Model Card for Kyutai TTS (public data) See also the pre print research paper, the project page, the GitHub repository, and the evaluation pipeline. This is a model for streaming text to speech (TTS). Unlike offline text to speech, where the model needs the entire text to produce the audio, our model starts to output audio as soon as the first few words from the text have been given as input. This model was trained on a mixed of public TTS datasets, allowing for a fair comparisons with other methods. Model Details The model architecture is a hierarchical Transformer that consumes tokenized text and generateds audio tokenized by Mimi, see the Moshi paper. The frame rate is 12.5 Hz and each audio frame is represented by 16 audio tokens. You cannot use less tokens at inference. The backbone model is 300M parameters, and the depth transformer is 450M parameters and uses partial weight sharing similar to Hibiki. The audio is shifted by 16 steps (1.28 sec.) with respect to the text, and the model uses an acoustic/semantic delay of 2. Model Description Kyutai TTS is a decoder only model for streaming speech to text. It leverages the multistream architecture of Moshi to model text stream b…
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