RunPod Serverless CPU worker that removes background noise from audio using ffmpeg's RNNoise filter (arnndn), with a built-in FFT denoiser (afftdn) as an automatic fallback.

Contract

Input: {"audio": "<base64>"} — any format/sample rate. A data:...;base64, prefix is stripped automatically.
Output: {"audio": "<base64 WAV>"} — denoised, 48 kHz mono WAV.
Error: {"error": "<message>"}.

Pipeline

decode base64 -> temp input file -> ffmpeg -i in -af arnndn=m=/app/models/rnnoise.rnnn -ar 48000 -ac 1 out.wav -> read -> base64.

Deploy

Dockerfile path: /workers/audio-denoise/Dockerfile
Build context: repo root (docker build -f workers/audio-denoise/Dockerfile .)
GPU: none (CPU-only worker)
App env var to set: RUNPOD_AUDIO_DENOISE_URL (your deployed endpoint URL)

Model

The Dockerfile downloads the beguiling-drafts RNNoise model from GregorR/rnnoise-models to /app/models/rnnoise.rnnn. If that download fails at build time, the handler transparently falls back to ffmpeg's built-in afftdn FFT denoiser (no model file needed).