SONAR Sentence Embedding System

A SONAR Sentence Embedding System is a fixed-size sentence embedding system that can be used to create multilingual and multimodal embeddings (that support text-to-text translation, speech-to-text translation, and multilingual similarity search).

• Context:
  • It can provide a comprehensive suite of speech encoders and text decoders for multilingual and multimodal processing.
  • It can process sentences into fixed-size embeddings via text encoders.
  • It can embed speech segments into the same embedding space as text, enabling cross-modality similarity searches.
  • It can handle text-to-text and speech-to-text translations through a unified architecture.
  • It can integrate with pipelines using APIs such as TextToTextModelPipeline and SpeechToEmbeddingModelPipeline.
  • It can support multilingual embeddings across diverse language combinations, including zero-shot transfer.
  • It requires 16kHz sample rate audio input for optimal speech processing.
  • ...
• Example(s):
  • TextToTextModelPipeline, which supports text-to-text translation.
  • SpeechToTextModelPipeline, which enables speech-to-text conversion.
  • SpeechToEmbeddingModelPipeline, which generates fixed-size embeddings for speech input.
  • ...
• Counter-Example(s):
  • LASER3, which lacks multimodal embedding capabilities.
  • LabSE, which is limited in zero-shot modality combinations compared to SONAR.
  • Universal Sentence Encoder, which does not handle cross-modality embeddings.
• See: Multimodal Embeddings, Machine Translation, Speech Processing Systems.

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References

2024

• https://github.com/facebookresearch/SONAR
  • NOTES:
    • Multilingual Embedding Space: SONAR unifies up to 200 different languages into a single 1024-dimensional sentence embedding space, enabling cross-lingual comparisons and translations with minimal overhead.
    • Speech Encoders: Beyond text, SONAR includes aligned speech encoders for over 37 languages, allowing audio input to be mapped into the same embedding space as text.
    • Teacher-Student Training: SONAR leverages a teacher-student method to align speech embeddings with text embeddings, ensuring speech vectors remain comparable to textual sentence representations.
    • Universal Text Decoder: A single text decoder in SONAR converts embeddings back into words in multiple languages, enabling text-to-text translation, speech-to-text translation, and sentence reconstruction.
    • Zero-Shot Transfer: By encoding the semantics of sentences at a universal level, SONAR enables tasks in language pairs never directly trained together, powering zero-shot cross-lingual retrieval and translation.
    • Denoising and Translation Objectives: SONAR’s text encoder-decoder bottleneck is refined through large-scale translation and denoising tasks, which jointly shape robust, language-agnostic sentence embeddings.
    • Cross-Modal Retrieval: Because speech and text share the same semantic space, SONAR supports retrieval scenarios where a user can find matching textual sentences for a given spoken clip—or vice versa.
    • BLASER 2.0: Built on top of SONAR, BLASER 2.0 evaluates machine translation quality by predicting semantic similarity between the source, reference, and hypothesis embeddings across many languages.
    • MuTox: SONAR’s embedding space also supports toxicity classification through MuTox, illustrating how a single universal embedding can feed into specialized downstream models.
    • Open-Source Implementation: Released under MIT and some non-commercial licenses for certain models, SONAR’s repository (with fairseq2 dependencies) offers ready-to-use pipelines for text embedding, speech embedding, translation, and more.

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