seamless-expressive

Maintainer: facebook

Total Score

154

Last updated 4/29/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The seamless-expressive model is composed of two main modules: Prosody UnitY2 and PRETSSEL. Prosody UnitY2 is an expressive speech-to-unit translation model that can transfer phrase-level prosody like speech rate or pauses. PRETSSEL is an expressive unit-to-speech generator that can efficiently disentangle semantic and expressivity components from speech to transfer utterance-level expressivity like the style of one's voice. The model was developed by Facebook researchers and is available on the Hugging Face platform.

The seamless-expressive model is related to other models in the Seamless Communication suite, such as the SeamlessM4T v2 large and SeamlessM4T medium models, which provide high-quality multilingual and multimodal machine translation capabilities.

Model inputs and outputs

Inputs

  • Audio input for speech-to-speech translation (S2ST)

Outputs

  • Translated speech output in the target language

Capabilities

The seamless-expressive model can perform expressive speech-to-speech translation, preserving both semantic content and speaker expressivity. It can transfer phrase-level prosody like speech rate and pauses, as well as utterance-level expressivity like the style of the speaker's voice.

What can I use it for?

The seamless-expressive model could be used to develop applications that enable more natural and engaging multilingual communication, such as virtual assistants, language learning tools, or dubbing/voiceover services. The model's ability to preserve speaker expressivity can make translated speech sound more human-like and emotionally resonant.

Things to try

Experiment with the model's ability to transfer different aspects of expressivity, such as speaking rate, pauses, and vocal style, to see how it impacts the perceived naturalness and quality of the translated speech output. You can also try fine-tuning the model on domain-specific data to optimize its performance for your particular use case.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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