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Sonorus AI (sonorus-ai)

PyPI version License: MIT Python 3.8+

Sonorus AI is an enhanced speech recognition library built on top of OpenAI's Whisper, optimized for Indian multilingual contexts:

  • 🇮🇳 Native Hinglish Transcription: Transcribes Hindi speech directly into clean, phonetic Roman English script (--language Hinglish).
  • ✍️ Phonetic Corrections: Built-in dictionary post-processing to fix common transliteration slips (e.g., bina bikhar, mohabbat, guroor).
  • 📁 Organized Outputs: Automatically saves transcriptions into sorted folders (outputs/Hinglish/, outputs/Hindi/, outputs/English/) in .txt format without cluttering your project root.

⚡ Quick Start

1. Installation

Install via PyPI:

pip install sonorus-ai

Or install the latest version directly from GitHub:

pip install git+https://github.com/Krishnanshu3708/sonorus-ai.git

Prerequisite: Make sure ffmpeg is installed on your system:

  • Windows (Chocolatey): choco install ffmpeg
  • Windows (Scoop): scoop install ffmpeg
  • Mac (Homebrew): brew install ffmpeg
  • Ubuntu/Debian: sudo apt update && sudo apt install ffmpeg

🚀 Usage

Option A: In Python

import sonorus

# 1. Load the model (turbo, tiny, base, small, medium, large)
model = sonorus.load_model("turbo")

# 2. Transcribe Hindi speech directly into Hinglish (Roman English script)
result = model.transcribe("hindi_meeting.mp3", language="Hinglish")
print(result["text"])

# Standard English / Multilingual transcription
result_en = model.transcribe("english_podcast.mp3")
print(result_en["text"])

Option B: Command Line (CLI)

Transcribe directly from your terminal using sonorus:

# Transcribe Hinglish with automated phonetic correction & clean folder organization
sonorus "audio.mp4" --model turbo --language Hinglish

# Transcribe standard English/Multilingual
sonorus "lecture.mp3" --model turbo

# Transcribe multiple files at once
sonorus audio1.flac audio2.mp3 --model turbo

To see all available CLI options:

sonorus --help

📊 Available Models

Size Parameters Multilingual Model Required VRAM Relative Speed Recommended For
tiny 39 M tiny ~1 GB ~10x Fast testing / CPU
base 74 M base ~1 GB ~7x Low-spec devices
small 244 M small ~2 GB ~4x Balanced accuracy & speed
medium 769 M medium ~5 GB ~2x High accuracy & translation
large 1550 M large ~10 GB 1x Best quality
turbo 809 M turbo ~6 GB ~8x Recommended for Hinglish & English

🛠️ Local Development

If you want to contribute or build from source:

git clone https://github.com/Krishnanshu3708/sonorus-ai.git
cd sonorus-ai
pip install -e .

Run tests:

pytest

📜 License

This project is licensed under the MIT License.

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