styletts2
Maintainer: adirik
4.2K
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
styletts2 is a text-to-speech (TTS) model developed by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, and Nima Mesgarani. It leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. Unlike its predecessor, styletts2 models styles as a latent random variable through diffusion models, allowing it to generate the most suitable style for the text without requiring reference speech. It also employs large pre-trained SLMs, such as WavLM, as discriminators with a novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness.
Model inputs and outputs
styletts2 takes in text and generates high-quality speech audio. The model inputs and outputs are as follows:
Inputs
- Text: The text to be converted to speech.
- Beta: A parameter that determines the prosody of the generated speech, with lower values sampling style based on previous or reference speech and higher values sampling more from the text.
- Alpha: A parameter that determines the timbre of the generated speech, with lower values sampling style based on previous or reference speech and higher values sampling more from the text.
- Reference: An optional reference speech audio to copy the style from.
- Diffusion Steps: The number of diffusion steps to use in the generation process, with higher values resulting in better quality but longer generation time.
- Embedding Scale: A scaling factor for the text embedding, which can be used to produce more pronounced emotion in the generated speech.
Outputs
- Audio: The generated speech audio in the form of a URI.
Capabilities
styletts2 is capable of generating human-level TTS synthesis on both single-speaker and multi-speaker datasets. It surpasses human recordings on the LJSpeech dataset and matches human performance on the VCTK dataset. When trained on the LibriTTS dataset, styletts2 also outperforms previous publicly available models for zero-shot speaker adaptation.
What can I use it for?
styletts2 can be used for a variety of applications that require high-quality text-to-speech generation, such as audiobook production, voice assistants, language learning tools, and more. The ability to control the prosody and timbre of the generated speech, as well as the option to use reference audio, makes styletts2 a versatile tool for creating personalized and expressive speech output.
Things to try
One interesting aspect of styletts2 is its ability to perform zero-shot speaker adaptation on the LibriTTS dataset. This means that the model can generate speech in the style of speakers it has not been explicitly trained on, by leveraging the diverse speech synthesis offered by the diffusion model. Developers could explore the limits of this zero-shot adaptation and experiment with fine-tuning the model on new speakers to further improve the quality and diversity of the generated speech.
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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