Auphonic vs DeepFilterNet

Auphonic is an automated audio post-production web service for podcasts, broadcasts, radio, interviews, audiobooks, lectures and other audio or video projects. Its algorithms can reduce noise and reverb, adjust levels, filter and enhance recordings, meet loudness specifications, and remove filler words, coughs and silence. Multitrack processing creates mixdowns with automatic ducking, noise gates and mic-bleed removal. Auphonic also provides multilingual speech-to-text, automatic shownotes and chapters, and a shareable transcript editor. It supports common audio and video formats, waveform audiograms, metadata, chapters and burned-in styled subtitles. Integrations include storage, publishing, automation and speech-recognition services; workflows can use the API, CLI, watch folders and batch productions. The free plan is free and includes two hours per month, but free accounts do not include multilingual speech recognition, automatic shownotes and chapters, watch folders or batch productions. Free multitrack productions must be under 20 minutes. Billing has a three-minute minimum per production, and productions are deleted after 21 days for audio and API work or seven days for video.

DeepFilterNet is a free, open-source framework for speech enhancement on full-band 48 kHz audio, using deep filtering. Its command-line tool suppresses noise in WAV files, with 48 kHz as its only supported sampling rate. A LADSPA plugin provides real-time noise suppression and can be used with PipeWire filter chains to create a virtual microphone or audio output, including for VoIP apps such as Zoom or Discord. The plugin's minimum latency is 20 ms from STFT processing, plus any delay added by the LADSPA host. The Rust command-line tool defaults to DeepFilterNet2; the Python interface documents DeepFilterNet and DeepFilterNet2. An optional post-filter may slightly over-attenuate very noisy sections. The framework supports Linux, macOS and Windows, though training is tested only under Linux. The Python package installs as deepfilternet, and an optional train extra adds data-loading functionality for Linux training. Code is available under either the MIT License or Apache License 2.0.