SEAMLESSEXPRESSIVELM represents an advanced decoder-only language model designed for style transferred speech-to-speech translation. It employs speech tokenizers like HuBERT for extracting semantic units and EnCodec for fine-grained acoustic features, utilizing both to enhance the translation process. The model architecture includes an embedding layer that vectorizes speech tokens from semantic and acoustic streams. Training involves using acoustic prompts derived from semantically aligned data to ensure effective style transfer while avoiding simple copy-paste mechanisms in the generated outputs.
In SEAMLESSEXPRESSIVELM, we optimize speech-to-speech translation by integrating semantic and multi-codebook acoustic units to enhance style transfer and translation accuracy.
Employing HuBERT for semantic unit extraction and EnCodec for fine-grained acoustic information, we leverage both for effective speech representation in our model.
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