Shunya TTS and Deepgram Aura-2: Enterprise Voice AI Comparison

Deepgram Aura was originally introduced as a real-time, conversational text-to-speech system for voice AI agents, with a strong emphasis on the three things that matter most in production voice: speed, quality, and efficiency.

Since then, the Deepgram TTS stack has evolved.

Today, Aura-2 is Deepgram’s broadest-language TTS model, supporting English, Spanish, German, French, Dutch, Italian, and Japanese, while Flux TTS is now Deepgram’s newest voice-agent-first model for English and became generally available in August 2026.

That makes a current comparison with Deepgram a little more nuanced.

For multilingual TTS, Aura-2 is the relevant Deepgram model.

For English-first realtime voice agents, Deepgram now recommends Flux.

Shunya approaches the problem from a different direction. Its TTS stack combines global, Asian, and 23 Indic language applications, with 46 voices across 23 Indic languages plus English, cross-lingual synthesis, expressive styles, voice cloning, streaming, and telephony support.

The useful question for an enterprise is therefore not simply which TTS model sounds better.

It is:

Which speech stack fits your languages, conversations, infrastructure, and production requirements?

Deepgram Aura has evolved beyond the original model

The original Deepgram Aura was designed around real-time conversational speech, especially for AI agents, customer service, and high-throughput voice applications.

The current Deepgram architecture separates that offering into two generations.

Aura-2 is the multilingual model.

Flux TTS is the newer conversational model for English.

Deepgram describes Flux as streaming-first, turn-based, expressive by default, and designed specifically for voice-agent pipelines. It can maintain voice consistency across turns and report exactly what was spoken before an interruption, which is useful for barge-in handling.

Aura-2 remains important because it supports the languages that Flux does not currently cover.

So when someone searches for Deepgram Aura, there are effectively two things to understand today:

Aura-2 for multilingual synthesis

and

Flux for the newest English realtime voice-agent experience.

Shunya approaches TTS as part of a wider voice stack

Shunya’s Zero TTS is built around production voice applications rather than only standalone speech generation.

The current documentation lists:

46 speaker voices

23 Indic languages plus English

11 expression styles

Cross-lingual synthesis

Voice cloning

Batch and streaming TTS

and support for common web and telephony formats.

Shunya also supports an extended set of world languages, including Japanese, Korean, Chinese, Arabic, French, German, Italian, and others, beyond its named Indic voice catalogue.

The broader Shunya Platform connects TTS with STT, speech intelligence, voice agents, and custom SLMs.

That distinction matters when speech is not an isolated API call.

For example:

Customer speaks → STT → reasoning → TTS → phone call

or:

Customer speaks → STT → enterprise knowledge → voice agent → TTS

In these systems, the TTS model has to fit into the complete conversation loop.

Voice quality is about difficult production text

A basic TTS test is easy:

“Welcome to customer support.”

A production workload is not.

Consider:

“Your payment of ₹1,25,499.50 for order AX-2047 has been successfully processed.”

Now the system needs to correctly handle:

Currency

Numbers

Order IDs

Punctuation

Pronunciation

Pausing

Deepgram explicitly provides pronunciation controls in Aura-2, including the ability to override pronunciation using IPA, and speed controls for English and Spanish. These controls are designed for brand names, industry terminology, and other difficult text.

Deepgram’s prompting documentation also covers pronunciation strategies for numbers, acronyms, foreign words, and custom pronunciations.

Shunya’s TTS API also exposes a language parameter specifically to improve preprocessing of numbers, dates, and script-specific rules, which becomes particularly important for languages such as Hindi and Marathi that share scripts.

For enterprise TTS, this is more important than a generic “human-like” claim.

Indic voice quality is a different requirement

Deepgram currently supports English, Spanish, German, French, Dutch, Italian, and Japanese through Aura-2.

Its English voice catalogue includes a range of accents and characteristics, and its current Flux catalogue includes an Indian-accented English voice, Priya.

But Indian English is not the same problem as Indian-language TTS.

An enterprise serving India may need:

Hindi

Tamil

Telugu

Bengali

Marathi

Gujarati

Punjabi

and many other regional languages and mixed-language conversations.

Shunya’s Zero TTS Indic is specifically designed around Indian phonetics, regional pronunciation, accents, dialects, and mixed-language speech. Its current TTS documentation lists 23 Indic languages with 46 named voices.

That makes this a key architectural difference.

Deepgram has a strong multilingual TTS catalogue.

Shunya puts much more emphasis on Indic voice infrastructure within a broader global and Asian TTS system.

Multilingual voice identity matters for global enterprises

Suppose a company creates a recognizable customer-support voice.

The application starts in English.

The customer switches to Hindi.

Later, the same customer switches to Tamil.

A multilingual voice system should ideally preserve:

Timbre

Speaking personality

Prosody

Brand identity

Shunya explicitly supports this model. Its documentation states that every voice can speak every supported language, enabling cross-lingual synthesis using the same speaker identity.

Deepgram’s multilingual model coverage is broader than a single-language system, and selected Aura-2 Spanish voices support English-Spanish code-switching.

But its current Aura-2 language architecture is still a collection of language-specific model and voice options rather than a 23-language Indic voice system.

For an enterprise with a primarily European or English-speaking audience, that distinction may not matter.

For an enterprise serving India and Asian markets, it can matter considerably.

Expression and conversational delivery

Deepgram’s newer Flux TTS is explicitly described as expressive by default and built around conversation. Its architecture supports turn lifecycle, cross-turn voice consistency, and interruption-aware voice agents.

That is an important shift from conventional TTS.

The goal is no longer only:

Text → Speech

It becomes:

Conversation → Context → Speech

Shunya provides a more explicit style system with 11 expression styles, including Happy, Sad, News, Narrative, Conversational, and Enthusiastic, alongside other emotional styles.

That makes the two approaches somewhat different.

Deepgram Flux: conversation-native generation.

Shunya Zero TTS: structured expression and voice control.

For an AI voice agent, the most important test is whether a voice responds appropriately when the conversation changes.

A customer saying:

“That’s great, thank you!”

should not receive the same delivery as:

“I’ve already called twice about this.”

Prosody needs to change with context.

Realtime TTS is where Deepgram has a strong architecture

Deepgram’s current Flux TTS is specifically designed for realtime voice agents.

Its /v2/speak WebSocket supports:

Streaming

Turn-based lifecycle

Cross-turn voice consistency

Interruption handling

Raw PCM

μ-law

A-law

This makes the model particularly suitable for voice agents and telephony.

Aura-2 also supports streaming and can be used in Voice Agent workflows.

Shunya provides WebSocket streaming TTS, with its documentation reporting first audio in under 350 ms and describing a direct LLM → TTS pipeline for low-latency voice agents.

The key point is that raw TTS latency isn’t the final metric.

Measure:

User stops speaking

→ STT detects the turn

→ LLM starts producing

→ TTS receives text

→ First audio reaches the user

That’s the latency that matters in a real conversation.

Telephony is a major production consideration

Voice AI often ends up on a phone call.

That changes the technical requirements.

Deepgram Flux’s streaming transport directly supports μ-law and A-law, along with multiple PCM sample rates, making it well suited to realtime audio and telephony pipelines.

Aura also supports compressed and containerized output formats, which Deepgram specifically describes as one of Aura’s strengths inside the Voice Agent API.

Shunya provides PCM, WAV, MP3, OGG Opus, FLAC, μ-law, and A-law through its TTS API.

For a contact center or IVR, don’t benchmark TTS only through headphones.

Test it through the actual:

SIP / telephony provider

Codec

Audio pipeline

Network

and measure customer-perceived latency and intelligibility.

Pronunciation controls can make or break enterprise TTS

Deepgram provides particularly useful controls here.

Aura-2 supports:

Speed control

Pronunciation override

and pronunciation overrides can use IPA notation for difficult words.

This is especially useful for:

Drug names

Company names

Acronyms

Product names

Technical terminology

Deepgram’s documentation explicitly positions these controls for enterprise use cases requiring precise pronunciation of industry-specific terminology and brand names.

Shunya’s TTS API also provides language-aware preprocessing, speed control, and voice selection, while its broader Indic stack is designed around regional pronunciation.

For a production evaluation, create a pronunciation test set rather than relying on generic sentences.

Include:

Customer names

Indian place names

Drug names

Bank names

Acronyms

Currency

Product SKUs

Order IDs

Then score exact pronunciation.

Voice cloning is another major difference

Shunya provides voice cloning from a short 3–6 second reference clip, with the clone available across its supported Indic language set.

This can be useful when the requirement is:

One brand voice

Across multiple languages

Without recording separate voices for each language

Deepgram’s current Aura documentation focuses on its pre-built voice catalogue rather than presenting Aura-2 as a self-service voice-cloning platform.

That distinction is useful when voice identity is a core product requirement.

For enterprises, cloning also needs a proper governance process.

The important questions are:

Who owns the source voice?

Was consent obtained?

Where is the reference audio stored?

Can the voice be used across languages?

Can the voice be deployed privately?

These questions matter as much as similarity.

Deployment can change the buying decision

Deepgram provides self-hosting options for Aura-2, with deployment configurations for English and Spanish services and GPU-based deployment requirements documented for its self-hosted TTS stack.

That is important for enterprises that need greater control over infrastructure.

Shunya also offers self-hosted and on-device deployment options on Enterprise, alongside cloud APIs.

This creates a useful evaluation axis beyond model quality:

Cloud only

Private infrastructure

Self-hosted

Edge / on-device

The right architecture depends on the application’s latency, data, regulatory, and connectivity requirements.

Pricing

Deepgram’s current published TTS pricing is:

Model	Pay-as-you-go
Flux TTS	$0.045 / 1K characters
Aura-2	$0.030 / 1K characters
Aura-1	$0.015 / 1K characters

Deepgram states that these standard rates apply from September 13, 2026, following the introductory Flux pricing period.

That puts Aura-2 at roughly:

$30 per 1 million characters

Shunya currently lists:

$5.26 per 1 million characters

for Zero TTS pay-as-you-go, with $4.74 per million characters at volume pricing. Enterprise plans have custom pricing and self-hosted options.

Pricing alone should not determine the platform.

A production voice system also incurs:

STT

LLM / SLM

TTS

Telephony

Infrastructure

Concurrency

Voice cloning

The meaningful metric is cost per completed interaction, not simply cost per million characters.

Where Deepgram stands out

Deepgram is particularly compelling when you need:

A strong speech infrastructure provider

Deepgram combines STT and TTS within a broader speech platform.

Realtime voice agents

Flux is explicitly designed around streaming, turns, interruptions, and conversational consistency.

English conversational voice

Deepgram currently positions Flux as its recommended model for English voice-agent applications.

Pronunciation control

Aura-2 provides runtime pronunciation and speed controls for English and Spanish.

Multilingual European and Asian TTS

Aura-2 currently supports English, Spanish, German, French, Dutch, Italian, and Japanese.

Self-hosted speech

Deepgram documents self-hosted Aura-2 deployments for supported languages.

Where Shunya fits differently

Shunya is particularly relevant when you need:

Global, Asian, and 23 Indic language applications

The documented TTS catalogue includes 46 voices across 23 Indic languages plus English, while the broader service supports additional world languages.

Indic voice specialization

Zero TTS Indic is built around Indian phonetics, regional pronunciation, accents, and local speech patterns.

One voice across languages

Shunya’s TTS architecture supports cross-lingual synthesis with the same voice identity across supported languages.

Short-reference voice cloning

Voice cloning uses a 3–6 second reference clip in the documented TTS workflow.

Enterprise telephony

μ-law and A-law are available directly through the TTS API.

Broader voice AI

TTS can sit alongside STT, speech intelligence, voice agents, and custom SLMs through the Shunya Platform.

Private and edge deployment

Enterprise plans include self-hosted and on-device options.

Shunya TTS and Deepgram by use case

Use case	Deepgram	Shunya
General enterprise TTS	Strong fit	Strong fit
English realtime voice agents	Flux	Strong fit
Multilingual TTS	Aura-2	Strong fit
Indic languages	Limited current Aura-2 coverage	Core capability
Asian language TTS	Japanese	Broader Asian coverage
Cross-lingual voice identity	More model-dependent	Core capability
Pronunciation controls	Strong	Strong
Voice cloning	Not an Aura-2 focus	Yes
Expressive styles	Flux / voice controls	11 styles
Turn-aware voice agents	Flux	Voice-agent integration
Telephony	Strong	Strong
Self-hosting	Aura-2	Enterprise
On-device	Product dependent	Available through Enterprise
STT + TTS stack	Deepgram	Shunya
Indian enterprise voice	Limited Aura-2 language coverage	Core use case

This table describes documented capabilities and intended positioning. Actual speech quality should be validated with your own workload.

How to benchmark Deepgram Aura-2 against Shunya

Start with real production text, not benchmark sentences designed to make every system sound good.

Build a test set with:

Indian names

Currency

Dates

Numbers

Product names

Acronyms

Medical terms

Order IDs

Long customer responses

English + Indian-language code-switching

For each platform, measure:

Naturalness

Does speech remain natural during both short responses and longer conversations?

Pronunciation

Are names, numbers, brands, and technical terms pronounced correctly?

Expression

Does the voice respond appropriately to the emotional context?

Multilingual consistency

Does the voice retain its identity when the language changes?

TTS latency

Measure time to first audio, but also measure full voice-agent latency.

Interruption handling

For realtime agents, measure how naturally the system handles:

barge-in

stop speaking

resume

new turn

Telephony quality

Test through your actual phone stack.

Cost

Calculate your expected spend at:

1 million characters

10 million characters

100 million characters

and your expected concurrent calls.

The resulting benchmark will be much more useful than a single “most human TTS” score.

Final words

Deepgram Aura remains an important part of the modern conversational TTS landscape, but its current architecture is different from when Aura was first launched.

Today, Aura-2 is Deepgram’s broadest-language TTS model, covering seven languages, while Flux TTS is the newer streaming-first model designed specifically for English voice agents, with turn management, interruption handling, and cross-turn voice consistency.

Deepgram’s strengths are particularly visible in realtime speech infrastructure, English conversational voice, pronunciation control, telephony-friendly streaming, and tight integration between speech recognition and speech synthesis.

Shunya takes a different route.

Its TTS stack combines global, Asian, and 23 Indic language applications, 46 voices across 23 Indic languages plus English, cross-lingual voice identity, 11 expression styles, short-reference voice cloning, streaming, telephony formats, and a wider voice-AI platform.

For an enterprise whose primary requirement is English-first realtime voice agents, Deepgram’s current Flux architecture deserves serious evaluation.

For an enterprise that needs Indic speech, Asian and multilingual voice experiences, cross-lingual brand identity, telephony, voice cloning, and a broader speech platform, Shunya presents a different production architecture.

The right evaluation should therefore start with your workload.

Take the same:

Text

Language

Voice

Agent

Telephony path

and traffic profile

and test both systems.

Because the best production TTS is not necessarily the voice that sounds most impressive in a demo.

It is the voice that stays natural, accurate, responsive, and reliable when your customers actually start talking to it.

Frequently asked questions

What is Deepgram Aura?

Deepgram Aura is Deepgram’s text-to-speech family originally built for real-time conversational AI agents. The current family includes Aura-2, while Deepgram’s newer Flux TTS is now its recommended English voice-agent model.

Is Shunya an alternative to Deepgram Aura?

Yes. Shunya provides production TTS for global, Asian, and 23 Indic language applications, with 46 documented voices across 23 Indic languages plus English, cross-lingual synthesis, voice cloning, expression styles, streaming, and telephony support.

How many languages does Deepgram Aura-2 support?

Aura-2 currently supports English, Spanish, German, French, Dutch, Italian, and Japanese.

Does Deepgram support Indian languages?

Aura-2’s current listed language set does not include Hindi or other Indic-language TTS. Deepgram’s newer Flux catalogue does include an Indian-accented English voice, Priya, but Indian-accented English is different from Indic-language TTS.

Does Shunya support Indian languages?

Yes. Shunya’s current TTS documentation lists 23 Indic languages and 46 voices, with named male and female voices for each language.

Does Deepgram Aura support pronunciation control?

Yes. Aura-2 provides runtime speed and pronunciation controls in English and Spanish, including IPA-based pronunciation overrides.

Does Shunya support pronunciation control?

Yes. Shunya provides language-aware preprocessing and controls for speed and output behavior, with its Indic stack designed around regional pronunciation and language-specific speech rules.

Does Deepgram support voice cloning?

Voice cloning is not presented as a core Aura-2 capability in Deepgram’s current Aura documentation. Its current TTS documentation primarily focuses on its built-in voice catalog and language models.

Does Shunya support voice cloning?

Yes. Shunya documents 3–6 second reference audio for voice cloning, with cross-lingual synthesis across its supported Indic languages.

Does Deepgram support realtime TTS?

Yes. Deepgram’s current Flux TTS is explicitly designed for realtime voice agents and streams through WebSocket, while Aura-2 remains available through the Aura TTS interfaces.

Does Shunya support realtime TTS?

Yes. Shunya provides WebSocket streaming TTS and documents first audio in under 350 ms for its streaming TTS path.

Does Deepgram support telephony?

Yes. Flux TTS directly supports μ-law and A-law streaming output, while Aura supports compressed and containerized audio formats within the Voice Agent API.

Does Shunya support telephony?

Yes. Shunya supports μ-law and A-law in addition to PCM, WAV, MP3, OGG Opus, and FLAC.

What is Deepgram Aura-2 pricing?

Deepgram currently lists Aura-2 at $0.030 per 1,000 characters, or approximately $30 per million characters on pay-as-you-go pricing.

What is Shunya TTS pricing?

Shunya currently lists Zero TTS at $5.26 per million characters on pay-as-you-go pricing, with volume pricing at $4.74 per million characters and custom Enterprise plans.

Which should enterprises evaluate?

The practical choice depends on your languages, voice-agent architecture, pronunciation requirements, latency, telephony setup, deployment model, and usage volume. Benchmark both against the actual workload rather than relying on generic TTS rankings.