SeamlessM4T is a groundbreaking multimodal model designed to facilitate high-quality speech translation across different languages, enabling seamless communication through speech and text.
With our world becoming increasingly interconnected and multilingual content abundant, the necessity to comprehend and communicate in various languages has never been more crucial. SeamlessM4T is equipped to handle a myriad of translation tasks, including automatic speech recognition for nearly 100 languages, speech-to-text translation for nearly 100 input and output languages, and speech-to-speech translation for nearly 100 input languages and 35 output languages (including English). Additionally, it offers text-to-text translation for nearly 100 languages and text-to-speech translation for nearly 100 input languages and 35 output languages (including English).
Unlike existing systems that only cover a fraction of the world’s languages, SeamlessM4T revolutionizes translation by offering a unified multilingual model. By addressing the challenges of limited language coverage and the reliance on separate subsystems, it aims to bridge the gap between low and mid-resource languages and high-resource languages, thereby improving performance across the board.
Furthermore, SeamlessM4T boasts the capability to implicitly recognize source languages without requiring a separate language identification model. Its development builds upon previous advancements, including Meta’s creation of the No Language Left Behind (NLLB) machine translation model, supporting 200 languages, and the Universal Speech Translator for Hokkien, a language lacking a widely used writing system.
Built on the multitask UnitY model architecture, SeamlessM4T enables the generation of translated text and speech, automatic speech recognition, text-to-text, text-to-speech, speech-to-text, and speech-to-speech translations. Leveraging lightweight and highly composable tools like fairseq2 from the PyTorch ecosystem, it enhances its modeling capabilities to provide efficient and accurate translations.
