Best AI Models for Hinglish Speech-to-Text in 2026

Hinglish is how millions of Indians naturally speak. A sentence can move between Hindi and English without the speaker thinking about it:

Mujhe woh report send karni hai before the meeting starts.

Or:

Aap payment confirm karke mujhe bata dena, okay?

For a speech-to-text model, this creates a very different challenge from recognizing Hindi or English on their own.

The model needs to identify the language being spoken, recognize words from both vocabularies, understand the transition between them, and preserve that mixed-language structure in the transcript.

This is known as code-switching.

So which AI model is best for Hinglish speech-to-text?

We compare OpenAI Whisper, AI4Bharat IndicWhisper, and Shunya’s speech models, looking at their approach to Indian languages, code-switching, real-time transcription, and production use cases.

What Is Hinglish Speech-to-Text?

Hinglish speech-to-text converts spoken Hindi-English conversations into text while preserving the way the languages are naturally mixed.

For example:

Audio:

Mujhe apna credit card ka payment check karna hai.

A useful Hinglish transcription might be:

मुझे अपना credit card का payment check करना है।

The goal isn’t simply to recognize Hindi and English separately. It is to understand where the speaker switches languages and preserve those switches.

This matters particularly for Indian customer conversations, voice agents, contact centres, and multilingual applications.

Why Is Hinglish Difficult for AI?

Most ASR models are trained to recognize languages individually. A model can perform well on Hindi and English separately without being equally good at Hindi-English code-switching.

Consider:

Mera account freeze ho gaya hai.

The system needs to understand that freeze is an English word being naturally used inside a Hindi sentence.

There are several challenges:

• Language identification: The model has to detect language changes within an utterance.
• Vocabulary: Hindi and English words can appear next to each other.
• Pronunciation: English words may be pronounced with an Indian accent.
• Context: The model needs to understand the meaning of the mixed sentence.
• Output format: The transcript should preserve the Hindi-English structure instead of forcing everything into one language.

This is why code-switching requires more than simply supporting two languages.

Whisper vs IndicWhisper vs Shunya Labs for Hinglish

OpenAI Whisper

Whisper is a general-purpose multilingual speech recognition model. Its strengths include broad language coverage, open-source availability, and a large developer ecosystem.

It can transcribe Indian and multilingual speech, but it was not specifically designed around native Indian code-switching.

AI4Bharat IndicWhisper

IndicWhisper takes a more specialized approach by focusing on Indian languages.

This specialization gives it an advantage for Indic speech recognition. IndicWhisper has achieved an average 13.8% WER.

However, strong Hindi performance does not automatically mean strong Hinglish performance. A model needs to handle the interaction between Hindi and English, not simply recognize both languages individually.

Shunya Zero STT Codeswitch

Shunya takes this specialization further with Zero STT Codeswitch, designed specifically for mixed-language speech.

The model supports Hinglish and other code-switched patterns and is designed to preserve English words in Latin script within Indic-language transcripts.

For example:

मुझे अपना EMI details check करना है।

This is different from asking a general Hindi or multilingual model to figure out the language switch after transcription.

Feature Comparison

Feature	Whisper	IndicWhisper	Shunya Zero STT Codeswitch
General multilingual ASR	Yes	Indic-focused	Yes
Indian language focus	General	Yes	Yes
Hinglish focus	General multilingual	Indic-focused	Purpose-built for code-switching
Hindi + English in one utterance	Yes	Yes	Yes, specifically optimized
Mixed-language output	Possible	Possible	Yes
English words preserved in Latin script	Not a primary focus	Not a primary focus	Yes
Real-time applications	Possible	Depends on implementation	Designed for real-time use
Best suited for	General multilingual ASR	Indian-language ASR	Hinglish and mixed-language speech

The key distinction is specialization. Whisper is a general multilingual model. IndicWhisper is optimized for Indian languages. Shunya’s Codeswitch model is specifically designed for conversations where languages are mixed within the same utterance.

What Do the ASR Benchmarks Tell Us?

Shunya’s benchmark results provide useful context for comparing broader speech recognition performance.

• 3.10% composite WER across eight OpenASR benchmarks for the Universal tier
• 216+ languages for the Universal tier
• 55 Indian languages for the Indian tier
• 11.9% Hindi WER
• Approximately 200 ms streaming partials

Shunya’s Indian ASR has achieved an average 11.9% WER, compared with 13.8% for IndicWhisper.

However, for Hinglish, the stronger differentiator is therefore model design and dedicated code-switching capability, rather than claiming a specific WER advantage that the available benchmark data doesn’t establish.

Why Code-Switching Matters for Voice Agents

Hinglish isn’t just a transcription problem. Errors can propagate into the rest of a voice AI system.

Consider a banking call:

Mera credit card payment kal due hai, mujhe two days ka extension chahiye.

If the ASR system incorrectly transcribes two days, the voice agent could misunderstand the customer’s request.

The same applies to:

• Loan applications
• Insurance claims
• Customer support
• Telecom queries
• E-commerce orders
• Healthcare conversations
• Appointment booking

For these applications, the speech recognition layer needs to preserve the information that the user actually said.

That’s why code-switching becomes particularly important when speech-to-text is feeding an LLM or voice agent.

Which AI Model Is Best for Hinglish Speech-to-Text?

There isn’t one model that is best for every ASR application.

Whisper remains a strong choice for general-purpose multilingual speech recognition, particularly when open-source flexibility is important.

IndicWhisper is a strong option for applications primarily focused on Indian languages and open-source Indic ASR.

But if your specific requirement is natural Hindi-English code-switching, the evaluation criteria change.

Shunya Zero STT Codeswitch is specifically designed for this use case.

Its key difference isn’t simply that it supports Hindi and English. It is designed to recognize both languages within the same utterance and generate mixed-language transcripts while preserving the language structure.

For enterprises building Hinglish voice agents, multilingual contact centres, customer support systems, and other Indian speech applications, that specialization can be more important than a generic multilingual language count.

Frequently Asked Questions

What is Hinglish speech-to-text?

Hinglish speech-to-text converts Hindi-English code-switched speech into text while preserving the mixed-language structure of the conversation.

Which AI model is best for Hinglish speech-to-text?

The answer depends on the use case. Whisper is a strong general-purpose multilingual model, while IndicWhisper is focused on Indian languages. Shunya’s Zero STT Codeswitch is specifically designed for mixed Hindi-English speech.

Can Whisper understand Hinglish?

Yes, Whisper can transcribe multilingual and Indian speech. However, it is a general multilingual model rather than a model specifically optimized for native Hinglish code-switching.

What is code-switching in speech recognition?

Code-switching occurs when a speaker switches between two or more languages during a conversation or within the same sentence. Hinglish is a common example.

Does Shunya support Hinglish?

Yes. Shunya provides a dedicated Zero STT Codeswitch model designed for Hinglish and other mixed-language speech patterns.