Custom Voice Cloner

Usharani K, Nandha kumaran H, Nikhilesh Pranav M.S, Nithish kumar K.K, Prasanna Krishna A.S
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Abstract

The Custom Voice Cloner is based on voice signal speech synthesizer. It is a technology that converts text into audible speech, simulating human speech characteristics like pitch and tone. It finds applications in virtual assistants, navigation systems, and accessibility tools. Building one in Python typically involves Text-to-Speech (TTS) libraries such as gTTS, pyttsx3, or platform-specific options for Windows and macOS, offering easy text-to-speech conversion.However, TTS libraries might lack customization and voice quality needed for advanced projects. For more sophisticated applications, custom voice synthesizers can be built using deep learning techniques like Tacotron and WaveNet. These models learn speech nuances for more natural output.Creating a custom voice synthesizer is challenging, requiring high-quality training data, machine learning expertise, and substantial computational resources. It goes beyond generating speech to convey emotions and nuances in pronunciation for natural and expressive voices. Key Word: Voice signal speech synthesizer,text-to-speech conversion, deep learning,TTS, gTTS, pyttsx3,etc.
自定义语音克隆器
自定义语音克隆器基于语音信号语音合成器。它是一种将文本转换为可听语音的技术,可模拟人的语音特征,如音高和音调。它可应用于虚拟助手、导航系统和无障碍工具。在 Python 中构建语音合成器通常需要使用文本到语音(TTS)库,如 gTTS、pyttsx3 或针对 Windows 和 macOS 平台的特定选项,这些库提供了简单的文本到语音转换功能。对于更复杂的应用,可以使用 Tacotron 和 WaveNet 等深度学习技术构建自定义语音合成器。创建定制语音合成器具有挑战性,需要高质量的训练数据、机器学习专业知识和大量计算资源。它不仅能生成语音,还能传达情感和发音上的细微差别,从而发出自然而富有表现力的声音。关键字语音信号语音合成器、文本到语音转换、深度学习、TTS、gTTS、pyttsx3 等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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