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Voice-cloning and fine-tuning text-to-speech models
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Video Overview
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Understanding text to speech models
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Text to speech Transformers
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Diffusion networks for text to speech
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Generative Adversarial Networks for Text to Speech
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Controlling style in text to speech models
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StyleTTS2 Text to Speech
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Voice cloning versus fine-tuning
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Dataset preparation tips for voice cloning
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Materials, Code, Scripts
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Dataset preparation for StyleTTS fine-tuning in Colab
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Fine-tuning StyleTTS2 in a Jupyter Notebook
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Text to speech inference and performance
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Understanding losses.
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Voice Cloning performance without fine-tuning
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Dataset and Fine-tuning tips
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Trelis Internships
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Dive into a comprehensive tutorial on fine-tuning text-to-speech models for voice cloning. Explore the fundamentals of text-to-speech technology, including transformers, diffusion networks, and generative adversarial networks. Learn about StyleTTS2, a powerful text-to-speech model, and understand the differences between voice cloning and fine-tuning. Gain practical knowledge on dataset preparation, fine-tuning processes in Colab and Jupyter Notebook, and performance evaluation. Discover tips for improving voice cloning results and understand the importance of loss functions in the training process. This in-depth video also covers materials, code, and scripts needed for implementation, making it an essential resource for those looking to master text-to-speech fine-tuning techniques.

Text-to-Speech Fine-tuning Tutorial - StyleTTS2 Voice Cloning and Model Adaptation

Trelis Research
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