Refactor code structure for improved readability and maintainability

workers/audio-denoise/README.md

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+# Audio Denoise Worker (RNNoise / arnndn)
+
+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).