Omnilingual ASR - Multilingual Speech Recognition Framework from Meta

What is Omnilingual ASR?

Omnilingual ASR is a multilingual speech recognition framework from Meta, covering 1600+ languages, with 781 TP3T language character error rate lower than 101 TP3T. its 7 billion parameter wav2vec 2.0 encoder combined with CTC and Transformer decoder supports zero-sample transcription of unseen languages, and only a few samples are needed to adapt to the new Language. The model is open-source and contains a corpus of 350 low-resource languages, which promotes the digitization of endangered languages and the universalization of speech technology worldwide.

Features of Omnilingual ASR

• multilingual coverage: Supports more than 1,600 languages, covering a wide range of low-resource and endangered languages, significantly improving the global language coverage of speech recognition.
• Low Resource Language Support: Through self-supervised learning and data enhancement techniques, it effectively solves the problem of sparse data in low-resource languages and reduces the threshold of speech recognition.
• Zero sample learning capability: The ability to transcribe new languages with only a small number of examples, without the need for a large corpus, greatly expands language coverage.
• High Performance ArchitectureThe wav2vec 2.0 encoder combined with CTC and Transformer decoder supports high accuracy and high performance speech recognition.
• Open Source and Collaboration: Open source models and datasets to promote global developers and researchers to work together to advance speech recognition technology and help endangered language preservation.

Core Benefits of Omnilingual ASR

• Extensive language coverage: Supports over 1,600 languages, including a large number of low-resource and endangered languages, significantly improving global language coverage for speech recognition.
• Zero sample learning capability: Transcribing unseen languages with only a few audio and text samples dramatically reduces the cost of developing new languages.
• High Performance Architecture: A 7 billion parameter wav2vec 2.0 encoder and an advanced decoder are used, combined with self-supervised learning, to achieve high-precision speech recognition.
• Open Source and Community Support: Open source of models and datasets to promote the participation of developers and researchers around the world to advance technology development and language preservation.
• Innovative data enhancement technology: Solve the problem of sparse low-resource linguistic data through techniques such as synthesized speech to improve the generalization ability of the model.
• Flexible decoder selection: Provides both CTC and Transformer decoder options to meet the performance and efficiency needs of different scenarios.

What is Omnilingual ASR's official website?

• Project website:: https://ai.meta.com/blog/omnilingual-asr-advancing-automatic-speech-recognition/
• GitHub repository:: https://github.com/facebookresearch/omnilingual-asr
• HuggingFace Model Library:: https://huggingface.co/datasets/facebook/omnilingual-asr-corpus
• Technical Papers:: https://ai.meta.com/research/publications/omnilingual-asr-open-source-multilingual-speech-recognition-for-1600-languages/

Who Omnilingual ASR is for

• language researcher: It can be used to study low-resource and endangered languages and help language preservation and linguistic research.
• Technology Developer: Suitable for developing speech recognition applications that take advantage of its open source nature for secondary development and integration.
• content creator: Facilitate the production of multilingual audio and video content, enabling fast transcription and subtitle generation.
• educator: To help develop multilingual educational resources to support language teaching and intercultural communication.
• business user: Suitable for enterprises that require multi-language speech recognition services, such as customer service, meeting recording and other scenarios.
• Community and non-profit organizations: Can be used to support linguistic diversity programs and to promote cultural exchange and language preservation.