Why Shunya is a Stronger Choice than Rev AI for Production Speech to Text

Rev AI is a well-established speech-to-text platform built around easy-to-integrate APIs, real-time transcription, multilingual ASR, custom vocabulary, diarization, and transcript analytics. Its current documentation lists 58+ languages for asynchronous speech-to-text, while streaming supports a smaller set of languages. Rev AI also offers topic extraction, sentiment analysis, language identification, and a self-hosted option for pre-recorded audio.

That makes Rev AI a practical choice for many transcription workflows.

But modern enterprise ASR is no longer just about turning audio into text.

Production systems need to deal with noisy calls, accents, interruptions, domain terminology, code-switching, latency, high concurrency, and downstream intelligence. The more important question is what the speech platform can do once transcription becomes a core part of the product.

This is where Shunya takes a broader approach.

Shunya Zero STT combines 216+ languages, published 3.10% composite WER, sub-500ms first-token latency, 240+ concurrent streams per GPU, specialized speech models, and speech intelligence capabilities. Its model family includes dedicated paths for Indian languages, code-switching, healthcare, and on-device inference.

The result is a platform designed not only to transcribe speech, but to help enterprises understand and act on conversations in production.

Shunya and Rev AI at a glance

Capability	Shunya	Rev AI
Languages	216+	58+ async; fewer in streaming
Published WER	3.10% composite across 8 OpenASR benchmarks	Model and language dependent
Streaming	Sub-500ms first token	Yes
Throughput	146× real time on published benchmark	Model/workload dependent
Code-switching	Dedicated model	Multilingual capabilities vary by API
Indian-language specialization	55+ language Indic model	Broad multilingual offering
Domain terminology	Keyterm normalization + specialized models	Custom vocabulary
Diarization	Yes	Yes
Intent	Yes	Requires downstream workflow
Sentiment	Yes	Separate API
Emotion	Yes	Not a core transcription API feature
Healthcare	Dedicated Zero STT Med	Healthcare/HIPAA capabilities
Self-hosted	Enterprise/private options	Self-hosted async STT
Edge inference	Dedicated on-device models	Not the primary product path
Broader voice stack	STT, TTS, voice agents, SLMs, edge AI	Primarily speech APIs

Rev AI is simple to integrate. Shunya is built to do more with the speech.

Rev AI’s developer experience is one of its strengths.

Its API platform gives developers dedicated services for asynchronous speech-to-text, streaming speech-to-text, custom vocabulary, topic extraction, sentiment analysis, language identification, and forced alignment.

That modular design is useful.

A team can start with transcription, then add another API when it needs additional analysis.

But that’s also where enterprise speech architectures can become fragmented.

A production workflow might eventually look like:

Audio → ASR → language detection → speaker processing → transcript → sentiment → topic extraction → intent model → application workflow

Every additional step introduces another integration, another API, another latency component, and another place to manage failures.

Shunya’s approach is to make speech intelligence part of the speech stack.

Its speech intelligence capabilities are designed to extend the transcript with intent, sentiment, emotion, diarization, speaker information, and other structured signals.

That doesn’t eliminate the need for application logic.

It does, however, reduce the distance between “we have audio” and “we know what happened in the conversation.”

Production speech is harder than transcription demos

Real enterprise audio is messy.

A customer may call from a train.

An employee may use a low-quality headset.

Two people may interrupt each other.

A speaker may switch languages halfway through a sentence.

A product name may be unusual.

An account number may be spoken quickly.

A medical term may sound almost identical to another term.

These are very different failure modes.

Shunya’s Zero STT positioning explicitly focuses on noise, accents, phone audio, multiple speakers, code-switching, and domain vocabulary.

Rev AI also has strong production-oriented capabilities. Its self-hosted ASR offering highlights global accent support, speaker separation, custom vocabulary, timestamps, punctuation, and long-form transcription, and says hour-long files can be transcribed in under a minute in its self-hosted environment.

So the distinction isn’t that one platform understands “real speech” and the other doesn’t.

The stronger question is:

How many different kinds of production speech can the platform optimize for?

Shunya uses specialized models for different speech problems

One of Shunya’s strongest differentiators is its model family.

Instead of treating every audio workload as the same ASR problem, Shunya provides dedicated models for different environments.

Zero STT handles broad speech recognition.

Zero STT Indic is designed around Indian languages and regional speech.

Zero STT Codeswitch is designed for mixed-language conversations.

Zero STT Med is built around healthcare and clinical terminology.

Shunya also offers on-device models for lightweight local speech workloads.

This matters because the hardest ASR problems are not always solved by making one general-purpose model larger.

A customer-service call in Hinglish has different requirements from a medical consultation.

A medical consultation has different requirements from a financial call.

A regional-language contact center has different requirements from a global English meeting.

Shunya’s architecture gives enterprises the ability to align the model with the workload.

Rev AI’s customization is useful, especially for terminology

Rev AI provides a Custom Vocabulary API designed to improve recognition of specialized words, names, and terminology.

That is valuable for:

Brand names

People’s names

Industry terminology

Acronyms

Rare words

Shunya also supports terminology normalization, but adds specialized models for domains where vocabulary is only one part of the problem.

There is a practical difference between:

“This word appears in our vocabulary.”

and:

“This entire type of speech is common in our domain.”

For many enterprise workloads, the second matters more.

Consider healthcare.

A medical conversation is not difficult only because it contains unfamiliar words. It has drug names, abbreviations, dosages, procedures, diagnoses, anatomy, and domain-specific conversational patterns.

That’s why Shunya has a dedicated Zero STT Med model rather than relying only on generic vocabulary customization.

Indian speech is a particularly important specialization

Rev AI’s current multilingual offering is substantial, with 58+ languages listed for asynchronous speech-to-text.

But language coverage is not the same as language specialization.

India is a good example.

A real customer call might contain:

“Sir mera payment deduct ho gaya but refund abhi tak reflect nahi hua.”

The system needs to recognize Hindi, English, financial terminology, customer speech patterns, and possibly an Indian regional accent in the same interaction.

Shunya’s Zero STT Indic is specifically positioned for 55+ Indian languages and regional speech, along with 216+ global.

Its published benchmark also reports 11.9% average Hindi WER across seven datasets.

That makes Indic speech an explicit part of the model strategy rather than a language-support checkbox.

For an Indian bank, insurer, telecom company, healthcare provider, or consumer platform, this distinction can be significant.

Code-switching is not the same as detecting a language

Rev AI provides a Language Identification API that analyzes audio and identifies probable languages.

That’s useful when you need to know what language is being spoken.

But consider:

“Mujhe order cancel karna hai because it hasn’t arrived yet.”

The speaker isn’t making a language-selection decision.

They are simply speaking naturally.

That’s code-switching.

Shunya’s Zero STT Codeswitch is designed specifically for mixed-language speech such as Hinglish, Tanglish, and Benglish.

This distinction is particularly relevant for customer support and voice agents, where users rarely follow the neat language boundaries assumed by traditional ASR pipelines.

Accuracy should be measured on business-critical words

Shunya currently publishes a 3.10% composite WER in English across eight OpenASR benchmarks, including LibriSpeech, SPGISpeech, TED-LIUM, AMI, VoxPopuli, GigaSpeech, and Earnings22.

Its benchmark also reports 146× real-time throughput.

Those are strong indicators.

But an enterprise evaluation should go further.

Imagine a transcript containing:

Account number 438729

Policy ID HDFC-2047

₹25,000

Metformin 500 mg

Order number AZ-19B

Getting every ordinary sentence correct while repeatedly getting those values wrong can make the system unusable.

So the benchmark should include:

Overall WER

Critical-word accuracy

Numbers

Alphanumeric strings

Proper nouns

Domain terminology

Speaker attribution

Code-switched speech

Noisy audio

This is especially important for BFSI, healthcare, telecom, logistics, and contact centers, where a small transcription error can trigger the wrong downstream action.

Real-time ASR is about latency and stability

Rev AI provides a dedicated Streaming Speech-to-Text API for real-time transcription.

Shunya currently positions Zero STT around sub-500ms first-token latency.

For interactive systems, however, “supports streaming” is only the beginning.

You need to measure:

Time to first token

Finalization latency

Transcript stability

Speaker attribution

Interruption handling

Throughput

Concurrent sessions

A voice agent might need to start reasoning while the user is still speaking.

A contact center may need to identify intent during the call rather than after it.

A live assistant cannot wait for a five-minute recording to finish.

That is why Shunya publishes not only latency but also throughput and concurrency metrics.

Concurrency becomes a major differentiator at enterprise scale

Ten simultaneous calls are not especially difficult.

Five hundred are a different problem.

Five thousand are a very different architecture.

Shunya publishes 240+ concurrent streams per GPU for Zero STT.

That gives engineering teams a concrete way to think about ASR infrastructure:

How many streams can one inference resource support?

What happens to latency under load?

How much infrastructure is required at peak traffic?

What is the cost per concurrent conversation?

Rev AI offers a managed API for real-time workloads and a self-hosted option for asynchronous pre-recorded speech. Its self-hosted solution is positioned around high-volume transcription and can process up to eight distinct speaker channels.

For high-volume real-time applications, however, concurrency should be benchmarked directly rather than inferred from a generic “scales automatically” claim.

Speech intelligence is where transcription becomes useful

Transcription is often the beginning of the workflow, not the end.

A contact center may want:

Intent

Why did the customer call?

Sentiment

How positive or negative was the interaction?

Emotion

Was the customer frustrated, confused, or calm?

Speaker

Who said what?

Summary

What happened?

Shunya’s speech stack is designed to move directly from speech to transcript and intelligence, with intent, sentiment, emotion, and speaker information available around the transcription workflow.

Rev AI also provides analytics beyond ASR. Its Topic Extraction API identifies important topics and returns relevant transcript fragments, while its Sentiment Analysis API classifies sentence-level sentiment as positive, negative, or neutral.

The difference is largely architectural.

Rev AI provides a modular collection of speech APIs.

Shunya is building toward a speech intelligence layer, where transcription feeds directly into broader voice understanding.

For applications where every call needs to become a structured business event, this can reduce the amount of orchestration required.

The broader Shunya platform changes the role of ASR

Shunya’s ASR is also part of a larger voice-AI platform.

That platform connects:

Speech-to-text

Text-to-speech

Voice agents

Custom SLMs

Edge speech understanding

Knowledge graphs

This matters for teams building voice applications.

Consider a customer service voice agent:

Customer speaks

↓

STT understands speech

↓

Intent is identified

↓

Knowledge graph grounds the answer

↓

SLM reasons over the request

↓

TTS generates the response

↓

Voice agent speaks back

The ASR model is therefore not just a transcription tool.

It is the first intelligence layer in the interaction.

That is where Shunya’s broader platform strategy can become a meaningful differentiator for voice-AI applications.

On-device and private deployment open another class of use cases

Rev AI’s self-hosted offering is a significant advantage when organizations want to keep pre-recorded audio transcription inside their own environment. Rev AI positions this specifically for enterprises working with sensitive audio and large-scale transcription.

Shunya also provides private deployment options, but extends the story toward edge inference through its on-device model family.

The Zero STT Tiny ONNX / on-device models are designed for lightweight speech processing where local inference, low latency, or limited connectivity matters.

That opens different possibilities:

Embedded devices

Retail systems

Automotive

Field applications

Private enterprise environments

Offline or constrained networks

The important distinction is that private speech infrastructure and edge speech intelligence solve different problems.

Pricing should be compared against the complete workflow

ASR pricing can look simple until you add the actual features an enterprise needs.

Rev AI’s current public documentation emphasizes flexible API access and free trial credits, while its current product pages promote 58+ language transcription and multiple speech APIs.

Shunya currently lists:

Zero STT: $0.0039/minute

Zero STT Indic: $0.0045/minute

Zero STT Codeswitch: $0.005/minute

Zero STT Med: $0.005/minute

Volume pricing is also available.

At the base Zero STT rate, that is approximately $0.234/hour before volume discounts.

But price per minute should not be the only calculation.

For a production system, include:

Transcription

Additional analytics APIs

Custom vocabulary

Model customization

Infrastructure

Concurrency

Private deployment

Engineering effort

Downstream intelligence

A platform that has a lower transcription price can still cost more if the architecture requires several additional services to achieve the desired business outcome.

Where Rev AI is the better choice

Rev AI is a strong platform for teams that want:

A straightforward transcription API

Its developer-first API suite makes it easy to integrate transcription and individual speech capabilities.

Broad multilingual coverage

Rev AI currently lists 58+ languages for asynchronous speech-to-text.

Custom vocabulary

Useful for names, specialized terminology, and industry jargon.

Speaker separation

Its self-hosted solution supports speaker channels and separation for multi-speaker audio.

Modular speech analytics

Topic extraction and sentiment analysis are available as dedicated APIs.

Self-hosted pre-recorded transcription

A meaningful option for organizations that want speech processing within their own infrastructure.

For conventional transcription and modular speech analytics, Rev AI can be an excellent fit.

When Shunya makes more sense

Shunya becomes more compelling when:

Indian speech is a core requirement

Use the dedicated Zero STT Indic model for 55+ Indian languages and regional speech.

Code-switching is normal

Use Zero STT Codeswitch for mixed-language conversations such as Hinglish and Tanglish.

The domain is specialized

Use Zero STT Med for healthcare and domain-specific terminology.

You need high concurrency

Shunya publishes 240+ concurrent streams per GPU.

You need low-latency speech

Shunya publishes sub-500ms first-token latency.

You need more than a transcript

Use the speech intelligence layer for intent, sentiment, emotion, diarization, and speaker information.

You are building a voice-AI product

The Shunya Platform combines STT, TTS, voice agents, SLMs, edge intelligence, and knowledge-grounded AI.

You need lightweight local inference

Explore the on-device model family.

Shunya vs Rev AI by use case

Use case	Stronger fit
Straightforward transcription API	Rev AI
General English transcription	Both
Broad multilingual batch transcription	Both
Indian-language contact centers	Shunya
Hinglish / Indic code-switching	Shunya
Domain-specific ASR	Shunya
Medical speech	Both
Real-time transcription	Both
High-concurrency voice applications	Shunya
Topic extraction	Rev AI
Basic sentiment analysis	Both
Real-time speech intelligence	Shunya
Private batch transcription	Rev AI / Both
Edge speech inference	Shunya
Voice-agent platform	Shunya
Modular developer APIs	Rev AI

How to benchmark Shunya and Rev AI

The best comparison is not a generic audio file.

Use your production audio.

For a contact center, include:

Noisy calls

Indian accents

Hinglish

Customer names

Account numbers

Product names

Agent-customer overlap

For healthcare, include:

Drug names

Dosages

Procedures

Clinical abbreviations

Doctor-patient conversations

For voice agents, include:

Interruptions

Short utterances

Rapid turn-taking

Background noise

Code-switching

Then measure:

Accuracy

WER and critical-word accuracy

Language

Language recognition and code-switching accuracy

Terminology

Names, numbers, products, acronyms, and domain terms

Speakers

Diarization and speaker attribution

Real-time behavior

First-token latency and finalization latency

Scale

Throughput and concurrent sessions

Business outcome

Intent accuracy, call routing, agent assist, task completion, or downstream workflow accuracy

That final metric is often the most important.

A transcript is valuable only when it helps the application do something correctly.

The real decision: transcription API or speech intelligence layer?

Rev AI gives developers a clean collection of speech APIs.

That makes it attractive when the immediate requirement is:

“We need reliable transcription and a few speech analytics capabilities.”

Shunya is designed for a broader requirement:

“Speech is becoming a core input to our product, and we need to understand it, process it, and act on it at scale.”

That difference becomes significant for:

Contact centers

Voice agents

BFSI

Healthcare

Telecom

E-commerce

Enterprise automation

These are applications where the transcript is only one step in the workflow.

Final words

Rev AI is a strong speech-to-text platform with a straightforward developer experience, 58+ asynchronous languages, real-time transcription, custom vocabulary, diarization, language identification, topic extraction, sentiment analysis, and self-hosted pre-recorded speech recognition.

Shunya’s proposition goes beyond a transcription API.

Its Zero STT family lets enterprises choose models according to the speech problem:

Universal speech

55+ Indian languages

Code-switched conversations

Medical speech

And its broader platform adds speech intelligence, voice agents, TTS, SLMs, edge models, and knowledge-grounded AI.

On performance, Shunya currently publishes 3.10% composite WER, sub-500ms first-token latency, 146× real-time throughput, and 240+ concurrent streams per GPU.

That creates a strong proposition for enterprises where speech is a central part of the product rather than a secondary transcription feature.

The strongest reason to choose Shunya is therefore not simply:

“We support more languages.”

It is:

“We can specialize the speech stack around the way your users actually speak and what your application needs to do with that speech.”

For an Indian contact center, that can mean Indic and code-switched models.

For healthcare, it can mean specialized medical recognition.

For a voice agent, it can mean low latency, high concurrency, and speech intelligence.

For an edge application, it can mean local inference.

And for an enterprise building a larger voice system, it can mean moving from speech-to-text to speech-to-intelligencewithout assembling the entire stack from separate services.

The best way to make the final decision is simple:

Take your hardest production recordings and test both.

Measure WER, critical terminology, code-switching, speaker accuracy, latency, concurrency, and the actual business outcome.

Because the best ASR platform is not the one with the longest feature list.

It is the one that performs best on the speech your business actually depends on.

Frequently asked questions

Is Shunya better than Rev AI?

It depends on the workload. Rev AI is strong for straightforward transcription, multilingual ASR, custom vocabulary, diarization, and modular analytics. Shunya is particularly strong for specialized speech models, Indian languages, code-switching, high concurrency, speech intelligence, and broader voice-AI applications.

Which supports more languages?

Shunya currently supports 216+ languages, while Rev AI lists 58+ languages for asynchronous speech-to-text. Rev AI’s streaming language availability is smaller and should be checked for the specific language required.

Does Rev AI support real-time transcription?

Yes. Rev AI provides a dedicated Streaming Speech-to-Text API for real-time transcription.

Does Shunya support code-switching?

Yes. Zero STT Codeswitch is specifically designed for mixed-language conversations, including use cases such as Hinglish and Tanglish.

Does Rev AI have custom vocabulary?

Yes. Rev AI provides a dedicated Custom Vocabulary API for improving recognition of specialized terms and names.

Does Shunya have a medical speech model?

Yes. Zero STT Med is designed for medical and clinical speech, including specialized healthcare terminology.

Does Rev AI support sentiment and topic analysis?

Yes. Rev AI provides separate Sentiment Analysis and Topic Extraction APIs that operate on transcripts.

Does Shunya support intent, sentiment, and emotion?

Yes. Shunya’s speech intelligence capabilities extend transcription with intent, sentiment, emotion, diarization, and speaker information.

Which is faster?

Both support real-time speech recognition. Shunya currently publishes sub-500ms first-token latency. Rev AI provides a dedicated streaming API, but performance should be measured under the same audio, network, and concurrency conditions.

Can Rev AI be self-hosted?

Yes. Rev AI offers a self-hosted asynchronous speech-to-text solution for pre-recorded audio with features including custom vocabulary, timestamps, and speaker separation.

Can Shunya run on-device?

Yes. Shunya provides on-device models designed for lightweight local speech processing.

Which is cheaper?

Shunya currently lists Zero STT at $0.0039/minute, with specialized models priced separately. Rev AI pricing depends on the API and product being used, so the fairest comparison is based on the exact workload and required features.

Which is better for voice agents?

Both can be integrated into voice-agent systems. Shunya has a broader native proposition because its platform combines STT, speech intelligence, TTS, voice agents, SLMs, and knowledge-grounded AI.

How should I compare Shunya and Rev AI?

Use representative production audio and measure WER, critical terminology, language behavior, code-switching, diarization, latency, concurrency, and downstream task accuracy.