Meta Omnilingual ASR: Run 1,600+ Languages Locally

Meta has released an omnilingual automatic speech recognition model that can transcribe speech in over 1,600 languages, including about 500 that were not covered by previous AI systems. I installed it locally and tested it across multiple languages to understand how it works in practice, its requirements, and how to run it on your own machine.

This article walks through the model’s purpose, architecture, installation, and local inference. It also covers resource usage, language coverage, and how the interface behaves during transcription.

What Is Omnilingual ASR?

Omnilingual ASR is a suite of models designed to make speech technology accessible across the globe, with particular attention to low-resource languages. It uses self-supervised learning to extract rich multilingual representations from raw audio and supports transcriptions in 1,600+ languages.

The approach is community-driven and built to scale. It includes a large speech encoder and two decoder variants to handle alignment and in-context transcription.

Omnilingual ASR Overview

Key Features of Omnilingual ASR

• Wide language coverage with support for 1,600+ languages, including hundreds of previously uncovered ones.
• Strong performance for low-resource languages due to self-supervised pretraining.
• Two-stage architecture with a powerful speech encoder and interchangeable decoders.
• Local installation and inference, enabling private and offline testing.
• Extensible to new languages and dialects.

Why Omnilingual ASR Matters

Many languages lack labeled datasets and tools for speech technology. This model aims to close that gap by learning directly from raw audio at scale and making transcription possible for a broad range of languages and dialects. The design focuses on inclusivity and scalability, allowing further expansion and community-based contributions.

System Setup and Requirements

I ran the model on an Ubuntu system with a single NVIDIA RTX 6000 GPU featuring 48 GB of VRAM. During inference, the loaded model consumed close to 18 GB of VRAM. The first run downloaded approximately 29 GB of model data.

A virtual environment is optional but recommended to keep dependencies isolated.

Suggested environment

• OS: Ubuntu (or a comparable Linux system)
• GPU: NVIDIA with sufficient VRAM (observed usage ~18 GB when loaded)
• Storage: At least 35–40 GB free for model files and cache
• Python: Recent Python 3.x
• Optional: Virtual environment for dependency management

Omnilingual ASR Architecture Overview

Two-Stage Design

• Encoder: The core of the system is an omnilingual wav2vec 2 speech encoder scaled to around 7 billion parameters. It creates detailed multilingual representations from raw speech input.
• Decoders:
  • CTC decoder for direct alignment of audio to text.
  • Transformer-based decoder for in-context learning and adaptive transcription.

This design allows flexible behavior during transcription while preserving accuracy across diverse languages.

Coverage and Extensibility

• The current release supports over 1,600 languages.
• The framework allows adding new languages, including dialects.
• In principle, it aims to expand toward thousands of languages, with community contributions playing a role.

Omnilingual ASR Installation

Follow these steps to install the omnilingual ASR package and prepare for local inference.

1) Prepare a virtual environment (optional)

• Create and activate a virtual environment to keep dependencies contained.

Example:

python -m venv .venv
source .venv/bin/activate

2) Install the package

• Install the omnilingual ASR package via pip.

Example:

pip install omnilingual-asr

This step fetches the required Python dependencies. The main model weights are downloaded later at first run.

3) Run the application

• Use a simple application script to start the local interface. A minimal app can wrap the model with a Gradio interface that accepts audio uploads.

Example:

python app.py

On first launch, the model weights (about 29 GB) will be downloaded automatically.

Running Locally

Launch and access

• After starting the app, the interface runs on localhost (for example at port 7860).
• The UI allows uploading or recording audio, selecting the language, and running transcription.

First-run download

• Expect an initial download of roughly 29 GB for model weights.
• Once loaded, the model remains in memory, and subsequent transcriptions are quicker.

Resource use

• VRAM consumption observed: around 18 GB with the model fully loaded.
• Ensure adequate GPU memory and storage to avoid runtime errors.

Using the Interface

Typical workflow

• Select a language from the list.
• Upload an audio file or record audio directly in the browser.
• Run transcription and review the output.

The interface supports multilingual transcription across a broad selection of languages and dialects. Language selection helps guide decoding and improves transcription quality.

Notes on performance

• The transcription quality appeared strong across different languages tested.
• Initial runs after startup may take longer due to model initialization.

Language Tests and Observations

I transcribed a set of multilingual audio files to gauge behavior. The tests included Arabic, Bulgarian, Chinese, Croatian, Czech, Danish, Dutch, German, Indonesian, Hindi, and Urdu. Based on visual inspection and partial language familiarity, the transcriptions looked accurate and consistent.

The Urdu test highlighted the presence of multiple dialect options. Selecting the correct dialect can improve results, especially for languages with strong regional variations.

While the GPU was fully engaged, the interface remained responsive after the initial load. Once the model was in memory, consecutive transcriptions proceeded more quickly.

Practical Tips

• Use a recent NVIDIA GPU with enough VRAM. The observed consumption was close to 18 GB during inference.
• Allocate sufficient disk space to accommodate model weights and any temporary cache files.
• Keep audio inputs clean and clear. Proper language selection can improve accuracy.
• If you plan to work with specific dialects, check for available variants in the language menu.

Troubleshooting Basics

• If the first run stalls, check available disk space and network connectivity for the model download.
• If you encounter out-of-memory errors, close other GPU-intensive applications and reattempt.
• For CPU-only environments, performance and feasibility will vary; a GPU is recommended.

Adding Your Own Language

The framework is designed to be community-driven and supports expansion to new languages and dialects. While the detailed process was not covered here, the system’s architecture and configuration options indicate that custom language entries can be added, provided you have representative audio data and the correct settings.

Security and Privacy

Running the model locally keeps audio data within your environment. This is useful for testing, prototyping, and workflows that require privacy or offline processing.

Performance Summary

• Model loading: Requires downloading around 29 GB on first start.
• GPU memory: Observed around 18 GB VRAM usage when fully loaded.
• Language coverage: Supports over 1,600 languages and offers multiple dialects in several cases.
• Interface: Simple upload/record, language selection, and transcription workflow.

FAQ

How many languages are supported?

Over 1,600 languages are supported at release, including hundreds not previously covered by AI systems. The framework supports further expansion.

What hardware do I need?

A modern NVIDIA GPU with ample VRAM is recommended. In practice, around 18 GB of VRAM was used during inference, and the model weights require about 29 GB of disk space on first download.

Can I run it offline?

Yes, after the initial model download, you can run it locally without sending audio to external services.

What decoders are included?

Two decoder options are available:

• CTC decoder for alignment-focused transcription.
• Transformer-based decoder for in-context learning and adaptability.

Can I add custom languages or dialects?

Yes, the system is meant to be scalable and community-driven. It supports adding languages and dialects with the appropriate configuration and data.

Omnilingual ASR Installation Recap

: Quick install checklist

• Python environment ready (virtual environment recommended).
• pip install of the omnilingual ASR package completed.
• Sufficient disk space for the first model download.
• GPU drivers and CUDA set up if using an NVIDIA GPU.

: Commands (example)

• Create and activate venv:
  • python -m venv .venv
  • source .venv/bin/activate
• Install:
  • pip install omnilingual-asr
• Run:
  • python app.py

After setup

• Access the UI at localhost (for example, port 7860).
• Select language, upload audio, transcribe, and review output.

Architecture Recap

Encoder

• Omnilingual wav2vec 2 model (~7B parameters).
• Learns directly from raw audio, building multilingual speech representations.

Decoders

• CTC decoder for direct alignment between audio and text.
• Transformer-based decoder for context-aware transcription behavior.

Coverage

• 1,600+ languages supported at release.
• Expandable to new languages and dialects.

Key Takeaways

Strengths

• Broad language coverage, including low-resource languages.
• Local setup for privacy and control.
• Flexible decoding strategies for different transcription needs.

Requirements

• Initial model download of about 29 GB.
• Around 18 GB of GPU VRAM for smooth inference in tests.
• Clean audio and correct language selection for best results.

Next steps

• Explore adding custom languages and dialects.
• Integrate the ASR with your processing pipeline via the local interface.
• Benchmark transcription quality on your own datasets.

Conclusion

Omnilingual ASR offers wide multilingual coverage with a design that supports local inference and expansion to new languages. The installation was straightforward, the interface was easy to operate, and the transcription results across several languages looked accurate. With a large encoder and flexible decoders, it balances coverage with practical usability, making it a strong option for multilingual transcription work, especially for languages that previously lacked robust tools.