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SeamlessM4T
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Translations (14)

SeamlessM4T

Multilingual speech and text translation made easy.

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Tool Information

SeamlessM4T is a foundational multimodal model for speech translation that enables high-quality translation between different languages. Its primary purpose is to facilitate effortless communication through both speech and text. With the increasing interconnectedness of our world and the abundance of multilingual content available, the ability to understand and communicate in any language is becoming more important than ever.SeamlessM4T supports various translation tasks, including automatic speech recognition for nearly 100 languages, speech-to-text translation for nearly 100 input and output languages, speech-to-speech translation for nearly 100 input languages and 35 output languages (including English), 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 addresses the challenges of limited language coverage and the reliance on separate subsystems by providing a unified multilingual model. It aims to bridge the gap between low and mid-resource languages and high-resource languages, improving performance for both types. Furthermore, SeamlessM4T can implicitly recognize the source languages without the need for a separate language identification model.The development of SeamlessM4T builds upon previous advancements made by Meta and others, such as the creation of the No Language Left Behind (NLLB) machine translation model supporting 200 languages and the Universal Speech Translator for Hokkien, a language without a widely used writing system.SeamlessM4T is built on the multitask UnitY model architecture, which enables the generation of translated text and speech, as well as automatic speech recognition, text-to-text, text-to-speech, speech-to-text, and speech-to-speech translations. It utilizes lightweight and highly composable tools like fairseq2, a PyTorch ecosystem library, to enhance its modeling capabilities.

Pros and Cons

Pros

  • Supports nearly 100 languages
  • Includes speech-to-speech translation
  • Text-to-text and text-to-speech translations
  • Implicit source language recognition
  • Single unified multilingual model
  • Improved performance on high-resource languages
  • Addresses low-resource language limitations
  • Improves mid-resource language translation
  • Built on multitask UnitY model
  • Enhanced by fairseq2 toolkit
  • Supports wide variety of translation tasks
  • Effortless communication through speech and text
  • No need for separate language identification
  • Covers universal speech translator concept
  • Open-source release under CC BY-NC 4.0
  • Released metadata of large translation dataset
  • Unified model for all translation tasks
  • Built using modern PyTorch ecosystem
  • Lightweight
  • easily composable toolkit
  • Direct generation of translated text and speech
  • Automatic speech recognition built in
  • Improved training stability
  • Redesigned fairseq for more efficiency
  • High-quality end-to-end data mining
  • Extensive language and modality coverage
  • SONAR for multilingual similarity search
  • Teacher-student approach for embedding space extension
  • 433
  • 000 hours of speech-text aligned training data
  • State-of-the-art performance across multiple tasks
  • Toxicity and bias management mechanisms
  • Significant toxicity reduction on speech translations
  • Gender bias quantification in translation
  • Improved robustness against background noises
  • Better performance on speaker variations
  • Reduced toxicity and enhanced safety
  • Speech-to-text translation improvements
  • Demonstrates state-of-the-art results
  • Significant improvement for low-resource languages
  • Strong performance on high-resource languages
  • Improved training stability
  • Easily integrable into existing systems

Cons

  • Supports 100 languages not 200
  • Limited speech-to-speech translation languages
  • Dependent on fairseq2
  • Designed for specific UnitY architecture
  • Possible mistranscription and bias
  • Doesn't handle speech-to-speech well
  • Requires text-to-text for accuracy
  • Doesn't handle background noises well
  • May need constant improvements

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Applicable Tasks

speech text translation
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