Audio Normalizer

Normalize audio volume levels using peak or RMS normalization to ensure consistent loudness across files.

Purpose

Volume normalization for:

Podcast episode consistency Music playlist leveling Speech recording standardization Broadcast loudness compliance Features Peak Normalization: Normalize to maximum peak level (dBFS) RMS Normalization: Normalize to average loudness level Loudness Matching: Match LUFS target for broadcast compliance Batch Processing: Normalize multiple files to same level Format Preservation: Maintain original audio format Headroom Control: Prevent clipping with configurable headroom Quick Start from audio_normalizer import AudioNormalizer

Peak normalization to -1 dBFS

normalizer = AudioNormalizer() normalizer.load('input.mp3') normalizer.normalize_peak(target_dbfs=-1.0) normalizer.save('normalized.mp3')

RMS normalization for consistent average loudness

normalizer.normalize_rms(target_dbfs=-20.0) normalizer.save('normalized_rms.mp3')

Batch normalize all files to same level

normalizer.batch_normalize( input_files=['audio1.mp3', 'audio2.mp3'], output_dir='normalized/', method='rms', target_dbfs=-20.0 )

CLI Usage

Peak normalization

python audio_normalizer.py input.mp3 --output normalized.mp3 --method peak --target -1.0

RMS normalization

python audio_normalizer.py input.mp3 --output normalized.mp3 --method rms --target -20.0

Batch normalize directory

python audio_normalizer.py *.mp3 --output-dir normalized/ --method rms --target -20.0

Show current levels without normalizing

python audio_normalizer.py input.mp3 --analyze-only

API Reference AudioNormalizer class AudioNormalizer: def load(self, filepath: str) -> 'AudioNormalizer' def normalize_peak(self, target_dbfs: float = -1.0, headroom: float = 0.1) -> 'AudioNormalizer' def normalize_rms(self, target_dbfs: float = -20.0) -> 'AudioNormalizer' def analyze_levels(self) -> Dict[str, float] def save(self, output: str, format: str = None, bitrate: str = '192k') -> str def batch_normalize(self, input_files: List[str], output_dir: str, method: str = 'rms', target_dbfs: float = -20.0) -> List[str]

Normalization Methods Peak Normalization Scales audio so highest peak reaches target level Preserves dynamic range Good for preventing clipping Target: typically -1.0 to -3.0 dBFS RMS Normalization Scales audio so average level reaches target Better for perceived loudness matching Good for podcasts and speech Target: typically -20.0 to -23.0 dBFS LUFS Matching Integrated Loudness Units relative to Full Scale Broadcast standard (EBU R128, ITU BS.1770) Target: -23 LUFS (broadcast), -16 LUFS (streaming) Best Practices

For Podcasts:

normalizer.normalize_rms(target_dbfs=-19.0) # Speech clarity

For Music:

normalizer.normalize_peak(target_dbfs=-1.0) # Preserve dynamics

For Broadcast:

normalizer.normalize_rms(target_dbfs=-23.0) # EBU R128 compliance

Use Cases Podcast Production: Consistent volume across episodes Music Playlists: Even loudness for continuous playback Audiobooks: Standardized narration levels Conference Recordings: Normalize different speakers Video Production: Match audio levels before mixing Limitations Does not apply dynamic compression (use separate compressor) Does not remove DC offset (pre-processing recommended) Peak normalization won't match perceived loudness Doesn't fix clipped audio (distortion is permanent)