GitHub

Seamless Communication Project

What is the project about?

The project, "Seamless Communication," is a family of AI models focused on enabling more natural and authentic communication across different languages. It aims to break down language barriers through advanced machine translation.

What problem does it solve?

The project addresses the limitations of traditional machine translation systems, which often struggle with:

  • Multilinguality: Supporting a wide range of languages.
  • Modality: Handling both speech and text input/output.
  • Expressiveness: Preserving nuances like tone, style, speech rate, and pauses.
  • Real-time translation: Providing low-latency, streaming translation for simultaneous conversations.

What are the features of the project?

  • SeamlessM4T: A foundational, massively multilingual and multimodal machine translation model supporting nearly 100 languages for speech and text. It performs:
    • Speech-to-speech translation (S2ST)
    • Speech-to-text translation (S2TT)
    • Text-to-speech translation (T2ST)
    • Text-to-text translation (T2TT)
    • Automatic speech recognition (ASR)
  • SeamlessM4T v2: An updated version with improved quality and inference latency.
  • SeamlessExpressive: Focuses on preserving prosodic elements (speech rate, pauses, style) in speech-to-speech translation.
  • SeamlessStreaming: Enables real-time, simultaneous translation and streaming ASR for around 100 languages.
  • Seamless: A unified model combining SeamlessExpressive and SeamlessStreaming for expressive, real-time translations.
  • W2v-BERT 2.0 speech encoder: A conformer-based speech encoder.
  • unity.cpp: GGML implementation for running SeamlessM4T models.
  • Expressive Datasets: mExpresso and mDRAL.
  • SeamlessAlignExpressive: Metadata for expressive speech alignment.
  • Libraries: fairseq2, SONAR and BLASER 2.0, stopes, SimulEval.

What are the technologies used in the project?

  • AI/Machine Learning: Deep learning models, specifically transformer-based architectures (UnitY2).
  • Programming Languages: Primarily Python.
  • Frameworks/Libraries: fairseq2 (sequence modeling), PyTorch, Hugging Face Transformers, Gradio (for demos), stopes (mining), SimulEval (simultaneous translation evaluation), GGML (C tensor library).
  • Models: Conformer-based W2v-BERT 2.0 speech encoder.

What are the benefits of the project?

  • Improved Communication: Facilitates more natural and effective communication across language barriers.
  • High-Quality Translation: Delivers accurate translations for both speech and text.
  • Expressiveness: Captures nuances of speech, leading to more engaging and understandable translations.
  • Real-time Capability: Enables simultaneous translation for live conversations and streaming applications.
  • Open-Source: Provides access to models, code, and datasets, fostering research and development.
  • Multilingual: Supports a large number of languages.
  • Multimodal: Works with both speech and text.

What are the use cases of the project?

  • Real-time translation: Live conversations, meetings, presentations, and broadcasts.
  • Accessibility: Assisting individuals with hearing or speech impairments.
  • Content Creation: Dubbing, subtitling, and translating multimedia content.
  • Education: Language learning and cross-cultural communication.
  • Customer Service: Multilingual support for global businesses.
  • Research: Advancing the field of machine translation and speech processing.
  • On-device translation: Running translation models on mobile devices.
seamless_communication screenshot