semvec/pypi-packages huggingface dataset:
indic-asr-onnx 0.2.1
Quantized IndicConformer ASR models for multiple Indian languages
Requires Python: >=3.8
Keywords: asr, speech-recognition, indic-languages, multilingual, conformer, ctc, rnnt, onnx, quantized

# Indic ASR Quantized

A helper package to use Quantized Indic ASR (Automatic Speech Recognition) for multiple Indic languages.

The original model was developed by AI4Bharat and can be found [here](https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual)

## Installation

### GPU Installation
```bash
pip install uv
uv pip install indic-asr-onnx 

# Specify --extra-index-url if needed
```

### CPU-only Installation (Recommended for limited resources)
```bash
pip install uv
uv pip install indic-asr-onnx --extra-index-url https://download.pytorch.org/whl/cpu
```

## Quick Start

```python
from indic_asr_onnx import IndicTranscriber

# Initialize (downloads model automatically)
transcriber = IndicTranscriber()

# Transcribe audio using CTC head
text = transcriber.transcribe_ctc("audio.wav", "hi")  # Hindi
print(text)

# Transcribe audio using RNN-T head
text = transcriber.transcribe_rnnt("audio.wav", "hi")  # Hindi
print(text)
```

## Supported Languages

- Assamese (as)
- Bengali (bn)
- Bodo (brx)
- Dogri (doi)
- Gujarati (gu)
- Hindi (hi)
- Kannada (kn)
- Kashmiri (ks)
- Konkani (kok)
- Maithili (mai)
- Malayalam (ml)
- Manipuri (mni)
- Marathi (mr)
- Nepali (ne)
- Odia (or)
- Punjabi (pa)
- Sanskrit (sa)
- Santali (sat)
- Sindhi (sd)
- Tamil (ta)
- Telugu (te)
- Urdu (ur)

## Features

- **Quantized Models**: INT8 quantization for efficient CPU inference
- **Multiple Languages**: Support for 22 Indic languages
- **Two Modes**: CTC and RNN-T decoding
- **Auto Download**: Models download automatically on first use
- **ONNX Runtime**: Optimized inference with ONNX

## Audio Requirements

- Format: WAV, MP3, FLAC, etc.
- Sample Rate: Auto-resampled to 16kHz
- Channels: Mono (auto-converted)
jupyter-switch 0.1.3
Convert between Markdown (.md) and Jupyter Notebook (.ipynb) formats
Requires Python: >=3.10

# Jupyter Switch

[![PyPI version](https://badge.fury.io/py/jupyter-switch.svg)](https://badge.fury.io/py/jupyter-switch)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A powerful, lightweight and intuitive tool to seamlessly convert between Markdown (.md) and Jupyter Notebook (.ipynb) formats.

## ✨ Features

- 🚀 **Automatic detection**: Automatically detects whether the input file is `.md` or `.ipynb` and converts accordingly
- 🔄 **Bidirectional conversion**: Convert from Markdown to Jupyter Notebook and vice versa
- 🛡️ **Backup protection**: Automatically creates backups when output files already exist
- 📋 **Preserves structure**: Maintains code blocks, markdown content, and cell structure
- 🔧 **MCP integration**: Works as an MCP server tool for AI assistants

## 🤖 MCP Tool Usage (Recommended for AI Assistants)

This package provides an MCP (Model Context Protocol) server that can be used with AI assistants like Claude.

### Installation as MCP Server

Add to your MCP settings configuration:

```json
{
  "mcpServers": {
    "jupyter-switch": {
      "command": "uvx",
      "args": ["--from", "jupyter-switch", "jupyter-switch-mcp"]
    }
  }
}
```

### Available Tool

- **`switch_file`**: Converts between `.md` and `.ipynb` formats automatically based on input file extension

### Recommended Workflow for .ipynb Editing

1. **Convert to Markdown**: Use the `switch_file` tool to convert `.ipynb` → `.md` for easier editing
2. **Edit**: Modify the `.md` file with your preferred editor
3. **Convert back**: Use the tool again to convert `.md` → `.ipynb` when done

### Example

```
"Translate this notebook to French, using jupyter-switch"
```





## 📦 CLI Usage

### Installation

Install the package using pip:

```bash
pip install jupyter-switch
```

### Usage

The main command is `jupyter-switch`:

```bash
# Convert a Markdown file to Jupyter Notebook
jupyter-switch example.md

# Convert a Jupyter Notebook to Markdown
jupyter-switch example.ipynb
```

### Install with uv (Recommended) ⚡️

[`uvx`](https://docs.astral.sh/uv/concepts/tools/) will automatically install the package and run the command.

```bash
# Convert README.md to README.ipynb
uvx jupyter-switch README.md

# Convert notebook.ipynb to notebook.md
uvx jupyter-switch notebook.ipynb
```

The tool will automatically:
- Detect the input file format
- Generate the appropriate output filename
- Create a backup if the output file already exists
- Convert the content while preserving structure

### Help 🆘

```bash
jupyter-switch --help
jupyter-switch --version
```

## Conversion Details 🔄

### Markdown to Jupyter Notebook
- Python code blocks (```python...```) become code cells
- All other content becomes markdown cells
- Adds appropriate notebook metadata and structure

### Jupyter Notebook to Markdown  
- Code cells become Python code blocks
- Markdown cells are preserved as-is
- Cell outputs are ignored during conversion

## Requirements 🐍

- Python >= 3.10

## License 📄

MIT License
definit-db 0.6.7
DefinIT database
Requires Python: >=3.12

# DefinIT database

## Run tests
```
cd src
python -m pytest
```

## Generate Markdown database
```
cd src
python -m definit_db.generate_db_md
```

## Build and upload the package
Upgrade version in `pyproject.toml` and `src/definit_db/__init__.py` before running the commands below.

```
(just once) python -m pip install requirements-dev.txt

(optional cleanup) rm -rf dist/ build/ src/*.egg-info/

python -m build

python -m twine upload dist/*
```