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Convert Your Text Into SpeechUse by Microsoft Edge's online text-to-speech service.Enter your current content.0/5000Select the voice you want to convert.Select a voiceConvert --- TTS Voice ExampleFemaleMaleAdriMicrosoft Adri Online (Natural) - Afrikaans (South Africa)GeneralFriendlyPositiveWillemMicrosoft Willem Online (Natural) - Afrikaans (South Africa)GeneralFriendlyPositiveAnilaMicrosoft Anila Online (Natural) - Albanian (Albania)GeneralFriendlyPositiveIlirMicrosoft Ilir Online (Natural) - Albanian (Albania)GeneralFriendlyPositiveAmehaMicrosoft Ameha Online (Natural) - Amharic (Ethiopia)GeneralFriendlyPositiveMekdesMicrosoft Mekdes Online (Natural) - Amharic (Ethiopia)GeneralFriendlyPositiveAminaMicrosoft Amina Online (Natural) - Arabic (Algeria)GeneralFriendlyPositiveIsmaelMicrosoft Ismael Online (Natural) - Arabic (Algeria)GeneralFriendlyPositiveAliMicrosoft Ali Online (Natural) - Arabic (Bahrain)GeneralFriendlyPositiveLailaMicrosoft Laila Online (Natural) - Arabic (Bahrain)GeneralFriendlyPositiveSalmaMicrosoft Salma Online (Natural) - 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That is, you agree to only use voices whose speakers grant the permission to have their voice cloned, either directly or by license before making synthesized voices public, or you have to publicly announce that these voices are synthesized if you do not have the permission to use these voices.PagerYinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima MesgaraniIn this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to generate the most suitable style for the text without requiring reference speech, achieving efficient latent diffusion while benefiting from the diverse speech synthesis offered by diffusion models. Furthermore, we employ large pre-trained SLMs, such as WavLM, as discriminators with our novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. Moreover, when trained on the LibriTTS dataset, our model outperforms previous publicly available models for zero-shot speaker adaptation. This work achieves the first human-level TTS synthesis on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs.Read More👉 Look At More Audio Samples https://styletts2.github.io/👉 GitHub Repo StyleTTS2👉 Full Online Demo https://huggingface.co
