voxlevel 0.1.0

Normalize WAV voice recordings to a consistent target dB level using AGC, VAD, and limiting

voxlevel

Normalize WAV voice recordings so all speakers sound at the same volume level (-6 dB by default), regardless of their distance from the microphone.

Handles real-world scenarios: background noise, wind, echo, multiple speakers, and speakers moving during recording.

Installation

pip install voxlevel

Usage

Python API

import voxlevel

# From a WAV file
voxlevel.normalize("input.wav", "output.wav")

# From a numpy array
import numpy as np
result = voxlevel.normalize(audio_array, sample_rate=16000)

# With custom parameters
voxlevel.normalize(
    "input.wav",
    "output.wav",
    target_db=-6.0,
    max_gain_db=30.0,
    rms_window_ms=400.0,
    smooth_window_ms=200.0,
)

CLI

# Single file
voxlevel input.wav -o output.wav

# Batch processing
voxlevel *.wav -o normalized/

# Custom target level
voxlevel input.wav -o output.wav --target-db -3.0

How it works

voxlevel uses a two-pass offline approach (not real-time compression):

1. Preprocessing -- DC removal + 80 Hz high-pass filter to cut wind noise, handling noise, and plosives
2. Voice Activity Detection -- Silero-VAD (ONNX) identifies speech vs. silence segments
3. Automatic Gain Control -- Sliding RMS envelope computes the gain needed at each sample to reach the target level, with interpolation across silence gaps and bidirectional smoothing
4. Lookahead limiter -- 5 ms lookahead prevents peaks from exceeding the target, reducing transient distortion compared to brick-wall clipping

The two-pass design means gain is correct from sample 0 -- no lag or adaptation artifacts that real-time compressors exhibit.

Constraints

• 16-bit mono WAV at 8 kHz or 16 kHz
• Offline processing only (no streaming)

License

MIT