Zero STT: The Complete Guide to Speech-to-Text

What Is Zero STT?

Zero STT is Shunya Labs’ speech-to-text model family for real-time and batch transcription. It converts spoken audio into text and supports 216+ languages through its universal model, alongside specialized models for Indian languages, code-switched speech and healthcare.

Speech-to-text is often treated as a simple conversion:

Audio → Text

In production, it is much more complicated.

A speech recognition system needs to understand different accents, background noise, multiple speakers, incomplete sentences, domain-specific vocabulary, and people who switch languages while speaking.

For a voice agent, there is another constraint: the transcription needs to arrive while the person is still talking.

That’s where real-time ASR becomes different from ordinary transcription.

Zero STT is designed around this problem. It provides both batch and streaming ASR through the same model family, with streaming designed for live transcription, voice agents, IVRs, and other applications where latency matters.

Why Real-Time Speech-to-Text Matters

Imagine calling a voice agent and saying:

“I want to change my delivery date.”

If the speech recognition system waits until the entire call segment is finished before producing text, the agent cannot respond quickly.

A real-time system starts processing audio as it arrives.

Customer speaks
       ↓
Audio stream
       ↓
Speech recognition
       ↓
Partial transcript
       ↓
Intent detection
       ↓
AI response
       ↓
Text-to-speech
       ↓
Customer hears response

The goal is not simply accurate transcription. It is accurate transcription with low enough latency to keep a conversation natural.

That distinction matters for applications such as:

• AI voice agents
• Live captions
• Contact centers
• Real-time meeting transcription
• Voice-enabled applications
• Call monitoring
• Interactive IVRs
• Speech translation

How Zero STT Works

At a high level, Zero STT takes an audio signal and produces text.

But a production ASR system has several stages.

1. Audio Input

Audio can come from a microphone, phone call, uploaded recording, meeting, video, or another application.

For live applications, the audio is streamed continuously rather than uploaded as one complete file.

2. Speech Recognition

The model identifies speech patterns and converts them into words.

This is where factors such as accent, pronunciation, background noise, and language affect accuracy.

3. Language Detection

For multilingual applications, the system needs to determine which language is being spoken.

Zero STT Universal supports 216+ languages and can automatically detect the input language, or developers can provide a language code to lock the expected language.

4. Streaming Output

Instead of waiting for the complete recording, streaming ASR returns partial results as speech arrives.

This allows downstream systems to start processing the conversation sooner.

5. Intelligence Layer

Speech recognition can also serve as the first layer of a broader voice intelligence system.

Shunya’s ASR stack adds capabilities such as diarization, intent detection, sentiment analysis, summarization, redaction, and other post-transcription intelligence.

Zero STT vs Batch Speech-to-Text

Not every application needs real-time transcription.

The right architecture depends on when you need the transcript.

Requirement	Streaming ASR	Batch ASR
Live voice agent	✓
Live captions	✓
Real-time translation	✓
Phone conversations	✓
Meeting recording		✓
Podcast transcription		✓
Archived call analysis		✓
Large audio files		✓

The underlying Zero STT models can be used through both approaches. Shunya provides separate streaming and batch endpoints for these workflows.

What Makes Zero STT Different?

1. 216+ Languages Through One Universal Model

Zero STT Universal supports 216+ languages and dialects spanning multiple language families. The model is designed for conversational speech rather than only controlled, clean recordings.

This matters for global products.

Instead of maintaining separate speech providers for different markets, organizations can use a common API layer while supporting a wide range of languages.

For enterprises operating across Asia, however, language count is only part of the problem.

Language depth matters too.

A model can claim hundreds of languages while performing well on only a small subset.

That’s why Shunya also maintains specialized models for Indian languages and code-switched speech.

Zero STT Indic: Speech Recognition Built for Indian Languages

India presents a unique challenge for speech recognition.

People speak across dozens of languages and dialects, often switching between them during the same conversation.

Zero STT Indic is designed specifically for Indian speech and supports 55+ Indian languages, including languages such as Hindi, Bengali, Tamil, Telugu, Kannada, Marathi, Malayalam, Gujarati, Punjabi, Odia, Assamese, Bhojpuri, Maithili, Haryanvi, and others.

This makes it particularly relevant for:

• Indian customer support
• BFSI
• Healthcare
• Government services
• Contact centers
• Regional media
• Voice agents
• Field operations

For Indian deployments, testing the model on actual customer audio is still essential. Published benchmarks provide a useful starting point, but production audio can introduce accents, background noise, telephony compression, and domain vocabulary that standard benchmarks do not capture.

Zero STT Codeswitch: Understanding Hinglish and Mixed-Language Speech

One of the biggest problems with conventional speech recognition is code-switching.

A customer might say:

“Mera order kal deliver hona tha, can you check the status?”

The speaker has not changed languages deliberately. This is simply how multilingual conversations happen.

Zero STT Codeswitch is designed to recognize mixed-language speech and return native mixed-language transcripts. Shunya currently highlights Hinglish support for this model.

This is particularly important in India because English is frequently mixed with Hindi and other regional languages in everyday conversations.

For voice AI, preserving this mixed-language context can improve downstream intent detection and response generation.

Zero STT Med: Speech Recognition for Healthcare

Healthcare introduces another problem: domain vocabulary.

A generic ASR system may recognize everyday speech well but struggle with:

• Medical terminology
• Drug names
• Diagnoses
• Procedures
• Clinical abbreviations
• Dosages
• Multiple speakers
• Noisy clinical environments

Shunya’s Zero STT Med is a specialized model designed for medical transcription and healthcare terminology. The model is part of the current Zero STT family and supports healthcare-oriented workflows.

This makes specialized ASR useful for applications such as:

• Clinical documentation
• Medical transcription
• Healthcare call centers
• Patient conversations
• Medical voice assistants

Zero STT On-Device Models

Not every speech application should send audio to the cloud.

Kiosks, vehicles, mobile applications, industrial systems, and air-gapped environments may require speech recognition to run locally.

Shunya’s Zero Tinny ONNX models are designed for lightweight, on-device transcription and can run on CPU-based infrastructure.

This creates another deployment option:

Cloud → Private VPC → On-Premises → Edge

The choice depends on the application’s requirements for latency, connectivity, privacy, and data sovereignty.

How Accurate Is Zero STT?

Accuracy in speech recognition is commonly measured using Word Error Rate (WER).

Lower WER means fewer transcription errors.

Shunya’s published OpenASR benchmark reports a 3.10% composite WER for Zero STT Universal across eight datasets. Individual datasets include LibriSpeech, TED-LIUM, SPGISpeech, AMI, Earnings22, GigaSpeech, and VoxPopuli. With an average RTFx of 146.23.

RTFx measures how quickly audio can be processed relative to its duration. An RTFx of 100 means one second of audio can be processed in about 10 milliseconds under the benchmark conditions.

But benchmark accuracy should not be treated as a guarantee for every production environment.

The most useful test is always:

Your audio + your languages + your domain + your deployment conditions.

WER Isn’t the Only Metric That Matters

A speech model can have an excellent WER and still perform poorly in a voice agent.

For real-time applications, enterprises should also measure:

• First partial latency
• Final transcript latency
• Real-time factor
• Language accuracy
• Code-switch accuracy
• Numerical accuracy
• Domain terminology
• Speaker diarization
• Noise robustness
• Endpointing
• API reliability

For example, a model that produces a highly accurate transcript two seconds after someone finishes speaking may be less useful for a live voice agent than a slightly less accurate model that starts producing reliable partial results immediately.

This is why accuracy and latency need to be evaluated together.

Who Should Use Zero STT?

Zero STT is designed for organizations building speech-enabled products or applications where language coverage, accuracy, latency, or deployment flexibility matters.

Common use cases include:

Contact Centers

Transcribe live calls, power voice agents, and analyze customer conversations.

Healthcare

Create clinical transcripts and support medical voice workflows.

BFSI

Process customer calls, collections conversations, onboarding calls, and numerical information.

Media

Generate captions and transcripts across languages.

Meetings

Transcribe multi-speaker conversations and extract post-call intelligence.

Voice Agents

Use streaming transcription as the input layer for real-time conversational AI.

Which Zero STT Model Should You Choose?

The current Zero STT family is designed around different production requirements.

Model	Best for
Zero STT Universal	General-purpose speech across 216+ languages
Zero STT Indic	Indian languages and regional speech
Zero STT Codeswitch	Hinglish and mixed-language conversations
Zero STT Med	Healthcare and clinical speech
Zero Tinny ONNX	Lightweight, on-device speech recognition

The best model isn’t necessarily the one with the lowest benchmark WER.

It’s the one that performs best on your actual workload.

Cloud, On-Premises, or Edge?

Enterprise speech deployments increasingly need more than a cloud API.

Shunya supports multiple deployment approaches, including cloud, on-premises, and CPU-compatible environments.

Cloud

Best for:

• Rapid development
• Variable workloads
• Teams that don’t want to manage infrastructure

Private Infrastructure

Best for:

• Sensitive customer data
• Enterprise compliance
• Data residency requirements

On-Premises

Best for:

• Strict data sovereignty
• Air-gapped environments
• Government and regulated organizations

Edge

Best for:

• Low-connectivity environments
• Connected devices
• Kiosks
• Vehicles
• Low-latency applications

The important part is that deployment shouldn’t force you to completely change your speech stack.

Building a Voice AI System With Zero STT

Speech recognition is only one part of a complete voice system.

A modern voice application may look like this:

                 CUSTOMER
                    ↓
              Zero STT
                    ↓
        Intent / Entity Detection
                    ↓
             SLM / LLM
                    ↓
        Knowledge + Business APIs
                    ↓
              Zero TTS
                    ↓
              CUSTOMER

Zero STT provides the speech input layer. Other Shunya components can handle reasoning, knowledge, speech generation, translation, and voice agent orchestration.

This makes STT more than a transcription API. It becomes the first layer of an enterprise voice intelligence stack.

Why Real-Time Speech-to-Text Is Becoming Infrastructure

Voice interfaces are moving beyond simple transcription.

A modern enterprise voice system needs to listen, understand, act, and respond within a single conversation.

That means the speech recognition layer has to be:

Fast. Accurate. Multilingual. Domain-aware. Deployable.

Zero STT is built around that requirement, with a universal model for broad language coverage and specialized models for Indian languages, code-switching, healthcare, numerical speech, and edge deployments.

For enterprises, the question is no longer simply:

Can AI transcribe speech?

It is:

Can speech recognition work accurately enough, quickly enough, and securely enough for the conversations that matter to our business?

That’s the standard production speech AI needs to meet.

Frequently Asked Questions

What is Zero STT?

Zero STT is Shunya Labs’ speech recognition model family for converting speech into text. It supports streaming and batch transcription and includes models for general-purpose, Indic, code-switched, medical, numerical, and on-device use cases.

How many languages does Zero STT support?

Zero STT Universal currently supports 216+ languages, while Zero STT Indic supports 55+ Indian languages.

What is the difference between Zero STT and Zero STT Indic?

Zero STT Universal is designed for broad global language coverage. Zero STT Indic is specifically optimized for Indian languages and dialects.

Does Zero STT support real-time transcription?

Yes. Zero STT supports streaming ASR for live transcription, voice agents, and IVRs, in addition to batch transcription for completed audio files.

What is WER in speech recognition?

Word Error Rate measures how many word-level errors occur in a transcript. Lower WER generally indicates better transcription accuracy.

Can Zero STT handle Hinglish?

Yes. Zero STT Codeswitch is designed for mixed-language speech and currently supports native Hinglish transcription.

Can Zero STT run on-premises?

Shunya supports cloud, on-premises, and CPU-compatible deployment options, depending on the model and deployment configuration.

Final Takeaway

Real-time speech-to-text is no longer just about turning an audio recording into a transcript.

For modern voice applications, ASR needs to operate as a real-time intelligence layer. It needs to recognize speech as people naturally speak it, across languages, accents, domains, and noisy environments, while delivering results quickly enough for a conversation to continue without awkward pauses.

Zero STT takes this approach through a family of models rather than a one-model-fits-all system.

Zero STT Universal provides broad multilingual coverage. Zero STT Indic focuses on Indian languages. Zero STT Codeswitch handles mixed-language conversations. Zero STT Med addresses healthcare terminology. Zero Tinny ONNX brings speech recognition to lightweight edge environments.

For developers and enterprises building the next generation of voice applications, that flexibility matters.

Speech is becoming an interface. The quality of the interface starts with what the system hears.