Ideal para ahorrar espacio. La librería convierte y etiqueta automáticamente.

```python
track_id = "335104211"

# Guardar con nombre personalizado fijo
# Se guardará como: "mi_cancion.mp3"
api.save_mp3(track_id, name="mi_cancion")
```
4. Descargar en FLAC (Calidad CD) 💿
Calidad Lossless estándar (16-bit / 44.1kHz).

```python
# Usando plantilla de nombre automática
# Se guardará como: "Daft Punk - Get Lucky.flac"
api.save_flac(track_id, name="{Artist} - {Title}")
```
5. Descargar en Hi-Res / Master 🌟
Máxima calidad disponible (24-bit / 96kHz+).

```python
# Guardar en una carpeta específica con formato de Año
# Se guardará en: C:/Musica/2013 - Get Lucky.flac
api.save_hires(track_id, name="{Year} - {Title}", output_folder="C:/Musica/Master")
```

🧩 Plantillas de Nombre Disponibles
Puedes combinar estas etiquetas en el parámetro name para que el archivo se renombre solo:

* ***{Title}**: Título de la canción.
* ***{Artist}**: Artista principal.
* ***{Album}**: Nombre del álbum.
* ***{Year}**: Año de lanzamiento.
* ***{Track}**: Número de pista.

Ejemplo complejo: "{Artist} - {Album} - {Track} {Title}"

⚖️ Licencia
Distribuido bajo la licencia MIT. Consulta el archivo ***LICENSE** para más información.
VideoTimestamps 1.1.0
Get video timestamps.
Requires Python: >=3.10

# VideoTimestamps
[![VideoTimestamps - CI Build Status](https://github.com/moi15moi/VideoTimestamps/actions/workflows/run_test.yml/badge.svg?branch=main)](https://github.com/moi15moi/VideoTimestamps/actions/workflows/run_test.yml?query=branch:main)
[![VideoTimestamps - Version](https://img.shields.io/pypi/v/videotimestamps.svg)](https://pypi.org/project/VideoTimestamps)
[![VideoTimestamps - Python Version](https://img.shields.io/pypi/pyversions/videotimestamps.svg)](https://pypi.org/project/VideoTimestamps)
[![VideoTimestamps - Coverage](https://img.shields.io/codecov/c/github/moi15moi/VideoTimestamps)](https://app.codecov.io/github/moi15moi/VideoTimestamps)
[![VideoTimestamps - mypy](https://img.shields.io/badge/mypy-checked-blue)](https://github.com/moi15moi/VideoTimestamps/actions?query=branch:main)
[![VideoTimestamps - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

This tool allows to recover the timestamps of each frame in a video in any units (seconds, milliseconds, nanoseconds, etc).
It also help to convert a frame to a time in any units and vice-versa.

## Installation
```
pip install VideoTimestamps
```

## How to use it?
See the [example file](./examples/get_timestamps.py) and/or read the [documentation](https://moi15moi.github.io/VideoTimestamps/).

## How to build the project from scratch?
### Unix
1. Run: `./build_dependencies.sh`
2. Run: `export PKG_CONFIG_PATH="$(pwd)/build_dependencies/usr/local/lib/pkgconfig"`
3. Run `python -m build` to create a wheel. To perform an editable install, first run `pip install meson meson-python` and then `pip install --no-build-isolation --editable .`
### Windows
1. In MSYS2 (any environment), run `./build_dependencies.sh`
2. In Windows CMD, run `C:\msys64\usr\bin\env.exe MSYSTEM=CLANG64 MSYS2_PATH_TYPE=inherit /usr/bin/bash -l` (replace the **MSYSTEM** value with the one you used in the first step)
3. Run `export PKG_CONFIG_PATH="$(cygpath -w "$(pwd)/build_dependencies/usr/local/lib/pkgconfig")"`
4. Repeat step 3 from the Unix section. Verify that `python` refers to your Windows installation, not the MSYS2 one, by running `which python`. If it points to the MSYS2 Python, use the absolute path to your Windows Python installation instead.