I just rewrote the WaveGlow vocoder in tensorflow trying to improve its performance but it fails (1.7 sec generated / sec with the pytorch model drops down to less than 1.2 generated / sec in ...
Abstract: While transformers demonstrate outstanding performance across various audio tasks, their application to neural vocoders remains challenging. Neural vocoders require the generation of long ...
Abstract: Neural vocoders, used for converting the spectral representations of an audio signal to the waveforms, are a commonly used component in speech synthesis pipelines. It focuses on synthesizing ...
Recent advancements in speech synthesis have leveraged GAN-based networks like HiFi-GAN and BigVGAN to produce high-fidelity waveforms from mel-spectrograms. However, these networks are ...
Recent advances in speech enhancement (SE) have moved beyond traditional mask or signal prediction methods, turning instead to pre-trained audio models for richer, more transferable features. These ...
The development of neural networks and their constantly increasing popularity have led to substantial improvements in speech synthesis technologies. The majority of speech synthesis systems use a ...
This is achieved through the use of the latest state-of-the-art voice coding technology called Tri-Wave Excited Linear Prediction, developed by the digital voice coding experts at DSP Innovations.
Many new implementations of vocoder algorithms are designed to provide higher-quality voice at lower data rates. However, the sensitivity and transmitter quality of a mobile handset affects vocoder ...
1-24 — elektor january 1980 elektor vocoder (1) an absolute first! After all the articles describing the theory of vocoders, there must be a lot of enthusiastic readers just itching to build one.
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