Shunya vs Google Speech to Text: Which ASR Platform Is Better for Production?

Google Speech to Text is one of the most established cloud speech recognition platforms available today. It combines multilingual transcription, real-time streaming, speaker diarization and model adaptation with the broader Google Cloud ecosystem.

Its latest generation, Chirp 3, supports streaming, short-form and batch transcription, automatic language detection, speaker diarization and speech adaptation.

Shunya takes a different approach.

Shunya Zero STT is built for production speech workloads with 216+ languages, 55+ Indian languages, sub-500ms first-token positioning, speech intelligence and specialized speech models.

So the question isn’t simply which speech to text API has more features.

It’s:

Which ASR platform fits your languages, your application and the way you need to use speech in production?

Shunya vs Google Speech-to-Text: at a glance

Google Speech-to-Text and Shunya are both managed speech recognition platforms, but their strengths are different.

Aspect	Shunya Zero STT	Google Speech-to-Text
Platform	Enterprise speech-to-text platform	Google Cloud speech service
Current model family	Zero STT Universal, Indic, Codeswitch, Med	Chirp 3 and other Google Speech models
Deployment	Cloud and enterprise private deployment options	Google Cloud
Pricing	From $0.0039/min	Usage-based
Language coverage	216+	85+ languages and variants with Chirp 3
Indian languages	55+	Multiple Indian languages
Real-time	Sub-500ms first-token positioning	Streaming recognition
Speaker diarization	Supported	Supported
Language detection	Supported	Supported
Custom vocabulary	Supported	Model adaptation
Code-switching	Dedicated model	Model/language dependent
Speech intelligence	Intent, sentiment, emotion and more	Broader Google AI ecosystem
Medical ASR	Zero STT Med	General/custom recognition
Best fit	Enterprise speech, Indian languages, voice applications	Google Cloud-native applications

Google’s current documentation describes synchronous, asynchronous and streaming recognition, while Chirp 3 supports streaming, short-form and batch recognition.

Google Speech to Text is a strong cloud speech platform

The biggest advantage of Google Speech to Text is the ecosystem around it.

If your application already runs on Google Cloud, Speech to Text can fit naturally into an existing architecture.

You can connect speech with:

• Google Cloud Storage
• BigQuery
• Vertex AI
• Google Cloud IAM
• Other Google AI services

Chirp 3 is designed as Google’s latest generation of multilingual ASR models, with support for real-time and non-real-time transcription across 85+ languages and variants.

Shunya’s focus is narrower and on whole voice stack.

It is focused on the speech layer itself, with Zero STT spanning general multilingual recognition, Indian-language speech, code-switching and medical transcription.

That gives the two platforms different strengths.

Google is deeply integrated into a cloud ecosystem.

Shunya is more focused on speech as a production capability.

Which is more accurate?

There is no single Word Error Rate (WER) number that tells you which ASR platform is better for every workload.

Accuracy depends on:

• Language
• Accent
• Audio quality
• Domain
• Background noise
• Vocabulary
• Speaker characteristics

Shunya currently publishes a 3.10% composite WER in English across eight public benchmarks for Zero STT.

Benchmark	Shunya Zero STT
LibriSpeech Clean	0.71%
SPGISpeech	1.10%
TED-LIUM	1.43%
LibriSpeech Other	2.17%
AMI	4.19%
VoxPopuli	4.34%
GigaSpeech	4.99%
Earnings22	5.83%
Composite	3.10%

Google does not publish one equivalent universal WER figure for Chirp 3 across all Speech to Text workloads. Instead, Google publishes model- and language-specific evaluations and support details.

So the best test remains the same:

Use your own audio.

If you’re building for healthcare, test medical terms.

If you’re building for contact centres, test telephone audio.

If you’re building for India, test Indian languages and code-switched speech.

The feature gap is smaller than you might expect

Unlike a simple ASR model, Google already provides many production speech features.

That makes this comparison much more nuanced.

Feature	Shunya	Google Speech-to-Text
Batch transcription	Built in	Built in
Real-time streaming	Built in	Built in
Speaker diarization	Built in	Supported
Language detection	Supported	Supported
Automatic punctuation	Supported	Supported
Word timestamps	Supported	Supported
Confidence scores	Supported	Supported
Custom vocabulary	Supported	Model adaptation
Code-switching	Dedicated model	Model/language dependent
Indian-language specialization	Dedicated Indic model	Language/model dependent
Intent	Supported	Separate Google AI services
Sentiment	Supported	Separate Google AI services
Emotion	Supported	Separate Google AI services
Medical speech	Zero STT Med	General/custom recognition
PII workflows	Supported	Google Cloud ecosystem
Translation	Supported / ecosystem	Google Cloud ecosystem

Google’s current Chirp 3 documentation explicitly supports automatic punctuation, automatic capitalization, speech adaptation, automatic language-agnostic transcription and speaker diarization for supported models and methods.

The important difference is therefore not:

“Does Google have production features?”

It does.

The question is:

“How much of the speech intelligence and specialization do you want to get from the speech layer itself?”

Real-time: both platforms can stream

Real-time transcription is not a meaningful differentiator by itself anymore.

Google Speech-to-Text supports streaming recognition, and Chirp 3 can process streaming audio through the V2 API.

Shunya also provides streaming recognition and publicly positions Zero STT around sub-500ms first-token latency.

For production applications, the questions become:

How quickly does the first useful transcript arrive?

How accurate are partial results?

How does the system perform with noisy audio?

How many concurrent conversations can it support?

Shunya publicly reports 240+ concurrent streams per GPU.

For a contact centre or voice agent, those details can matter more than simply checking whether an API supports streaming.

Indian languages are where specialization matters

Google Speech-to-Text supports multiple Indian languages, and Chirp 3 includes Indian locales among its supported languages.

Shunya takes a more specialized approach.

Zero STT Indic supports 55+ Indian languages, while Zero STT Codeswitch is designed specifically for mixed-language speech.

Consider:

“Mera account block ho gaya, can you help me reactivate it?”

This is perfectly normal speech for many Indian users.

The challenge isn’t simply recognizing Hindi and English.

It’s recognizing them together.

That’s important for:

• Contact centres
• Banking
• Telecom
• Customer support
• Voice agents
• Regional applications

Language coverage is only useful when the system performs on the way people actually speak.

Code-switching is more than language detection

A customer may switch languages in the middle of a sentence.

That changes the ASR problem.

Shunya provides a dedicated Zero STT Codeswitch model for mixed Indic and English speech such as Hinglish and Tanglish.

This becomes especially important when the transcript feeds a downstream system.

For example:

speech → transcript → intent → routing

A transcription error can become an intent error.

That can become a routing error.

And eventually, a customer experience problem.

For applications where code-switching is common, a dedicated speech model can therefore be more useful than treating mixed-language speech as another generic multilingual workload.

Model adaptation: where Google is strong

Google’s model adaptation is one of its strongest features for domain-specific transcription.

It lets developers provide words and phrases that should be recognized more often, which is useful for:

• Product names
• Company names
• Medical terminology
• Financial vocabulary
• Proper nouns
• Industry-specific terms

Google’s current Chirp 3 documentation supports adaptation dictionaries of up to 1,000 phrases.

Shunya approaches the same problem through custom terminology, keyword normalization and specialized model variants such as Zero STT Med and Zero STT Codeswitch.

For many general applications, Google’s adaptation may be exactly what you need.

For specialized speech workloads, a dedicated model can be a different kind of advantage.

Speaker diarization: both support it

Google Speech to Text supports speaker diarization for supported models and languages. Chirp 3 currently documents diarization support for specific recognition methods and language sets.

Shunya also provides speaker diarization as part of Zero STT.

So this isn’t an area where one platform simply has the feature and the other doesn’t.

The better test is how each handles:

• Multiple speakers
• Overlapping speech
• Telephone audio
• Background noise
• Speaker changes

For customer calls and meetings, real-world diarization accuracy matters more than the checkbox.

Pricing: Google Speech to Text vs Shunya

Both platforms use usage-based pricing.

Shunya currently starts Zero STT at $0.0039/min for standard batch transcription. Its current pricing also lists Zero STT Indic at $0.0045/min and Zero STT Codeswitch and Zero STT Med at $0.0050/min for batch usage.

Google’s pricing varies based on the recognition model and usage configuration.

Google also states that pricing can depend on the amount of audio processed and the number of channels, with each audio channel billed separately.

So don’t compare only the headline rate.

Compare:

model + audio volume + channels + features + workload

For a real production deployment, calculate the cost on your actual monthly audio volume.

Google Cloud can be the better choice when…

Google Speech to Text can be the better fit when:

You’re already on Google Cloud

Your application, data and infrastructure already live in GCP.

You want a mature cloud ecosystem

Speech needs to connect with Google Cloud storage, analytics, identity and AI services.

You want broad managed speech capabilities

Chirp 3 provides multilingual recognition, streaming, diarization, language detection and model adaptation.

Model adaptation is important

Your application depends heavily on custom phrases and terminology.

Your workload fits Google’s supported languages and models

Google provides detailed model-specific language and feature availability.

When Shunya makes more sense

Shunya becomes more compelling when:

Indian speech is central to the product

You need dedicated Indian-language models across 55+ languages.

Code-switching is common

Your users naturally mix English and Indian languages.

Speech intelligence is part of the workflow

You need intent, sentiment, emotion and related information alongside transcription.

You need specialized models

Your use case involves medical or domain-specific speech.

You want a speech-focused stack

You need STT alongside TTS, voice agents and other speech components.

Deployment flexibility matters

You need enterprise deployment options beyond a standard cloud API.

Shunya vs Google Speech to Text by use case

Use case	Better fit	Why
GCP-native application	Google	Native Google Cloud ecosystem
General cloud transcription	Both	Both provide managed ASR
Real-time transcription	Both	Both support streaming
Indian-language application	Shunya	Dedicated Indic model
Hinglish application	Shunya	Dedicated code-switching model
Domain vocabulary	Both	Google adaptation; Shunya specialization
Speaker-labelled calls	Both	Both support diarization
Speech intelligence	Shunya	Intent, sentiment, emotion and related capabilities
Google AI ecosystem	Google	Native integration
Medical speech	Shunya	Dedicated Zero STT Med
Enterprise voice stack	Shunya	STT + broader voice platform
Google Cloud-centric enterprise	Google	Cloud ecosystem and services

Can you use Google and Shunya together?

Yes.

A hybrid architecture can make sense when Google Cloud already forms part of your infrastructure while some speech workloads require different language or model capabilities.

For example:

Google Speech-to-Text

→ GCP-native workloads

Shunya

→ Indian-language applications
→ code-switching
→ specialized speech
→ speech intelligence
→ production voice workflows

The best architecture does not always require a single ASR provider for every workload.

How should you evaluate them?

The best way to compare Google Speech to Text and Shunya is to use your own audio.

Build a representative evaluation set containing:

Your languages

Your accents

Your domain vocabulary

Telephone recordings

Noisy audio

Multiple speakers

Code-switched conversations

Then measure:

Metric	What to test
WER	Overall transcription accuracy
Entity accuracy	Names, brands and products
Number accuracy	Amounts, dates and identifiers
Code-switch accuracy	Mixed-language speech
Diarization	Speaker attribution
First-result latency	Responsiveness
Final latency	End-to-end performance
Concurrent streams	Production capacity
Cost per hour	Actual usage economics
Downstream accuracy	Intent and classification performance

A model that performs well on a benchmark is a useful starting point.

Your production audio should make the final decision.

Final words

Google Speech-to-Text is a strong enterprise ASR platform, and Chirp 3 gives developers multilingual recognition, streaming, speaker diarization, language detection and model adaptation.

For teams already deeply invested in Google Cloud, that ecosystem can be a major advantage.

Shunya takes a more speech-specialized approach.

Zero STT combines 216+ language coverage, 55+ Indian languages, sub-500ms first-token positioning, speech intelligence and specialized speech models.

The difference isn’t simply about which API has more features.

It’s about what your speech workload actually needs.

Google gives you speech recognition as part of a broader cloud ecosystem.

Shunya gives you speech recognition as a specialized production layer.

For GCP-native applications, Google may be the natural choice.

For applications where Indian languages, code-switching, speech intelligence and specialized speech workloads are central, Shunya can be the better fit.

The right question isn’t:

“Which ASR API is better?”

It’s:

“Which speech platform performs best on the languages, audio and workflows my product actually depends on?”

Frequently asked questions

Is Shunya better than Google Speech-to-Text?

It depends on the application. Google is a strong choice for teams already using Google Cloud and looking for a mature managed speech platform. Shunya is particularly differentiated around Indian languages, code-switching, speech intelligence and specialized ASR models.

How many languages does Google Speech-to-Text support?

Google currently lists 85+ languages and variants for Chirp 3. Overall availability varies by model and feature.

How many languages does Shunya support?

Shunya currently markets Zero STT across 216+ languages, with 55+ Indian languages supported through its Indic speech offering.

Does Google Speech-to-Text support Indian languages?

Yes. Google’s current supported-language documentation includes multiple Indian languages, including Bengali, Hindi and other Indian locales across its speech models.

Does Google Speech-to-Text support speaker diarization?

Yes. Google supports speaker diarization for supported models, languages and recognition methods.

Does Shunya support speaker diarization?

Yes. Speaker diarization is part of Zero STT’s published capabilities.

Does Google Speech-to-Text support custom vocabulary?

Yes. Google provides model adaptation for domain-specific terms and proper nouns. Chirp 3 currently supports dictionaries of up to 1,000 phrases for adaptation.

Can Google Speech-to-Text handle real-time transcription?

Yes. Google supports streaming recognition, including streaming with Chirp 3.

Does Shunya support real-time transcription?

Yes. Shunya provides streaming recognition and publicly positions Zero STT around sub-500ms first-token latency.

Which is cheaper, Google Speech-to-Text or Shunya?

It depends on the workload. Shunya currently starts at $0.0039/min for Zero STT batch transcription. Google uses usage-based pricing that varies by model and configuration.

Does Google charge separately for multiple audio channels?

Google states that each audio channel is billed separately, so multi-channel workloads should be included when calculating total cost.

Does Shunya support code-switching?

Yes. Shunya offers a dedicated Zero STT Codeswitch model for mixed-language speech.

Which is better for Hinglish?

Shunya is the more specialized choice because Zero STT Codeswitch is designed specifically for mixed Indic and English speech.

Which is better for healthcare speech?

Shunya offers a dedicated Zero STT Med model for medical speech.

Is Google Speech-to-Text good for enterprise applications?

Yes. Google Speech-to-Text is designed for scalable cloud applications and integrates with the broader Google Cloud infrastructure.

Can Shunya be deployed privately?

Yes. Shunya provides enterprise deployment options for private environments.

Can I use Google Speech-to-Text and Shunya together?

Yes. Teams can use Google for Google Cloud-native workloads and Shunya for specialized speech workloads where its model or language capabilities are a better fit.