phonepod 0.1.0b2

Local AI audio restoration. Phone recording → podcast quality.

phonepod

Local AI audio restoration. Phone recording → podcast quality.

Zero cloud. Zero uploads. Everything runs on your machine.

phonepod transforms noisy voice memos into broadcast-ready audio. It combines neural noise suppression (DeepFilterNet3 + MossFormer2) with a subtractive DSP mastering chain - all running locally on CPU. No cloud, no uploads, no subscription.

Status: 0.1.0-beta.1 - works well, API may change. Feedback welcome.

Before / After

Audio demos coming soon — record on your phone, run phonepod, hear the difference.

Install

pip install phonepod

Requires Python 3.11+ and ffmpeg (brew install ffmpeg on macOS).

Usage

CLI (simplest)

phonepod recording.m4a podcast.wav

Python API

import phonepod

# One-liner: file in, file out
phonepod.enhance("recording.m4a", "podcast.wav")

# Advanced: tensor-level control
engine = phonepod.Engine()
enhanced_tensor, sample_rate = engine.enhance(audio_tensor, input_sr)

Web UI

pip install phonepod[ui]
python -m phonepod.app
# Opens at http://localhost:7860

What it does

Stage	Model / Tool	What it does
1	DeepFilterNet3	Neural noise suppression - removes background noise
2	MossFormer2 (48kHz)	Speech enhancement - fills frequencies phones can't capture
3	Pedalboard DSP	Subtractive mastering - gate, HPF, EQ cuts (mud/box/nasal), 2x compression, de-ess
4	Pedalboard Reverb	Optional studio room ambience
5	pyloudnorm	Loudness normalization to -18 LUFS (podcast standard)
6	Limiter + ceiling	Prevents clipping at -1.5 dB ceiling

Subtractive philosophy: all EQ moves are cuts, not boosts. Remove mud (200Hz), boxiness (500Hz), nasal honk (1500Hz), and harshness (6500Hz). The ML models already shaped the frequency balance - cuts work with them, boosts fight them.

Processing a 2-minute recording takes ~7 seconds on Apple Silicon.

How it started

phonepod began as a personal problem: voice memos recorded on a phone sound terrible in a podcast. The AI models that exist are research demos, not products. Professional mastering chains exist but don't denoise. Nothing combines both into a single, local pipeline.

So I built it. The full build story — from first prototype to production pipeline, every dead end and breakthrough — is in JOURNEY.md.

Architecture

Input (any format)
  -> ffmpeg -> 48kHz mono WAV
  -> Stage 1: DeepFilterNet3 (noise suppression)
  -> Stage 2: MossFormer2_SE_48K (speech enhancement)
  -> Stage 3: Pedalboard mastering (gate -> HPF -> mud/box/nasal cuts -> 2x compression -> de-ess)
  -> Stage 4: Reverb (subtle room ambience, optional)
  -> Stage 5: LUFS normalization (-18 LUFS)
  -> Stage 6: Limiter + hard ceiling (-1.5 dB)
Output: podcast-quality 48kHz WAV

Hard boundaries: the engine never touches the filesystem. The processor never touches the model. The CLI never touches tensors.

Development

# Clone and setup
git clone https://github.com/vedantggwp/phonepod.git
cd phonepod
uv sync

# Run tests (fast unit tests only)
uv run pytest -m "not slow"

# Run full test suite (loads ML models, ~30s)
uv run pytest

# Run on a file
uv run phonepod recording.m4a output.wav

License

MIT