Why Shunya beats AssemblyAI for production speech-to-text

AssemblyAI has become one of the strongest speech-to-text platforms for teams that want transcription plus conversation intelligence without building the entire stack themselves.

Its latest Universal-3.5 Pro model adds native code-switching, contextual prompting, improved diarization, and a strong real-time offering.

But there is an important distinction between building a transcription feature and building speech recognition into a production system.

For many enterprise workloads, the hard part is not simply converting speech into text. It is handling noisy audio, accents, crosstalk, domain terminology, regional speech, code-switching, high concurrency, and the downstream intelligence that turns a transcript into something useful.

That is where Shunya takes a different approach.

Shunya’s Zero STT family is designed around production speech: 216+ languages, sub-500ms streaming latency, 3.10% published composite WER across eight OpenASR benchmarks, 240+ concurrent streams per GPU, native code-switching, specialized models, and an intelligence layer that can return diarization, intent, sentiment, emotion, summaries, and keyterm normalization alongside the transcript.

The question is therefore not simply which API has more transcription features.

It is which one fits the speech your production system actually receives.

Shunya and AssemblyAI at a glance

Capability	Shunya Zero STT	AssemblyAI
Languages	216+	99+ with Universal-2; 18 on Universal-3.5 Pro with native code-switching
Published WER	3.10% composite across 8 OpenASR benchmarks	Model- and benchmark-dependent
Streaming	Sub-500ms first token	Universal-3.5 Pro Realtime
Concurrent streams	240+ streams/GPU	Unlimited concurrency
Code-switching	Native model support	Native on Universal-3.5 Pro across 18 languages
Domain vocabulary	Keyterm normalization + specialized models	Contextual prompting + keyterms
Diarization	Built into intelligence layer	Available
Speaker identification	Available	Speaker labels/diarization
Intent	Available	Available through intelligence workflows
Sentiment	Available	Available
Emotion	Available	More workflow-dependent
Healthcare	Zero STT Med	Medical Mode
Deployment	Cloud, on-premise, edge-oriented options	Cloud API
Pricing	From $0.0039/min pay-as-you-go for Zero STT	$0.21/hr async Universal-3.5 Pro; $0.45/hr streaming Universal-3.5 Pro Realtime

Production audio doesn’t sound like benchmark audio

This is one of the biggest differences between an ASR demo and an actual production deployment.

Most benchmark recordings are relatively controlled. Production audio is not.

A customer might be speaking through a phone line from a noisy environment. An employee might be using a cheap headset. Two people might speak over each other. A caller might have an accent that is underrepresented in generic training data. A brand name might never appear in a standard English dictionary.

Shunya Zero STT is built around this “real speech” problem. The platform highlights phone audio, noise, accents, code-switching, multiple speakers, and domain-specific vocabulary as core production conditions rather than edge cases.

That matters because a transcription system can perform well on clean benchmark audio and still generate expensive errors in production.

Consider a customer support call:

“Mera card payment reverse nahi hua, can you check the transaction?”

The problem is not simply whether the ASR system understands Hindi or English independently. It needs to understand how those languages are used together in actual conversation.

Or consider a healthcare call where a medication name is spoken quickly with background noise.

A transcript that gets the general sentence right but changes the medication name is still a bad production transcript.

Accuracy is more than one WER number

Word error rate (WER) is still one of the most important ASR metrics.

But enterprise teams should not evaluate transcription accuracy using WER alone.

Shunya currently publishes a 3.10% composite WER in English across eight OpenASR benchmarks, which positions as #1 OpenASR result. The benchmark suite includes datasets such as LibriSpeech, SPGISpeech, TED-LIUM, AMI, VoxPopuli, GigaSpeech, and Earnings22.

Shunya also achieves a 11.9% Hindi WER across seven datasets, which is particularly relevant for Indian-language deployments.

But what matters even more for a production system is where the errors occur.

A useful enterprise evaluation should look at:

• Proper nouns
• Product and company names
• Medical terminology
• Acronyms
• Numbers and currency
• Addresses
• Mixed-language sentences
• Speaker changes
• Overlapping speech
• Noisy or compressed audio

AssemblyAI’s latest Universal-3.5 Pro release tackles a similar problem through contextual prompting. Its model can use a natural-language description of the audio to resolve difficult entities and terminology, while keyterms promptingcan bias transcription toward specific words and phrases.

That is a genuinely useful capability.

The difference is that Shunya goes beyond contextual steering with a model family designed around different speech environments and domains.

One model does not have to solve every speech problem

This is one of Shunya’s strongest architectural differentiators.

The Zero STT family includes different models for different workloads:

Zero STT for broad multilingual speech.

Zero STT Indic for Indian-language speech.

Zero STT Codeswitch for mixed-language conversations.

Zero STT Med for healthcare and clinical terminology.

The idea is straightforward: different speech problems require different optimization targets.

This matters when a company’s audio is highly specialized.

A healthcare organization does not necessarily want the same optimization target as a global contact center.

A multilingual customer-support platform does not necessarily want the same model as a medical transcription workflow.

And a system handling Hinglish conversations has different requirements from one processing clean English interviews.

AssemblyAI takes a somewhat different approach, with Universal-3.5 Pro positioned as its flagship model and Universal-2 providing broader 99+ language coverage. Universal-3.5 Pro currently supports native code-switching across 18 languages.

Both approaches are reasonable.

The question is whether your production workload benefits from one increasingly capable universal model or from purpose-built speech models mapped to the job.

Real-time isn’t just “streaming: yes”

Many ASR comparison tables reduce real-time transcription to a checkbox.

That isn’t enough.

For a voice agent, contact center, live assistant, or interactive application, latency affects the entire user experience.

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

Those numbers matter because production voice systems are not usually processing one audio file at a time.

They are processing many conversations simultaneously.

Imagine a contact center running hundreds of concurrent calls. A small improvement in throughput can have a much larger infrastructure impact than a small improvement in benchmark WER.

AssemblyAI’s Universal-3.5 Pro Realtime is also designed specifically for streaming use cases and is currently priced at $0.45 per hour. AssemblyAI describes the model as supporting native code-switching, contextual awareness, keyterm prompting, and real-time conversation use cases.

So this is not a case of one company supporting streaming and the other not.

The more useful question is:

How much concurrent speech can your infrastructure process while maintaining the latency your application requires?

Code-switching is about more than language detection

Code-switching is often treated as a multilingual feature.

In real conversations, it is a model-behavior problem.

A speaker might say:

“Mujhe payment confirm karna hai, but the transaction is still pending.”

A system that identifies one language at the beginning of the call and sticks with it may struggle with this kind of speech.

AssemblyAI’s Universal-3.5 Pro currently supports native code-switching across 18 languages, including Hindi, Japanese, Mandarin, Arabic, Spanish, French, German, Italian, Portuguese, Turkish and Vietnamese. The model is designed to transcribe words in the language in which they were actually spoken without requiring a separate language-detection pass.

Shunya similarly treats code-switching as a dedicated speech problem through its Zero STT Codeswitch model and broader multilingual stack.

The important point for enterprise buyers is not the language count.

It is whether the model works on your actual code-switched audio.

A banking company should test banking conversations.

A telecom company should test real customer calls.

An Indian consumer application should test Hinglish, regional accents, noisy phone recordings, and proper nouns.

That’s a much better evaluation than checking two boxes labelled “multilingual” and “code-switching.”

When domain terminology matters

This is where modern ASR systems are moving beyond traditional speech recognition.

AssemblyAI’s Universal-3.5 Pro supports both contextual prompting and keyterms prompting. You can describe the context of the recording or supply specific terms you expect the model to encounter. AssemblyAI positions this as a way to improve recognition of names, entities, jargon, and difficult-to-hear terms.

Shunya also supports keyterm normalization, allowing businesses to standardize brand names, acronyms, and custom terminology in the transcript. Its speech intelligence layer exposes this alongside other transcript-level capabilities.

But Shunya adds another layer to the problem: specialized models.

Healthcare is a good example.

With generic ASR, you can provide context for medical terms.

With a specialized healthcare model, the recognition system itself can be optimized around the vocabulary and characteristics of that domain.

Shunya’s Zero STT Med is specifically positioned for clinical vocabulary and healthcare speech.

That distinction becomes important when terminology errors are not just inconvenient, but operationally expensive.

What happens after the transcript?

A transcript is usually not the final product.

It is an input.

A contact center may want to know why a customer called.

A quality team may want to identify angry interactions.

A sales team may want to understand buying intent.

A compliance team may want speaker-level records.

A healthcare application may need a concise summary.

Shunya’s speech intelligence layer is designed around this exact workflow. The same transcription workflow can optionally return diarization, speaker identification, emotion, intent, sentiment, summarization, translation, redaction, word timings, and keyterm normalization.

For example, a customer call can come back as:

Transcript: what was said

Speaker: who said it

Intent: why the person called

Sentiment: overall conversational tone

Emotion: dominant emotion by segment

Summary: what happened

That reduces the distance between audio and an actionable business signal.

AssemblyAI is also strong here.

Its platform includes conversation intelligence capabilities and integrations around summaries, analytics, speaker information, and other downstream processing. Universal-3.5 Pro can also be paired with AssemblyAI’s broader intelligence tooling for summaries and structured extraction.

So the differentiation is not “AssemblyAI has transcription, Shunya has intelligence.”

Both go beyond plain transcription.

The stronger distinction is how closely the speech recognition and speech intelligence layers are connected to the workloads you need to support.

Indian speech is a model problem, not a checkbox

Language lists can be misleading.

Supporting Hindi on paper does not automatically mean supporting the way Hindi is spoken in a customer conversation.

Indian enterprise audio is often a combination of:

• Regional accents
• English words inside Indian-language sentences
• Product and brand names
• Informal speech
• Phone-quality recordings
• Multiple Indian languages across the same organization

Shunya supports 55+ Indian languages and specifically positions Zero STT Indic and Zero STT Codeswitch for Indian-language and mixed-language speech.

That specialization is particularly relevant for Indian banks, insurers, telecom companies, healthcare providers, consumer platforms, and public-sector deployments.

AssemblyAI has expanded its multilingual capabilities substantially, but its current Universal-3.5 Pro code-switching model covers 18 languages, while Universal-2 provides the broader 99+ language option.

For an organization where Indian speech is a core workload rather than a secondary requirement, a dedicated Indic model can therefore be more relevant than a generic global language list.

What does AssemblyAI cost compared with Shunya?

Pricing needs to be compared against the actual workload rather than a single headline number.

AssemblyAI currently lists:

Universal-3.5 Pro async: $0.21/hour

Universal-2 async: $0.15/hour

Universal-3.5 Pro Realtime: $0.45/hour

AssemblyAI also prices certain capabilities as add-ons. Its current pricing lists Medical Mode, diarization, keyterms prompting, voice isolation, and other capabilities separately depending on the API and model.

Shunya’s current pricing for Zero STT is $0.0039/minute on pay-as-you-go pricing, or approximately $0.234/hourbefore volume discounts. Volume pricing goes down to $0.0035/minute. Specialized models are priced separately.

So Shunya should not be positioned as simply “cheaper than AssemblyAI.”

The more useful value question is what you get around that base transcription rate.

Shunya puts transcription and an optional intelligence layer on the same API surface, while enterprise plans also provide access to custom-trained STT models, higher concurrency, self-hosted deployment options, and dedicated support.

For a production team, the total cost can therefore include much more than transcription minutes.

It can include additional APIs, infrastructure, model customization, engineering time, latency optimization, and the work required to turn transcripts into usable business data.

Where AssemblyAI is the better choice

A fair comparison should acknowledge where AssemblyAI is genuinely strong.

AssemblyAI is a compelling option when you want a mature speech API with strong transcription and conversation intelligence capabilities, particularly for recorded meetings, interviews, post-call analysis, and English-centric or broadly multilingual workflows.

Its latest Universal-3.5 Pro release is particularly strong around contextual prompting, native code-switching, diarization, and real-world transcription quality.

AssemblyAI also makes sense for teams already invested in its broader ecosystem and looking to add speech capabilities without changing their existing architecture.

In other words, there are production scenarios where AssemblyAI is absolutely the right choice.

When Shunya makes more sense

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

That includes systems where:

The audio is noisy or inconsistent.

Real calls matter more than clean benchmark recordings.

Indian languages are core to the product.

Regional and mixed-language speech are first-class requirements.

Code-switching is normal.

The user switches languages naturally within a conversation.

The domain vocabulary is difficult.

Healthcare, finance, telecom, product names, acronyms, and specialized terminology need more than generic recognition.

Concurrency matters.

You need to process large numbers of simultaneous conversations without building an increasingly expensive speech infrastructure layer.

The transcript is only the beginning.

You need intent, sentiment, emotion, speaker information, or other structured conversation intelligence.

Deployment requirements are complex.

Your enterprise may need cloud, on-premise, or more controlled deployment options. Shunya’s enterprise plans include self-hosted deployment options and higher-concurrency support.

Shunya vs AssemblyAI by use case

Use case	Better fit
General meeting transcription	AssemblyAI
Interview transcription	AssemblyAI
Post-call conversation analysis	Both
English-first applications	Both
High-volume contact centers	Shunya
Indian-language contact centers	Shunya
Hinglish and mixed-language calls	Shunya
Healthcare speech	Shunya
Domain-specific enterprise vocabulary	Both, with different approaches
Real-time voice systems	Both
High-concurrency speech workloads	Shunya
Transcript + built-in speech intelligence	Shunya
Teams wanting AssemblyAI’s broader conversation-intelligence ecosystem	AssemblyAI

Don’t choose from a benchmark table. Test your own audio.

This is the most important part of any ASR evaluation.

Take 20 to 50 representative recordings from your actual production environment.

Do not choose only the clean calls.

Include the worst audio you receive regularly.

Then measure:

Word error rate

How much text is wrong?

Critical word error rate

How often are product names, customer names, medication names, account information, numbers, and other important terms wrong?

Code-switch accuracy

Does the model preserve the language actually spoken?

Speaker accuracy

Can you reliably tell who said what?

Latency

How quickly does usable text appear?

Concurrency

How does performance change when hundreds of streams run simultaneously?

Downstream accuracy

Does the transcript produce the correct intent, sentiment, action, or business outcome?

This last measurement is particularly important.

A transcript can have a slightly better WER while producing worse results in the application that actually matters.

For a voice agent, measure whether the agent understands the customer.

For a contact center, measure whether calls are categorized correctly.

For healthcare, measure whether critical terminology is recognized accurately.

For an enterprise search system, measure whether users can actually find the information they need.

That is the test that matters.

The real difference: speech API or speech infrastructure?

AssemblyAI and Shunya both make it possible to add production speech recognition without training an ASR system from scratch.

The difference is where they put the emphasis.

AssemblyAI has built a strong platform around transcription, contextual understanding, and conversation intelligence, with Universal-3.5 Pro adding increasingly sophisticated handling of code-switching and difficult vocabulary.

Shunya is focused more heavily on the speech itself: difficult audio, multilingual and regional speech, specialized models, low-latency processing, high concurrency, and intelligence that sits directly alongside the transcript.

That difference becomes important when speech is not a side feature in your product.

It is the product.

Final words

AssemblyAI is a strong speech platform. Its latest Universal-3.5 Pro release shows how quickly modern speech recognition is moving beyond simple transcription into contextual, multilingual, real-time systems.

But production speech has a long list of problems that a clean transcript does not reveal.

Noise.

Accents.

Crosstalk.

Domain terminology.

Code-switching.

Regional languages.

Concurrency.

Latency.

And the need to turn speech into something actionable.

Shunya’s approach is to treat those problems as first-class parts of the speech recognition system rather than problems to solve around it.

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

The best way to decide is still simple:

Take your hardest 20 recordings and test them.

Not the easiest audio.

Not a benchmark dataset.

The calls your customers actually make.

That’s where the difference between a good speech API and a production-ready speech platform becomes clear.

Contact Us to know more

Frequently asked questions

Is Shunya more accurate than AssemblyAI?

There is no single universal answer. Shunya currently publishes a 3.10% composite WER across eight OpenASR benchmarks, but accuracy depends heavily on the dataset, language, audio conditions, and model.

The correct comparison is to run both systems on representative production audio.

Which supports more languages, Shunya or AssemblyAI?

Shunya publicly supports 216+ languages. AssemblyAI currently lists 99+ languages on Universal-2, while Universal-3.5 Pro supports 18 languages with native code-switching.

Does AssemblyAI support code-switching?

Yes. Universal-3.5 Pro currently supports native code-switching across 18 languages, including Hindi, without requiring a separate language-detection pass.

Does Shunya support code-switching?

Yes. Shunya has a dedicated Zero STT Codeswitch model and positions code-switched speech as a core production workload.

Does Shunya support speaker diarization?

Yes. Shunya’s intelligence layer supports speaker diarization and speaker identification alongside transcription.

Does Shunya support healthcare transcription?

Yes. Zero STT Med is a specialized model for healthcare and clinical vocabulary. Shunya also provides medical keyterm correction capabilities through its speech intelligence layer.

Which is cheaper, Shunya or AssemblyAI?

It depends on the model and workload. Shunya Zero STT starts at $0.0039/minute on pay-as-you-go pricing, while AssemblyAI currently lists Universal-3.5 Pro async at $0.21/hour and Universal-3.5 Pro Realtime at $0.45/hour. Feature add-ons and volume pricing can change the effective cost.

Can Shunya be deployed outside the public cloud?

Yes. Shunya’s enterprise offering includes self-hosted deployment options, while its documentation describes managed cloud and self-hosted deployment models.

How should I compare Shunya and AssemblyAI?

Use your own audio. Test WER, critical terminology, code-switching, diarization, latency, concurrency, and the downstream business task the transcript supports. That’s more informative than comparing a single benchmark number.