Using ONNX for Exporting Models: Enabling Offline ASR Systems

Introduction

Automatic Speech Recognition (ASR) has become a core component in many modern applications. While cloud-based ASR services are widely used, there is a growing need for offline-first solutions—systems that can run entirely on local devices without internet connectivity.

At eKidz, we build AI voice communication layers for children—enabling real-time, speech-based learning and play through research-driven ASR. Our goal is simple but ambitious: to ensure every child’s voice is understood, making education more inclusive, scalable, and responsive.

Working closely with education partners across the U.S., we see firsthand that privacy, latency, and deployment costs are not abstract concerns—they are fundamental constraints. These challenges shape every technical decision we make. In this context, our Head of AI, Yaroslav Nedashkovskiy, explores a promising approach to bridging the gap between high-performance ASR models and real-world deployment: using ONNX to enable efficient, offline speech recognition systems.

Offline ASR is especially important in contexts where:

Privacy is critical (e.g. children’s speech)
Connectivity is unreliable or unavailable (classroom environment)
Latency must be minimized
Operational costs of cloud inference are undesirable However, deploying ASR models outside of their training environment introduces practical challenges. Models are typically developed in frameworks that are not optimized for running in offline mode on the edge devices. This creates a gap between experimentation and real-world usage.

ONNX (Open Neural Network Exchange) addresses this gap by providing a standardized way to export and run machine learning models across different platforms and environments.

What ONNX Is and Why It Matters

ONNX is an open model format designed to make machine learning models portable and interoperable. Instead of being tied to a specific framework such as PyTorch or TensorFlow, a model can be exported into ONNX and then executed using a runtime optimized for inference.

In the context of ASR, this means:

A model trained in a development environment can be deployed in production without rewriting it
The same model can run on servers, edge devices, or mobile platforms
Inference becomes independent of the original training stack This separation between training and inference is particularly valuable for systems that must operate reliably in constrained environments.

ASR Systems in Offline Environments

A typical ASR system consists of several components:

Audio preprocessing, which transforms raw audio into a format suitable for modeling
Acoustic modeling, where a neural network interprets audio features
Decoding, which converts model outputs into readable text Modern ASR models—such as those used in large-scale systems like Whisper and Wav2Vec 2.0—are often based on deep learning architectures like transformers. In offline settings, these models must run efficiently on local hardware, often with limited compute and memory resources. This makes deployment optimization a critical concern.

Why ONNX Is Well-Suited for Offline ASR

1. Portability Across Platforms

ONNX allows a model to be deployed across different environments without modification. This is especially useful when:

The model is trained in Python but deployed in a non-Python environment
The inference system runs on embedded hardware or edge devices
Multiple platforms need to share the same model

2. Performance Optimization

ONNX models can be executed using ONNX Runtime, which is designed specifically for high-performance inference. It applies internal optimizations such as:

Efficient execution planning
Reduction of redundant computations
Support for hardware acceleration These optimizations help reduce both latency and resource consumption, which is critical for offline ASR systems.

3. Lightweight deployment

Unlike full training frameworks, ONNX-based deployments do not require heavy dependencies. This results in:

Smaller deployment packages
Simplified system environments
Easier distribution to edge devices

4. Deterministic behaviour

ONNX execution tends to be more stable and predictable across environments. This consistency is important when deploying systems that must behave reliably without human intervention.

Limitations and trade-offs

Despite its strengths, ONNX is not a complete solution for all aspects of ASR deployment:

Some advanced operations may not be fully supported
Debugging exported models can be more difficult than working in native frameworks
Streaming and real-time decoding often require additional engineering outside ONNX
End-to-end pipelines may need hybrid approaches combining ONNX with custom components These trade-offs are important to consider when designing a production system.

Conclusion

ONNX plays a crucial role in bridging the gap between backend/online deployment and offline ASR systems. By providing a standardized, portable, and optimized model format, it enables developers to run speech recognition models efficiently across a wide range of environments. In offline scenarios where privacy, reliability, and performance are critical, ONNX offers a practical foundation for building robust ASR solutions. While it does not eliminate all deployment challenges, it significantly simplifies the process of bringing modern speech models into production. In essence, ONNX transforms ASR models from local artifacts into deployable, cross-platform components—making offline speech recognition more accessible and scalable.

At eKidz, we work closely with partners to navigate these trade-offs and design ASR systems that truly fit their real-world constraints—from privacy-first architectures to low-latency, cost-efficient deployments. If you’re exploring how to bring speech recognition into your product, let’s connect and find the right ASR pipeline for your specific use case and requirements.

Yaroslav Nedashkovskiy
Head of AI at eKidz