{"schema_version":1,"metadata":{"branch":"current","release":"8.0","architecture":"amd64","generated_at":"2026-10-21T02:36:06.049247+00:00","source_url":"https://cdn.openbsd.org/pub/OpenBSD/snapshots/packages/amd64/sqlports-7.55.tgz","source_sha256":"a9ce8ec55a8fe364fc54ad95f17013a9932c665a6e50e55f31cee56f1d69cccc","package_count":12123,"source_kind":"sqlports"},"package":{"name":"rnnoise-0.2p0","path":"audio/rnnoise","url":"/packages/current/audio/rnnoise/","comment":"recurrent neural network for audio noise reduction","homepage":"https://jmvalin.ca/demo/rnnoise","maintainer":"Klemens Nanni <kn@openbsd.org>","description":"RNNoise is a noise suppression library based on a recurrent neural network.\nA description of the algorithm is provided in the following paper:\n\nJ.-M. Valin, A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech\nEnhancement, Proceedings of IEEE Multimedia Signal Processing (MMSP) Workshop,\narXiv:1709.08243, 2018.\nhttps://arxiv.org/pdf/1709.08243\n\nWhile it is meant to be used as a library, the code for a simple command-line\ntool is provided as an example. It operates on RAW 16-bit (machine endian) mono\nPCM files sampled at 48 kHz.\n","description_source":null,"description_unresolved":[],"package_architecture":"amd64","stem":"rnnoise","readme":null,"dependencies":[{"path":"devel/metaauto","type":"build","package_spec":"","url":"/packages/current/devel/metaauto/"},{"path":"devel/autoconf/2.71","type":"build","package_spec":"","url":"/packages/current/devel/autoconf/2.71/"},{"path":"devel/automake/1.16","type":"build","package_spec":"","url":"/packages/current/devel/automake/1.16/"},{"path":"devel/libtool","type":"build","package_spec":"","url":"/packages/current/devel/libtool/"}],"reverse_dependencies":{"count":4,"url":"/packages/data/46/46290126d5ef0f90ddc2355c687d2612174a7626b1015c8310ec4b0e7094e42d.json"},"categories":["audio"],"flavors":[],"only_for_architectures":[],"not_for_architectures":[]}}