Shunya Labs Hinglish ASR Model

We wanted to make ASR that could intuitively capture how conversational Hindi is actually spoken. On average, every 2 words out of 10 spoken in conversational Hindi are in English. Traditional ARS models are trained to handle one language at a time, which makes them too slow and inaccurate when transcribing multilingual speech.

And so we innovated. We trained Zero STT Codeswitch to natively process Hinglish speech and generate mixed-script tokens.

This is a model worthy of how India actually speaks, because it can capture the way people naturally switch between Hindi and English mid-conversation.

And now, we're making the lighter version of Zero STT Codeswitch open source for the community!

For a faster version of Zero STT Codeswitch, visit shunyalabs.ai.

Model Details

Base Model: OpenAI Whisper Medium

Post-trained by: Shunya Labs

Language: Hinglish (Hindi-English code-switching)

Why This Model?

Standard ASR models treat Hindi and English as separate languages, forcing transcription into one or the other. This creates errors when speakers naturally switch between languages mid-sentence—which is how millions of people actually talk. This model was trained specifically on code-switched speech, so it:

• Transcribes Hindi and English tokens as they naturally occur
• Handles mid-sentence language switches accurately
• Produces faster inference by avoiding language detection overhead
• Delivers higher accuracy on real-world Hinglish speech

Demo

• Try the model at: https://www.shunyalabs.ai/zero-code-switch

Transcription Comparison

Use Cases

• Transcription of Hinglish conversations, podcasts, and videos
• Customer support and conversational agents serving Indian users
• Meeting transcription for Indian workplaces
• Content creation and subtitling

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

transcriber = pipeline("automatic-speech-recognition", model="shunya-labs/hinglish-whisper-medium")
result = transcriber("audio.mp3")
print(result["text"])

Training Details

Openai/whisper-medium post-trained on Google Vaani as well as proprietary datasets.

For a faster version of Zero STT Codeswitch, vistit shunyalabs.ai