semvec/pypi-packages huggingface dataset:
onelab-tts 0.2.0
A Text-to-Speech and Podcast generation library using Chatterbox Turbo TTS
Requires Python: <3.12,>=3.8

# OneLab TTS Library

OneLab is a powerful Python library for Text-to-Speech (TTS) and Podcast generation, built on top of `ChatterboxTurboTTS`. It allows you to easily convert text to speech using various voices and create multi-speaker podcasts with custom logging and progress tracking.

## Features

*   **Text-to-Speech Conversion**: Convert individual text segments to audio using specific voices.
*   **Podcast Generation**: Create full conversations with multiple speakers.
*   **Dynamic Voice Loading**: Automatically detects available voices from the package's `sample` directory.
*   **Custom Logging**: Detailed logging and progress tracking during generation.
*   **Modular Design**: Clean and extensible architecture.

## Installation

You can install the package using pip:

```bash
pip install onelab
```

Or install directly from the source:

```bash
git clone https://github.com/satadeep3927/onelab.git
cd onelab
pip install .
```

## Usage

### Initialization

To use the library, simply initialize the `OneLab` class. The library automatically locates the bundled voice samples.

```python
from onelab import OneLab

# Initialize OneLab
onelab = OneLab(device="cpu") 
```

### Listing Available Voices

You can list all voices detected in the `sample` directory:

```python
voices = onelab.tts.list_voices()
print("Available voices:", voices)
```

### Converting Text to Speech

To convert a single line of text:

```python
wav = onelab.tts.convert("Hello, world!", voice="charlie")
# Save the output
import torchaudio
torchaudio.save("output.wav", wav, onelab.tts.model.sr)
```

### Creating a Podcast

To create a multi-speaker podcast, define your conversation segments and use the `podcast.create` method:

```python
from onelab import ConversationInput

data: ConversationInput = {
    "segments": [
        {"voice": "charlie", "text": "Hello Emilia!"},
        {"voice": "emilia", "text": "Hi Charlie! How are you?"},
    ]
}

# Generate the podcast
podcast_wav = onelab.tts.podcast.create(data)

# Save the podcast
torchaudio.save("podcast.wav", podcast_wav, onelab.tts.model.sr)
```

## API Reference

### `OneLab`

The main entry point for the library.

*   `__init__(device: str = "cpu")`: Initializes the library.

### `TextToSpeech` (accessed via `onelab.tts`)

Handles TTS operations.

*   `list_voices() -> List[str]`: Returns a list of available voice names.
*   `convert(text: str, voice: str) -> torch.Tensor`: Generates audio for the given text and voice.

### `Podcast` (accessed via `onelab.tts.podcast`)

Handles podcast generation.

*   `create(args: ConversationInput) -> torch.Tensor`: Generates a concatenated audio waveform for the entire conversation.

## License

MIT
fastapi-launch-tool 0.1.4
A CLI tool to scaffold professional FastAPI projects
Requires Python: >=3.8
Keywords: fastapi, cli, scaffold, project-generator, uv

# 🚀 FastAPI Launch

A powerful CLI tool to scaffold production-ready FastAPI projects with best practices built-in.

[![PyPI version](https://badge.fury.io/py/fastapi-launch-tool.svg)](https://pypi.org/project/fastapi-launch-tool/)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## ✨ Features

- 📁 **Professional Project Structure** - Organized, scalable architecture
- ⚡ **Fast Setup** - Uses `uv` for lightning-fast package management
- 🔧 **Pre-configured Components**:
  - RESTful API Router (v1)
  - Pydantic Settings & Validation
  - SQLAlchemy Database Setup (async/sync ready)
  - Testing Framework
  - Logging Configuration
- 🎯 **Best Practices** - Production-ready code patterns

## 📋 Prerequisites

| Requirement | Version | Installation |
|-------------|---------|--------------|
| Python | 3.8+ | [python.org](https://www.python.org/downloads/) |
| uv | Latest | See [Installing uv](#-installing-uv) |

## 📦 Installation

**Windows:**
```bash
pip install fastapi-launch-tool
```

**macOS / Linux:**
```bash
pip3 install fastapi-launch-tool
```

## 🛠️ Usage

Create a new FastAPI project:

```bash
fastapi-launch <project_name>
```

**Example:**

```bash
fastapi-launch my-awesome-api
```

This generates a complete project structure:

```
my-awesome-api/
├── .venv/
├── app/
│   ├── api/
│   │   └── v1/
│   │       └── router.py
│   ├── core/
│   │   ├── config.py
│   │   └── logging.py
│   ├── db/
│   │   └── database.py
│   ├── models/
│   ├── schemas/
│   ├── services/
│   ├── tests/
│   ├── utils/
│   └── main.py
├── .gitignore
├── pyproject.toml
└── README.md
```

## 🚀 Quick Start

After creating your project:

**macOS / Linux:**
```bash
cd my-awesome-api
source .venv/bin/activate
uvicorn app.main:app --reload
```

**Windows:**
```bash
cd my-awesome-api
.venv\Scripts\activate
uvicorn app.main:app --reload
```

Open your browser at `http://127.0.0.1:8000` 🎉

## 📥 Installing uv

`uv` is required for project scaffolding. Install it using:

**macOS / Linux:**
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

**Windows (PowerShell):**
```powershell
irm https://astral.sh/uv/install.ps1 | iex
```

## 📄 License

MIT License - see [LICENSE](LICENSE) for details.

---

<p align="center">
  Made with ❤️ by <a href="https://github.com/krishidevani">Krishi Devani</a>
</p>