semvec/pypi-packages huggingface dataset:
supertonic-mnn 0.1.3
CLI tool for Supertonic MNN inference
Requires Python: >=3.9

# Supertonic MNN

[![Models](https://img.shields.io/badge/🤗%20Hugging%20Face-Models-blue)](https://huggingface.co/yunfengwang/supertonic-tts-mnn)
[![Docs](https://img.shields.io/badge/Docs-GitHub%20Pages-blue)](https://vra.github.io/supertonic-mnn/)
[![PyPI](https://img.shields.io/pypi/v/supertonic-mnn)](https://pypi.org/project/supertonic-mnn/)

[English](#english) | [中文](#中文)

---

<a name="english"></a>
## English

**Supertonic MNN** is a high-performance, lightweight text-to-speech (TTS) library based on MNN. It supports both command-line interface (CLI) and Python API, making it easy to integrate into your projects.

### Features
*   **Fast Inference**: RTF ~ 0.07 on CPU.
*   **Lightweight**: Minimal dependencies.
*   **Supported Precisions**: fp32, fp16, int8.

### Available Voices
*   **M1**: Male voice 1
*   **M2**: Male voice 2
*   **F1**: Female voice 1
*   **F2**: Female voice 2

### Documentation
Full documentation is available at [Supertonic MNN Docs](https://vra.github.io/supertonic-mnn/).

### Installation

```bash
pip install supertonic-mnn
```

### Quick Usage

#### CLI
```bash
echo "Hello world" | supertonic-mnn -o hello.wav
```

#### Python API
```python
from supertonic_mnn import SupertonicTTS

# 1. Initialize
tts = SupertonicTTS()

# 2. Synthesize
# Models will be downloaded automatically if not present
audio, sample_rate = tts.synthesize("Hello world", voice="M1", output_file="hello.wav")
```

See [examples/](examples/) for more details.

### Acknowledgments
This project is based on the original [Supertonic](https://github.com/supertone-inc/supertonic/) by Supertone Inc.

---

<a name="中文"></a>
## 中文

**Supertonic MNN** 是一个基于 MNN 的高性能、轻量级文本转语音 (TTS) 库。它同时支持命令行接口 (CLI) 和 Python API，方便您将其集成到项目中。

### 特性
*   **极速推理**: CPU 上 RTF 约为 0.07。
*   **轻量级**: 依赖极少。
*   **多精度支持**: fp32, fp16, int8。

### 可用音色
*   **M1**: 男声 1
*   **M2**: 男声 2
*   **F1**: 女声 1
*   **F2**: 女声 2

### 文档
完整文档请访问 [Supertonic MNN 文档](https://vra.github.io/supertonic-mnn/)。

### 安装

```bash
pip install supertonic-mnn
```

### 快速上手

#### 命令行 (CLI)
```bash
echo "你好，世界" | supertonic-mnn -o hello.wav
```

#### Python API
```python
from supertonic_mnn import SupertonicTTS

# 1. 初始化
tts = SupertonicTTS()

# 2. 推理
# 如果模型不存在，会自动下载
audio, sample_rate = tts.synthesize("你好，世界", voice="M1", output_file="hello.wav")
```

更多详情请参阅 [examples/](examples/) 目录。

### 致谢
本项目基于 Supertone Inc. 的原始 [Supertonic](https://github.com/supertone-inc/supertonic/) 工作。
applyllm 0.0.9rc3
A python package to apply opensource LLM in local CUDA environment
Requires Python: >=3.12

# applyllm: Training and Serving LLMs and SLMs

This repository contains the code for the [applyllm](https://pypi.org/project/applyllm/) python package, which supports loading and training open‑source LLM models such as LLaMA 2, Mixtral 8x7B, and etc. on CUDA and MPS.

## Install the package
```shell
pip install applyllm
```
cfxdb 25.12.2
Crossbar.io Database - Core database access classes for Crossbar.io
Requires Python: >=3.11
Keywords: crossbar, database, ethereum, lmdb, wamp, web3, xbr

# cfxdb

[![PyPI](https://img.shields.io/pypi/v/cfxdb.svg)](https://pypi.python.org/pypi/cfxdb)
[![Python](https://img.shields.io/pypi/pyversions/cfxdb.svg)](https://pypi.python.org/pypi/cfxdb)
[![CI](https://github.com/crossbario/cfxdb/workflows/main/badge.svg)](https://github.com/crossbario/cfxdb/actions?query=workflow%3Amain)
[![Docs](https://readthedocs.org/projects/cfxdb/badge/?version=latest)](https://cfxdb.readthedocs.io/en/latest/)
[![License](https://img.shields.io/pypi/l/cfxdb.svg)](https://github.com/crossbario/cfxdb/blob/master/LICENSE)
[![Downloads](https://img.shields.io/pypi/dm/cfxdb.svg)](https://pypi.python.org/pypi/cfxdb)

---

**cfxdb** is a Crossbar.io Python support package with core database
access classes written in native Python. The package allows direct
in-memory data access from Python programs (including Jupyter notebooks)
to CrossbarFX edge node data:

-   persisted WAMP event history
-   persisted router tracing data
-   XBR market maker transactions database
-   XBR network backend database
-   WAMP session cache
-   custom user, embedded object databases

------------------------------------------------------------------------

## Contents

::: {.toctree maxdepth="2"}
OVERVIEW.md ai/index
:::

------------------------------------------------------------------------

*Copyright (c) typedef int GmbH. Licensed under MIT. WAMP, Crossbar.io
and XBR are trademarks of typedef int GmbH (Germany).*
omicron-gap 0.0.56
Utilities to analyze and fill gaps in omicron triggers
Requires Python: <3.13,>=3.9

# Omicron gap related utilities

This package is a collection of utilities to compliment LIGO's detector 
characterization group Omicron trigger generator task. Our goal is to
visualize the times when we should have triggers and any gaps.

There are also programs to submit htCondor jobs to fill in 
those gaps.

* __omicron-batch-merge-dir__ - to reduce the number of files, this program 
  safely merges contiguous trigger files in a metric day
* __omicron-channel-check__ - examines each channel in our configuration file 
  to confirm it is available in current frame files.
* **omicron-compare-dirs** - Confirm that 2 directories have trigger files that cover 
  the same time period and have the same number of triggers. Works with merge programs
* **omicron-find-gaps** - Check DQ segments, frame availability and trigger file
  ecistance to determine which segments if any need to be reprocessed
* **omicron-gaps** 
* **omicron-gap-analysis**
* **omicron-merge-days**
* **omicron-metric-day-merge**
* **omicron-plot-gaps**
* **omicron-segfile-print**
genflux 0.1.0
GenFlux Python SDK
Requires Python: >=3.10

# GenFlux

GenFlux Python SDK.

## Installation

```bash
pip install genflux
```

## Quick Start

```python
import genflux

# Simple greeting
print(genflux.hello())          # Hello, World!
print(genflux.hello("GenFlux")) # Hello, GenFlux!

# Check version
print(genflux.version())        # 0.1.0
```

## Using the Client

```python
from genflux import GenFlux

client = GenFlux()

# Test the SDK
print(client.ping())   # {'status': 'ok', 'message': 'GenFlux SDK is working!'}

# Echo a message
print(client.echo("Hello!"))  # Hello!

# Simple calculation
print(client.add(1, 2))  # 3
```

## Configuration (for API features)

For API features that require authentication:

### 1. Direct API Key

```python
from genflux import GenFlux

client = GenFlux(api_key="your-api-key")
```

### 2. Environment Variable

Set the `GENFLUX_API_KEY` environment variable:

```bash
export GENFLUX_API_KEY="your-api-key"
```

Then initialize without arguments:

```python
from genflux import GenFlux

client = GenFlux()
```

## Development

### Setup

```bash
# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync
```

### Running Tests

```bash
uv run pytest
```