Text to speech (TTS) technology is increasingly popular, giving computers the ability to read text into sound. Among them, Python is one of the programming languages that provides good support for developing TTS applications. Below is how to develop Vietnamese TTS applications using Python.
First, it is necessary to prepare basic resources including a Vietnamese text dataset for training and a set of Vietnamese audio samples covering a variety of phonemes. Vietnamese language processing libraries such as Underthesea and PyVi also need to be installed to support language processing.
Then, we use the gTTS library to synthesize Vietnamese speech from the text. gTTS is based on Google Text-to-Speech API and supports many languages, including Vietnamese. With just a few simple lines of code, we can convert text to mp3 audio in Vietnamese.
To increase processing speed, multi-threading can be used. Besides, integrating Machine Learning and Deep Learning libraries such as NumPy, Tensorflow, Keras,… will help improve the quality of reading voice, making it sound more natural and fluent.
Some applicable techniques include training an LSTM model to predict intonation and pitch; Using neural networks to synthesize sounds to create natural Vietnamese voices. Besides, integrating emotion and context recognition features to adjust appropriate intonation and intensity is also necessary.
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In general, with the strong development of Python in the field of natural language processing and machine learning, building effective Vietnamese TTS applications is completely possible. This may be the direction to develop Vietnamese TTS technology in the future to better support the blind as well as many other smart applications.