MMS de Meta – Speech2Text & Text2Speech

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Well, here is some super exciting news from yesterday! Meta's Massively Multilingual Speech project has made impressive progress in enabling machines to recognize and produce speech in over 1,100 languages, covering ten times more languages than any existing speech recognition or speech generation model. It's heartening to see that Meta is working towards breaking down language barriers and enabling people from all over the world to access information and communicate effectively in their preferred language.

The MMS project has addressed a significant challenge in creating a multi-modal system for speech recognition and generation by using Bible translations to create a dataset of readings in over 1,100 languages, combined with unlabeled audio recordings from various religious readings, providing labeled data for 1,100 languages and unlabeled data for nearly 4,000 languages.

What's exciting is that Meta has provided code and models publically so that other researchers can build upon their work and make contributions to the preservation of language diversity. This is an important step towards creating a world where technology can understand and communicate in any language, effectively preserving linguistic diversity. Overall, the Massively Multilingual Speech (MMS) project represents a significant step towards achieving this goal.

The Massively Multilingual Speech (MMS) project is an incredible advancement in the field of artificial intelligence and language technology. The project's ability to recognize and produce speech in over 1,100 languages is revolutionary and has the potential to make a significant impact on how people communicate across the world.

One of the most significant challenges in creating a multi-model system for speech recognition and speech generation is finding the vast amounts of labeled data necessary for training such models. The MMS project overcomes this challenge by using the New Testament translations, providing labeled data for 1,100 languages and unlabeled data for nearly 4,000 languages. The results of this project show that the MMMS models outperform existing models and cover ten times as many languages.

This project has the power to make a significant impact on the future of communication, breaking down the language barriers that have historically existed and encouraging people to preserve their languages. The ability to communicate effectively in any language will positively affect global commerce and cultural exchange.

Moreover, the MMS project provides code and models publicly so that other researchers can build upon their work and make contributions to the preservation of language diversity in the world. The project recognizes the importance of collaboration within the AI community to ensure that AI technologies are developed responsibly, which is crucial for promoting trust and avoiding unintended consequences.

The MMS project represents a significant step towards achieving the goal of creating a world where technology can understand and communicate in any language, effectively preserving linguistic diversity. It holds the potential to change the way the world thinks about cross-linguistic communication. With the advancements of technology, the future is looking bright for communication across languages and cultures.

In conclusion, the MMS project signifies a fantastic achievement in the field of artificial intelligence and language technology. It will undoubtedly impact the future by breaking down language barriers and promoting linguistic diversity. It is exciting to think about the potential positive implications of this project and other advancements in AI technology in facilitating an inclusive global conversation.

Source:https://ai.facebook.com/blog/multilingual-model-speech-recognition/?utm_source=twitter&utm_medium=organic_social&utm_campaign=blog&utm_content=card

Source: Github: https://github.com/facebookresearch/fairseq/tree/main/examples/mms

Source Meta Video: https://twitter.com/i/status/1660722199395704852

Source: FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
https://arxiv.org/abs/2205.12446

Source: Robust Speech Recognition via Large-Scale Weak Supervision: https://arxiv.org/abs/2212.04356