Why Shunya beats Amazon Transcribe for production speech to text

Amazon Transcribe is one of the most established cloud speech-to-text services. It supports streaming and batch transcription, speaker diarization, automatic language identification, custom vocabularies, custom language models, channel identification, Call Analytics, and medical transcription.

For organizations already deep in AWS, it is an obvious option.

But production speech recognition is not just about having a transcription endpoint.

The harder problem is making speech work when the audio is messy, the vocabulary is specialized, people switch languages mid-sentence, hundreds of conversations need to run simultaneously, and the transcript has to become an actionable business signal.

That is where Shunya takes a different approach.

Shunya Zero STT is built around 216+ languages, 3.10% published composite WER across eight OpenASR benchmarks, sub-500ms first-token latency, native code-switching, specialized STT models, and speech intelligence capabilities such as intent, sentiment, emotion, and speaker labels.

The more useful question, then, is not whether Amazon Transcribe can turn audio into text.

It can.

The question is how well each platform handles the speech your production system actually receives.

Shunya and Amazon Transcribe at a glance

Capability	Shunya Zero STT	Amazon Transcribe
Languages	216+	Broad multilingual support, language availability varies by feature
Published WER	3.10% composite across 8 OpenASR benchmarks	Model- and language-dependent
Streaming	Sub-500ms first token	Yes
Batch	Yes	Yes
Throughput	146× real-time on published benchmark	Usage and architecture dependent
Code-switching	Dedicated native model	Multi-language identification available for supported languages
Domain vocabulary	Keyterm normalization + specialized models	Custom vocabulary
Custom model	Custom-trained STT models on Enterprise	Custom Language Models
Diarization	Yes	Yes
Intent	Yes	Through Call Analytics workflows
Sentiment	Yes	Call Analytics
Emotion	Yes	Not a standard Transcribe feature
Healthcare	Zero STT Med	Transcribe Medical
Deployment	Cloud, private, on-premise, edge-oriented options	AWS cloud service
Pricing	From $0.0039/min for Zero STT	Pay-as-you-go, tiered by region and usage

Amazon’s pricing and feature availability can vary by AWS Region, transcription mode, language, and enabled features, so production comparisons should use the exact AWS configuration being evaluated.

AWS makes transcription easy. Production speech is still hard.

One reason Amazon Transcribe is attractive is that AWS has already solved much of the infrastructure problem.

You can send audio to the service, receive a transcript, and combine it with the rest of your AWS stack.

Amazon Transcribe supports batch and real-time transcription, automatic language identification, speaker changes, channel identification, and domain-specific features.

But the difficult part starts when you move beyond the API demo.

A customer might be speaking through a noisy phone connection.

An employee may be using a cheap headset.

Two people may talk over one another.

The speaker may switch from Hindi to English halfway through a sentence.

A product name might not exist in the model’s standard vocabulary.

This is why Shunya’s Zero STT positioning starts with “the audio you actually receive” rather than clean benchmark audio. Its production examples explicitly include noise, accents, phone-line audio, multiple speakers, and code-switching.

That distinction matters because a transcript can look good in a demo and still fail on the calls that matter to your business.

Accuracy is not just whether the transcript is readable

Amazon Transcribe supports a wide set of transcription features, but its documentation makes an important point: domain-specific terms, brand names, acronyms, and jargon may require custom vocabulary or custom language modelsto improve accuracy.

Shunya approaches the same problem with both customization and model specialization.

Its OpenASR result is currently 3.10% composite WER across eight benchmarks, covering clean speech, meetings, earnings calls, podcasts, parliamentary speech, and other datasets.

Its Indian speech model also publishes an average 11.9% Hindi WER across seven datasets, with benchmarks covering datasets such as Kathbath, Common Voice, FLEURS, IndicTTS, RESPIN, and Gramvaani.

But even these numbers are not enough to choose an ASR platform.

For production, look at critical-word accuracy as well.

A mistake in a filler word is usually harmless.

A mistake in a medication name, transaction amount, product code, customer name, or account number can change the outcome completely.

That is why a serious evaluation should test:

WER

Critical terminology

Numbers and currency

Proper nouns

Mixed-language speech

Speaker attribution

Noisy audio

Domain-specific phrases

Custom vocabulary versus specialized speech models

Amazon Transcribe has a mature customization stack.

Its custom vocabulary feature lets you provide terms such as product names, acronyms, proper nouns, and technical vocabulary.

AWS also supports Custom Language Models, which go beyond simply telling the system which words exist. They learn domain context from text data, allowing the model to better understand how specialized words are used in relation to one another.

That is useful.

But there is an architectural difference worth understanding.

A vocabulary list says:

“This word is important.”

A language model says:

“This is how these words are used in this domain.”

A specialized speech model goes one step further:

“This is the kind of speech this system is optimized to recognize.”

Shunya’s Zero STT model family includes separate models for universal speech, Indic speech, code-switching, and healthcare.

That means a healthcare workflow can use Zero STT Med, while a Hinglish contact-center workflow can use Zero STT Codeswitch, rather than trying to solve every environment with exactly the same recognition path.

Code-switching is a real speech problem

Consider this customer sentence:

“Mera card block ho gaya, can you help me activate it?”

This is not two separate requests.

It is one natural utterance.

Amazon Transcribe supports language identification, including multi-language identification for supported language combinations. Its streaming documentation also describes identifying multiple languages in a stream when multi-language identification is enabled.

But language identification and code-switching are not the same thing.

Language identification asks: what languages are present?

Code-switching recognition asks: can the model accurately transcribe the way those languages interact inside one conversation?

Shunya has a dedicated Zero STT Codeswitch model specifically for this kind of speech. Its public examples include Hinglish and other mixed-language use cases.

For an Indian contact center, this distinction can be much more important than the headline language count.

Indian speech is where generic multilingual support gets tested

Amazon Transcribe supports a broad range of languages, including several Indian languages. Its current language documentation includes Hindi, Bengali, Gujarati, Kannada, Malayalam, Marathi, Tamil, Telugu, Punjabi, and others, with feature support varying by language.

But India’s speech environment is more complicated than a language dropdown.

People switch between Hindi and English.

Regional accents change pronunciation.

Customers use informal forms of speech.

Names and product terms get embedded inside regional-language sentences.

And many enterprise calls happen over noisy phone connections.

Shunya’s Zero STT Indic is specifically built around this environment, with support for 55+ Indian languages and regional dialects, alongside native handling of mixed-language speech.

Shunya’s published Hindi benchmark also shows 11.9% average WER across the evaluated datasets.

The point is not that an Indian-language application automatically needs Shunya.

The point is that when Indian speech is central to the product, the evaluation needs to test the actual regional and code-switched audio users produce.

Real-time performance changes the engineering problem

Amazon Transcribe supports real-time streaming transcription through secure streaming connections.

That makes it suitable for applications such as contact centers, assistants, and live transcription.

But real-time speech systems have several different performance constraints:

First-token latency

How quickly does text start appearing?

Finalization latency

How quickly does the system settle on the final transcript?

Throughput

How many times faster than real time can audio be processed?

Concurrency

How many conversations can run simultaneously?

Shunya currently publishes sub-500ms first-token latency and 146× real-time throughput on its speech-to-text platform. Its Zero STT page also publishes 240+ concurrent streams per GPU.

For a large contact center or voice-AI platform, these numbers can matter just as much as WER.

A system that is accurate for one call but becomes expensive or difficult to scale across thousands of simultaneous streams creates a different engineering problem.

Amazon Transcribe is deeply connected to AWS. That’s both a strength and a tradeoff.

This is probably Amazon Transcribe’s biggest advantage.

If your application already runs on AWS, you can connect transcription into the wider AWS ecosystem and use services around it for storage, analytics, security, automation, and application workflows.

Amazon also provides specialized services such as Transcribe Call Analytics and Transcribe Medical, rather than treating transcription as a completely isolated service.

For AWS-first organizations, that integration is valuable.

But it also means your speech architecture becomes increasingly tied to the AWS service model and configuration ecosystem.

Shunya takes a more speech-focused approach.

Its platform is centered on speech recognition, speech intelligence, TTS, voice agents, SLMs, edge speech understanding, and knowledge-grounded voice AI, rather than using transcription as one component inside a much larger general-purpose cloud stack.

That can make Shunya a better fit when speech is a core product capability rather than simply one AWS service in a broader cloud architecture.

What happens after transcription?

A transcript is rarely the final business output.

A contact center might need to know:

Why did the customer call?

Was the customer frustrated?

What action was requested?

Which agent handled the interaction?

What should happen next?

Shunya’s Zero STT workflow is designed to move directly from speech into transcript, intent, sentiment, emotion, and speaker labels.

That means the speech recognition layer can become the first stage of a broader intelligence pipeline.

Amazon provides related capabilities through Transcribe Call Analytics, which can generate insights from customer-agent conversations. AWS also provides separate services and tools throughout its ecosystem for downstream analysis.

Again, neither approach is inherently better.

The distinction is architectural.

Amazon gives you a powerful cloud service ecosystem around transcription.

Shunya puts speech and speech intelligence at the center of the platform.

Healthcare shows the difference clearly

Healthcare is a domain where generic transcription often needs additional specialization.

Amazon provides Transcribe Medical, including support for medical specialties, medical dictation, conversational medical speech, custom vocabulary, and speaker diarization.

So Amazon is not treating healthcare as a generic transcription workload.

Shunya takes a similar specialized-model approach through Zero STT Med, which is positioned for healthcare transcription and automatic medical-terminology correction.

For healthcare buyers, the important question is therefore not:

“Does the platform have medical transcription?”

Both do.

The better question is:

“How accurately does it recognize the medical vocabulary, accents, abbreviations, and conversation patterns found in our actual clinical environment?”

That is where a real sample of your audio matters more than a product feature list.

What does Amazon Transcribe cost compared with Shunya?

Amazon Transcribe uses pay-as-you-go pricing based on seconds of audio, with tiered rates that vary by AWS Region. AWS states that standard pricing includes features such as custom vocabularies, vocabulary filtering, speaker diarization, and language identification, while additional charges apply for capabilities such as automatic content redaction and custom language models.

AWS also currently offers a 60-minute monthly free tier for the first 12 months, subject to the stated AWS terms.

Shunya currently prices Zero STT at $0.0039/minute pay-as-you-go, with volume pricing of $0.0035/minute. Zero STT Indic is $0.0045/minute, while Zero STT Codeswitch and Zero STT Med are $0.0050/minute.

So the right comparison is not simply:

AWS price versus Shunya price

It is:

What does the complete production speech stack cost for your workload?

That can include:

Transcription

Customization

Additional intelligence services

Infrastructure

Model development

Engineering effort

Deployment constraints

Concurrency

and ongoing operations.

For high-volume speech applications, these factors can make the difference between a low API bill and a low total cost of ownership.

Where Amazon Transcribe is the better choice

There are situations where Amazon Transcribe is the obvious choice.

If your company already runs heavily on AWS, Transcribe can fit naturally into the architecture.

It is also compelling when you need:

Batch and streaming transcription

Automatic language identification

Custom vocabulary

Custom language models

Speaker diarization

Channel identification

Call Analytics

Medical transcription

all inside a mature cloud ecosystem.

For AWS-native teams that value deep cloud integration over speech specialization, Amazon Transcribe remains a strong production option.

When Shunya makes more sense

Shunya becomes particularly compelling when speech itself is the difficult engineering problem.

That includes:

Noisy production audio

Phone calls, cheap headsets, background noise, and inconsistent recording quality.

Indian-language applications

Hindi, Tamil, Telugu, Bengali, Marathi, and regional-language use cases where depth matters more than a generic language checkbox.

Code-switched conversations

Hinglish and other mixed-language interactions where people naturally switch languages mid-sentence.

Specialized domains

Healthcare and other workloads where terminology errors have real consequences.

High concurrency

Large-scale contact centers and voice systems where throughput and concurrent processing matter.

Speech intelligence

When the output needs to be more than a transcript and should include intent, sentiment, emotion, speaker information, or other structured signals.

Flexible deployment

Shunya’s enterprise offering includes self-hosted deployment options and higher-concurrency support, alongside its cloud offering.

Shunya vs Amazon Transcribe by use case

Use case	Better fit
AWS-native application	Amazon Transcribe
General batch transcription	Both
General English transcription	Both
AWS-integrated contact center	Amazon Transcribe
Indian-language contact center	Shunya
Hinglish and mixed-language calls	Shunya
High-concurrency voice systems	Shunya
Healthcare transcription	Both
Specialized enterprise vocabulary	Both
Real-time voice applications	Both
Transcript + speech intelligence	Shunya
Organizations wanting AWS-native architecture	Amazon Transcribe
Private/self-hosted speech deployments	Shunya

Don’t choose from the documentation. Test your own audio.

The most useful ASR benchmark is the one you run yourself.

Take 20 to 50 recordings from the environment your product actually operates in.

Include:

Clean audio

Phone-quality audio

Noisy calls

Multiple speakers

Accented speech

Code-switched speech

Domain-specific conversations

Important names and numbers

Then compare:

WER

Critical-word accuracy

Code-switch accuracy

Speaker attribution

First-token latency

Final transcript latency

Throughput

Concurrency

and, most importantly, downstream task accuracy.

For a contact center, measure whether the correct intent is identified.

For a voice agent, measure whether the agent takes the correct action.

For healthcare, measure whether critical terminology is recognized correctly.

For enterprise search, measure whether users can actually find the right information.

This matters because the model with the best headline benchmark may not be the model that produces the best business outcome on your audio.

The real decision: AWS service or speech platform?

Amazon Transcribe has a major advantage: it gives organizations a mature speech service inside one of the world’s largest cloud ecosystems.

That makes it an excellent choice for many AWS-native applications.

Shunya approaches the problem differently.

Its product is centered around production speech itself: difficult audio, multilingual and regional speech, specialized STT models, low-latency recognition, high concurrency, and intelligence directly around the transcript.

The difference becomes especially important when your application is not simply processing a few recordings.

When it is processing thousands of conversations, in multiple languages, with domain-specific terminology, in real time, the speech model becomes a critical part of the product.

At that point, choosing an ASR platform is no longer just a cloud-service decision.

It is a speech-engineering decision.

Final words

Amazon Transcribe is a mature and capable speech-to-text platform, particularly for organizations that already live inside AWS. It supports streaming, batch, language identification, diarization, custom vocabulary, custom language models, medical transcription, and Call Analytics.

But production speech has problems that a basic transcription API cannot solve by itself.

Noise.

Accents.

Crosstalk.

Domain terminology.

Code-switching.

Regional speech.

Concurrency.

Latency.

And the need to turn speech into something more useful than text.

Shunya’s approach is to make those problems part of the speech platform itself.

With 216+ languages, 3.10% published composite WER, sub-500ms first-token latency, 146× real-time throughput, 240+ concurrent streams per GPU, specialized STT models, and built-in speech intelligence, Shunya is designed for enterprises where real-world speech complexity is the problem to solve.

The best way to decide is simple:

Take your hardest 20 recordings and run them through both systems.

Not your cleanest recordings.

Not someone else’s benchmark.

Your actual production speech.

That’s where the difference between a capable cloud transcription service and a speech platform built around production complexity becomes clear.

Frequently asked questions

Is Shunya more accurate than Amazon Transcribe?

There is no universal answer because accuracy varies by language, dataset, audio quality, domain, and configuration. Shunya currently publishes a 3.10% composite WER across eight OpenASR benchmarks and a 11.9% average Hindi WER across seven evaluated datasets.

The fairest comparison is to test both on representative production audio.

Does Amazon Transcribe support multiple languages?

Yes. Amazon Transcribe supports a broad set of languages, and its language identification features can detect languages in supported batch and streaming scenarios. Feature availability varies by language.

Does Amazon Transcribe support code-switching?

Amazon Transcribe supports multi-language identification for supported language combinations. The precise behavior and feature availability depend on the languages and configuration used.

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

Does Amazon Transcribe support custom vocabulary?

Yes. Amazon Transcribe provides custom vocabularies for brand names, acronyms, proper nouns, and other domain-specific terms.

Does Amazon Transcribe support custom language models?

Yes. AWS provides Custom Language Models designed to improve recognition of domain-specific speech by learning the context and relationships between specialized terms.

Does Shunya support healthcare speech?

Yes. Shunya provides Zero STT Med, a specialized model for healthcare transcription and medical terminology.

Amazon also provides Amazon Transcribe Medical with medical-specific capabilities.

Which is cheaper, Shunya or Amazon Transcribe?

Pricing depends on the AWS Region, transcription type, volume, and enabled features. AWS uses tiered pay-as-you-go pricing, while Shunya Zero STT currently starts at $0.0039/minute, with lower volume pricing available.

Can Shunya be deployed outside the public cloud?

Yes. Shunya’s enterprise offering includes self-hosted deployment options, in addition to its cloud offering.

Which is better for an AWS-native company?

Amazon Transcribe may be the more natural fit when deep AWS integration is a priority.

Shunya can be the stronger choice when the application depends heavily on speech quality, Indian languages, code-switching, specialized models, concurrency, or speech intelligence.

How should I compare Shunya and Amazon Transcribe?

Use your own recordings. Compare WER, critical terminology, code-switching, diarization, latency, concurrency, and the business task that depends on the transcript.