Omnilingual ASR enables speech recognition across thousands of languages using shared encoders and decoders, combining self-supervised learning with LLMs to reduce costs and improve accessibility. The system scales from low-resource to high-resource languages, offering few-shot expansion, automatic language detection, and balanced training strategies.
Key benefits include:
- Language-Adaptive Encoders: Share speech representations across 1,600+ languages, allowing scarce languages to benefit from abundant ones via self-supervised learning.
- LLM-Decoders: Convert acoustic states into grammatically rich text and manage translations using transformer-based language models.
- Few-Shot Extensibility: Add support for 5,000+ languages with minimal labeled data or in-context prompts, enabling community-driven expansion.
- Integrated Language ID: Auto-detect languages with MMS’s 4,000-language classifier or Whisper’s built-in detection for mixed-language audio.
- Deployment Flexibility: Choose open-source models (Apache-2.0) for self-hosting or leverage cloud APIs (Google, Azure, AWS) with features like diarization and streaming.
Perfect for developers, researchers, and businesses requiring scalable multilingual speech recognition with minimal computational resources and licensing constraints.
