{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vocbench-a-neural-vocoder-benchmark-for","title":"VocBench: A Neural Vocoder Benchmark for Speech Synthesis","arxiv_id":"2112.03099","date":"2021-12-06","proceeding":null,"authors":["Ehab A. AlBadawy","Andrew Gibiansky","Qing He","JiLong Wu","Ming-Ching Chang","Siwei Lyu"],"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 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.","url_abs":"https://arxiv.org/abs/2112.03099v1","url_pdf":"https://arxiv.org/pdf/2112.03099v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vocbench-a-neural-vocoder-benchmark-for","repo_url":"https://github.com/facebookresearch/vocoder-benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-causal-convolution","method_name":"Dilated Causal Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"film-module","method_name":"FiLM Module"},{"method_slug":"feature-matching","method_name":"GAN Feature Matching"},{"method_slug":"gan-hinge-loss","method_name":"GAN Hinge Loss"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"melgan","method_name":"MelGAN"},{"method_slug":"melgan-residual-block","method_name":"MelGAN Residual Block"},{"method_slug":"mixture-of-logistic-distributions","method_name":"Mixture of Logistic Distributions"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"wavegrad","method_name":"WaveGrad"},{"method_slug":"wavegrad-dblock","method_name":"WaveGrad DBlock"},{"method_slug":"wavegrad-ublock","method_name":"WaveGrad UBlock"},{"method_slug":"wavenet","method_name":"WaveNet"},{"method_slug":"wavernn","method_name":"WaveRNN"},{"method_slug":"weight-normalization","method_name":"Weight Normalization"},{"method_slug":"window-based-discriminator","method_name":"Window-based Discriminator"}],"datasets_introduced":[{"slug":"vocbench","name":"VocBench","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.03099","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}