  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 waveforms from low-dimensional
representation, such as Mel-Spectrograms. In recent years, different approaches
have been introduced to develop such vocoders. However, it becomes more
challenging to assess these new vocoders and compare their performance to
previous ones. To address this problem, we present VocBench, a framework that
benchmark the performance of state-of-the art neural vocoders. VocBench uses a
systematic study to evaluate different neural vocoders in a shared environment
that enables a fair comparison between them. In our experiments, we use the
same setup for datasets, training pipeline, and evaluation metrics for all
neural vocoders. We perform a subjective and objective evaluation to compare
the performance of each vocoder along a different axis. Our results demonstrate
that the framework is capable of showing the competitive efficacy and the
quality of the synthesized samples for each vocoder. VocBench framework is
available at https://github.com/facebookresearch/vocoder-benchmark.
