What is WaveVAE?
WaveVAE is a type of generative audio model that can be used to enhance text-to-speech systems. It uses a VAE-based model and can be trained from scratch by optimizing the encoder and decoder. The encoder represents the ground truth audio data as a latent representation, while the decoder predicts future audio frames
How Does WaveVAE Work?
WaveVAE uses a Gaussian autoregressive WaveNet for its encoder. This means that it maps the ground truth audio data into a latent representation. The decoder uses a one-step ahead prediction from an inverse autoregressive flow to predict how the audio waveform will evolve in the future
What is the Purpose of Joint Optimization?
The encoder and decoder in WaveVAE are jointly optimized to ensure that the generated audio output corresponds to the mel spectrogram conditioner. This is important for improving the overall functionality of text-to-speech systems
What Is the Significance of the ELBO?
The ELBO is used as a training objective by WaveVAE. It helps optimize the parameters of the encoder and decoder so that they can generate accurate audio output. The ELBO is a measure of how well the VAE can learn from the observed data
WaveVAE is a powerful tool that can be used to enhance text-to-speech systems. It uses a VAE-based model that can be jointly optimized to ensure that it generates accurate audio output. Its use of an autoregressive WaveNet for the encoder and one-step ahead prediction from an inverse autoregressive flow for the decoder make it a powerful tool in the field of audio generation and processing
