> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/facebookresearch/omnilingual-asr/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Open-source multilingual speech recognition for 1600+ languages Header image with a collage of on-the-ground photos from the transcription gathering efforts in Pakistan and Liberia ## Welcome to Omnilingual ASR Omnilingual ASR is an open-source speech recognition system supporting over **1,600 languages** — including hundreds never previously covered by any ASR technology. Designed for broad accessibility, it enables new languages to be added with just a few paired examples without requiring specialized expertise or large datasets. By combining scalable zero-shot learning with a flexible model family, Omnilingual ASR aims to make speech technology more inclusive and adaptable for communities and researchers worldwide. Get transcribing audio in minutes with simple Python code Install via pip or uv and set up your environment Explore 1600+ languages with language ID format reference Learn about W2V, CTC, and LLM model families ## Key Features State-of-the-art performance across 1,600+ languages with character error rates (CER) below 10% for 78% of those languages. Includes hundreds of languages never previously covered by ASR technology. Choose from three model architectures: * **CTC Models**: Fast parallel generation (up to 96x real-time) * **LLM Models**: Language-conditioned transcription with optional context * **Zero-Shot Models**: Transcribe new languages with just a few examples Available in 300M, 1B, 3B, and 7B parameter variants to balance accuracy and computational resources. Add support for new languages with just a few paired audio-text examples using the zero-shot model variant. ## Performance Highlights Performance results table Our 7B-LLM-ASR system achieves state-of-the-art performance across 1,600+ languages, with character error rates (CER) below 10 for 78% of those languages. Per-language CER results and training hours can be found in the [per\_language\_results\_table\_7B\_llm\_asr.csv](https://github.com/facebookresearch/omnilingual-asr/blob/main/per_language_results_table_7B_llm_asr.csv) file. ## Model Architecture Overview Omnilingual ASR provides three distinct model families: ### SSL (Self-Supervised Learning) Models Pre-trained wav2vec2 encoders that learn speech representations without transcriptions. These serve as the foundation for both CTC and LLM models. ### CTC (Connectionist Temporal Classification) Models Fast, parallel transcription models ideal for real-time applications: * **Real-Time Factor**: 0.001-0.006 (16x to 96x faster than real-time) * **Use Case**: High-throughput batch processing * **Limitation**: No language conditioning support ### LLM (Language Model) Models Autoregressive models with optional language conditioning: * **Real-Time Factor**: \~0.09 (approximately real-time) * **Language Conditioning**: Specify target language for better accuracy * **Unlimited Variants**: Support for audio of any length * **Zero-Shot Variant**: Learn new languages from context examples ## December 2025 Update We released two major improvements: 1. **Improved v2 Models**: Enhanced accuracy (CER) for both CTC and LLM-ASR models 2. **Unlimited Audio Length**: New LLM variant supporting transcription of unlimited-length audio (`omniASR_LLM_Unlimited_{300M,1B,3B,7B}_v2`) Unlimited audio length models have comparable accuracy to limited-length models, however finetuning recipes for these models are currently not supported. ## Resources Read the full technical paper Learn about the research and development Try the interactive demo Access the multilingual corpus ## Next Steps Follow the [installation guide](/installation) to set up Omnilingual ASR in your environment. Try the [quick start guide](/quickstart) to transcribe your first audio file. Learn about [language conditioning](/advanced/language-conditioning) and [zero-shot learning](/models/zero-shot). ## License Omnilingual ASR code and models are released under the [Apache 2.0 License](https://github.com/facebookresearch/omnilingual-asr/blob/main/LICENSE).