Imported from roohe/agentic-super-skills (skills_library/pydub-automation/SKILL.md). Install upstream with npx skills add roohe/agentic-super-skills --skill pydub-automation. Copyright stays with the author (MIT).

PyDub Audio Automation

Automate repetitive audio tasks with Python using PyDub for batch processing, format conversion, normalization, and content assembly.

When to Use This Skill

• Processing large numbers of audio files consistently
• Converting between audio formats at scale
• Normalizing loudness across a batch of files
• Assembling intros/outros automatically to episodes
• Trimming silence or extracting segments programmatically
• Building audio pipelines for content production

Methodology Foundation

Source: PyDub Library (James Robert) + Python Audio Processing

Core Principle: "Audio operations that take hours manually can run in minutes with code." PyDub provides a high-level interface that abstracts FFmpeg's complexity, making common operations accessible to non-audio engineers.

Why This Matters: Content teams producing regular podcasts, courses, or video content spend significant time on repetitive audio tasks. Automation enables consistent quality at scale while freeing humans for creative work.

What Claude Does vs What You Decide

What This Skill Does

1. Batch processes audio files - Apply same operations to hundreds of files
2. Converts formats - MP3, WAV, FLAC, OGG, and more
3. Normalizes loudness - Consistent levels across episodes
4. Assembles content - Concatenate intros, content, outros
5. Extracts segments - Trim, split, and slice audio programmatically

How to Use

Generate Processing Script

Help me write a PyDub script to [describe task].
Input files: [format, location]
Output requirements: [format, specs]

Create Batch Workflow

Create a Python script that processes all audio files in a folder:
- Input: [source folder, file type]
- Operations: [what to do]
- Output: [destination, naming convention]

Debug Audio Script

This PyDub script isn't working as expected:
[paste code]
Expected: [what you want]
Actual: [what's happening]

Instructions

When automating audio with PyDub, follow this methodology:

Step 1: Setup and Prerequisites

## Installation

# Install PyDub
pip install pydub

# FFmpeg is required (PyDub uses it under the hood)
# macOS:
brew install ffmpeg

# Ubuntu/Debian:
sudo apt-get install ffmpeg

# Windows:
# Download from ffmpeg.org, add to PATH

## Basic Imports

from pydub import AudioSegment
from pydub.effects import normalize, compress_dynamic_range
from pydub.silence import detect_silence, split_on_silence
import os
from pathlib import Path

Step 2: Core Operations

## Loading and Saving Audio

# Load audio file (format auto-detected from extension)
audio = AudioSegment.from_file("input.mp3")
audio = AudioSegment.from_file("input.wav", format="wav")

# Save audio file
audio.export("output.mp3", format="mp3", bitrate="192k")
audio.export("output.wav", format="wav")

# Export with metadata
audio.export(
    "output.mp3",
    format="mp3",
    bitrate="192k",
    tags={"artist": "Brand Name", "album": "Podcast"}
)

## Basic Properties

print(f"Duration: {len(audio)} ms")
print(f"Channels: {audio.channels}")
print(f"Frame rate: {audio.frame_rate} Hz")
print(f"Sample width: {audio.sample_width} bytes")
print(f"dBFS: {audio.dBFS}")  # Volume level

Step 3: Volume and Normalization

## Volume Adjustments

# Increase volume by 6 dB
louder = audio + 6

# Decrease volume by 3 dB
quieter = audio - 3

# Normalize to target level (0 dB = maximum)
normalized = normalize(audio)

# Normalize to specific headroom
def normalize_to_target(audio, target_dBFS=-16):
    """Normalize audio to target loudness."""
    change_in_dBFS = target_dBFS - audio.dBFS
    return audio.apply_gain(change_in_dBFS)

normalized = normalize_to_target(audio, target_dBFS=-16)

## Batch Normalization

def normalize_folder(input_dir, output_dir, target_dBFS=-16):
    """Normalize all audio files in a folder."""
    input_path = Path(input_dir)
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)

    for file in input_path.glob("*.mp3"):
        audio = AudioSegment.from_file(file)
        normalized = normalize_to_target(audio, target_dBFS)

        output_file = output_path / file.name
        normalized.export(output_file, format="mp3", bitrate="192k")
        print(f"Processed: {file.name}")

# Usage
normalize_folder("raw_episodes/", "processed_episodes/", target_dBFS=-16)

Step 4: Concatenation and Assembly

## Basic Concatenation

intro = AudioSegment.from_file("intro.mp3")
content = AudioSegment.from_file("episode.mp3")
outro = AudioSegment.from_file("outro.mp3")

# Concatenate (+ operator)
full_episode = intro + content + outro

# Add silence between segments
silence = AudioSegment.silent(duration=2000)  # 2 seconds
full_episode = intro + silence + content + silence + outro

full_episode.export("final_episode.mp3", format="mp3")

## Podcast Assembly Script

def assemble_episode(
    content_file,
    intro_file="assets/intro.mp3",
    outro_file="assets/outro.mp3",
    output_file=None,
    intro_fade_ms=500,
    outro_fade_ms=500
):
    """
    Assemble podcast episode with intro and outro.
    Includes crossfade for professional sound.
    """
    intro = AudioSegment.from_file(intro_file)
    content = AudioSegment.from_file(content_file)
    outro = AudioSegment.from_file(outro_file)

    # Apply fade out to intro, fade in to content
    intro = intro.fade_out(intro_fade_ms)
    content = content.fade_in(intro_fade_ms).fade_out(outro_fade_ms)
    outro = outro.fade_in(outro_fade_ms)

    # Crossfade join
    episode = intro.append(content, crossfade=intro_fade_ms)
    episode = episode.append(outro, crossfade=outro_fade_ms)

    # Generate output filename if not provided
    if output_file is None:
        output_file = content_file.replace(".mp3", "_final.mp3")

    episode.export(output_file, format="mp3", bitrate="192k")
    print(f"Assembled: {output_file} ({len(episode)/1000:.1f}s)")
    return output_file

# Usage
assemble_episode("episode_042_raw.mp3")

Step 5: Trimming and Splitting

## Time-Based Trimming

# Extract segment (milliseconds)
# audio[start:end]
first_30_seconds = audio[:30000]
last_minute = audio[-60000:]
middle_section = audio[60000:120000]

# Remove first 5 seconds (skip intro)
without_intro = audio[5000:]

## Silence-Based Operations

from pydub.silence import detect_silence, split_on_silence

# Detect silence regions
# Returns list of [start, end] in milliseconds
silence_ranges = detect_silence(
    audio,
    min_silence_len=1000,  # Minimum 1 second silence
    silence_thresh=-40     # dB threshold for "silence"
)

# Split on silence (useful for chapter markers)
chunks = split_on_silence(
    audio,
    min_silence_len=500,
    silence_thresh=-40,
    keep_silence=250  # Keep 250ms of silence on each side
)

# Export chunks
for i, chunk in enumerate(chunks):
    chunk.export(f"segment_{i:03d}.mp3", format="mp3")

## Trim Silence from Start/End

def trim_silence(audio, silence_thresh=-50, chunk_size=10):
    """Remove silence from beginning and end of audio."""

    # Find first non-silent moment
    start_trim = 0
    for i in range(0, len(audio), chunk_size):
        if audio[i:i+chunk_size].dBFS > silence_thresh:
            start_trim = max(0, i - 100)  # Keep 100ms before
            break

    # Find last non-silent moment
    end_trim = len(audio)
    for i in range(len(audio), 0, -chunk_size):
        if audio[i-chunk_size:i].dBFS > silence_thresh:
            end_trim = min(len(audio), i + 100)  # Keep 100ms after
            break

    return audio[start_trim:end_trim]

Step 6: Format Conversion

## Batch Format Conversion

def convert_folder(input_dir, output_dir, output_format="mp3", **export_kwargs):
    """
    Convert all audio files to specified format.

    Example kwargs for MP3:
        bitrate="192k"
    Example kwargs for WAV:
        parameters=["-ac", "1"]  # mono
    """
    input_path = Path(input_dir)
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)

    # Supported input formats
    extensions = ["*.mp3", "*.wav", "*.flac", "*.ogg", "*.m4a"]

    for ext in extensions:
        for file in input_path.glob(ext):
            audio = AudioSegment.from_file(file)

            output_file = output_path / f"{file.stem}.{output_format}"
            audio.export(output_file, format=output_format, **export_kwargs)
            print(f"Converted: {file.name} → {output_file.name}")

# Usage: Convert WAVs to MP3
convert_folder("recordings/", "mp3_output/", output_format="mp3", bitrate="192k")

# Usage: Convert to mono WAV
convert_folder("stereo/", "mono/", output_format="wav", parameters=["-ac", "1"])

Step 7: Complete Pipeline Example

## Full Podcast Processing Pipeline

from pydub import AudioSegment
from pydub.effects import normalize
from pathlib import Path
import json
from datetime import datetime

class PodcastProcessor:
    """
    Complete podcast processing pipeline.

    Operations:
    1. Normalize loudness
    2. Trim silence
    3. Add intro/outro
    4. Export with metadata
    """

    def __init__(self, config_file="podcast_config.json"):
        with open(config_file) as f:
            self.config = json.load(f)

        self.intro = AudioSegment.from_file(self.config["intro_file"])
        self.outro = AudioSegment.from_file(self.config["outro_file"])

    def process_episode(self, input_file, episode_number, title):
        """Process a single episode through the full pipeline."""

        print(f"Processing Episode {episode_number}: {title}")

        # 1. Load and normalize
        audio = AudioSegment.from_file(input_file)
        target_db = self.config.get("target_loudness", -16)
        audio = self._normalize_to_target(audio, target_db)
        print(f"  ✓ Normalized to {target_db} dBFS")

        # 2. Trim silence
        audio = self._trim_silence(audio)
        print(f"  ✓ Trimmed silence (duration: {len(audio)/1000:.1f}s)")

        # 3. Add intro/outro with crossfade
        fade_ms = self.config.get("crossfade_ms", 500)
        intro = self.intro.fade_out(fade_ms)
        outro = self.outro.fade_in(fade_ms)
        audio = audio.fade_in(fade_ms).fade_out(fade_ms)

        final = intro.append(audio, crossfade=fade_ms)
        final = final.append(outro, crossfade=fade_ms)
        print(f"  ✓ Added intro/outro (total: {len(final)/1000:.1f}s)")

        # 4. Export with metadata
        output_dir = Path(self.config["output_dir"])
        output_dir.mkdir(exist_ok=True)

        filename = f"episode_{episode_number:03d}.mp3"
        output_path = output_dir / filename

        final.export(
            output_path,
            format="mp3",
            bitrate=self.config.get("bitrate", "192k"),
            tags={
                "title": f"Episode {episode_number}: {title}",
                "artist": self.config["podcast_name"],
                "album": self.config["podcast_name"],
                "track": episode_number,
                "date": datetime.now().strftime("%Y"),
            }
        )
        print(f"  ✓ Exported: {output_path}")

        return output_path

    def _normalize_to_target(self, audio, target_dBFS):
        change = target_dBFS - audio.dBFS
        return audio.apply_gain(change)

    def _trim_silence(self, audio, threshold=-50):
        # Simple trim - just first/last 100ms chunks
        chunk_size = 100
        start = 0
        for i in range(0, min(5000, len(audio)), chunk_size):
            if audio[i:i+chunk_size].dBFS > threshold:
                start = max(0, i - 100)
                break

        end = len(audio)
        for i in range(len(audio), max(0, len(audio)-5000), -chunk_size):
            if audio[i-chunk_size:i].dBFS > threshold:
                end = min(len(audio), i + 100)
                break

        return audio[start:end]


# Example config file (podcast_config.json):
"""
{
    "podcast_name": "Marketing Mastery",
    "intro_file": "assets/intro.mp3",
    "outro_file": "assets/outro.mp3",
    "output_dir": "processed_episodes/",
    "target_loudness": -16,
    "crossfade_ms": 500,
    "bitrate": "192k"
}
"""

# Usage:
# processor = PodcastProcessor("podcast_config.json")
# processor.process_episode("raw/ep42.mp3", 42, "The Power of Audio")

Examples

Example 1: Batch Normalize Podcast Backlog

Context: 50 podcast episodes with inconsistent loudness levels.

Input:

Write a script to normalize all MP3 files in my episodes/ folder to -16 LUFS, keeping the originals as backup.

Output:

#!/usr/bin/env python3
"""
Batch normalize podcast episodes to -16 dBFS.
Creates backups before processing.
"""

from pydub import AudioSegment
from pathlib import Path
import shutil

def batch_normalize(
    input_dir: str,
    target_dBFS: float = -16,
    create_backup: bool = True
):
    """
    Normalize all MP3 files in directory to target loudness.

    Args:
        input_dir: Folder containing MP3 files
        target_dBFS: Target loudness level
        create_backup: If True, saves originals to backup/ folder
    """
    input_path = Path(input_dir)

    # Create backup folder if needed
    if create_backup:
        backup_path = input_path / "backup"
        backup_path.mkdir(exist_ok=True)

    # Find all MP3 files
    mp3_files = list(input_path.glob("*.mp3"))
    print(f"Found {len(mp3_files)} MP3 files")

    for i, file in enumerate(mp3_files, 1):
        print(f"\n[{i}/{len(mp3_files)}] Processing: {file.name}")

        # Backup original
        if create_backup:
            shutil.copy(file, backup_path / file.name)

        # Load and check current level
        audio = AudioSegment.from_file(file)
        original_dBFS = audio.dBFS
        print(f"  Original level: {original_dBFS:.1f} dBFS")

        # Calculate adjustment needed
        adjustment = target_dBFS - original_dBFS

        if abs(adjustment) < 0.5:
            print(f"  Already at target, skipping")
            continue

        # Apply normalization
        normalized = audio.apply_gain(adjustment)

        # Export (overwrite original)
        normalized.export(file, format="mp3", bitrate="192k")
        print(f"  Normalized: {original_dBFS:.1f} → {target_dBFS} dBFS")

    print(f"\n✓ Complete! Processed {len(mp3_files)} files")
    if create_backup:
        print(f"  Backups saved to: {backup_path}")


if __name__ == "__main__":
    import sys

    input_dir = sys.argv[1] if len(sys.argv) > 1 else "episodes/"
    batch_normalize(input_dir, target_dBFS=-16)

Usage:

python normalize_episodes.py episodes/

Example 2: Auto-Add Intro/Outro to New Episodes

Context: Weekly podcast needs intro/outro added to each new recording.

Input:

Create a script that watches a folder for new recordings and automatically adds my standard intro and outro.

Output:

#!/usr/bin/env python3
"""
Auto-assemble podcast episodes when new recordings are added.
Watch 'incoming/' folder, output to 'ready/' folder.
"""

from pydub import AudioSegment
from pathlib import Path
import time

# Configuration
WATCH_DIR = Path("incoming/")
OUTPUT_DIR = Path("ready/")
INTRO_FILE = Path("assets/intro.mp3")
OUTRO_FILE = Path("assets/outro.mp3")
CROSSFADE_MS = 500
TARGET_DBFS = -16

def assemble_episode(input_file: Path) -> Path:
    """Add intro/outro and normalize a podcast episode."""

    print(f"\nProcessing: {input_file.name}")

    # Load audio
    intro = AudioSegment.from_file(INTRO_FILE)
    content = AudioSegment.from_file(input_file)
    outro = AudioSegment.from_file(OUTRO_FILE)

    # Normalize content to target
    adjustment = TARGET_DBFS - content.dBFS
    content = content.apply_gain(adjustment)
    print(f"  Normalized: {content.dBFS:.1f} dBFS")

    # Apply fades
    intro = intro.fade_out(CROSSFADE_MS)
    content = content.fade_in(CROSSFADE_MS).fade_out(CROSSFADE_MS)
    outro = outro.fade_in(CROSSFADE_MS)

    # Assemble with crossfade
    episode = intro.append(content, crossfade=CROSSFADE_MS)
    episode = episode.append(outro, crossfade=CROSSFADE_MS)

    # Export
    output_file = OUTPUT_DIR / input_file.name
    episode.export(output_file, format="mp3", bitrate="192k")
    print(f"  Created: {output_file} ({len(episode)/1000/60:.1f} min)")

    return output_file


def watch_and_process():
    """Watch folder and process new files."""

    WATCH_DIR.mkdir(exist_ok=True)
    OUTPUT_DIR.mkdir(exist_ok=True)

    processed = set()
    print(f"Watching {WATCH_DIR} for new recordings...")
    print(f"Output to: {OUTPUT_DIR}")
    print("Press Ctrl+C to stop\n")

    while True:
        for file in WATCH_DIR.glob("*.mp3"):
            if file.name not in processed:
                try:
                    assemble_episode(file)
                    processed.add(file.name)
                    # Move original to archive
                    archive = WATCH_DIR / "processed"
                    archive.mkdir(exist_ok=True)
                    file.rename(archive / file.name)
                except Exception as e:
                    print(f"  Error: {e}")

        time.sleep(5)  # Check every 5 seconds


if __name__ == "__main__":
    watch_and_process()

Checklists & Templates

PyDub Project Setup

## Requirements

□ Python 3.7+ installed
□ pip install pydub
□ FFmpeg installed and in PATH
□ Test: python -c "from pydub import AudioSegment; print('OK')"

## Project Structure

project/
├── scripts/
│   ├── normalize.py
│   ├── assemble.py
│   └── convert.py
├── assets/
│   ├── intro.mp3
│   └── outro.mp3
├── incoming/      # Raw recordings
├── processed/     # Final output
└── config.json    # Settings

Common Operations Reference

## Quick Reference

# Load
audio = AudioSegment.from_file("file.mp3")

# Save
audio.export("out.mp3", format="mp3", bitrate="192k")

# Volume
louder = audio + 6  # +6 dB
quieter = audio - 3  # -3 dB

# Trim
first_30s = audio[:30000]  # milliseconds
last_min = audio[-60000:]

# Concatenate
combined = audio1 + audio2 + audio3

# Fade
audio = audio.fade_in(500).fade_out(500)

# Crossfade
combined = audio1.append(audio2, crossfade=500)

# Normalize
from pydub.effects import normalize
normalized = normalize(audio)

# Silence
silence = AudioSegment.silent(duration=2000)

# Properties
print(len(audio))  # duration in ms
print(audio.dBFS)  # volume level

Skill Boundaries

What This Skill Does Well

• Structuring audio production workflows
• Providing technical guidance
• Creating quality checklists
• Suggesting creative approaches

What This Skill Cannot Do

• Replace audio engineering expertise
• Make subjective creative decisions
• Access or edit audio files directly
• Guarantee commercial success

References

• PyDub. "GitHub Repository" (jiaaro/pydub) - Library documentation
• GeeksforGeeks. "Create an Audio Editor in Python Using PyDub"
• Real Python. "Working with Audio in Python"
• FFmpeg Documentation - Underlying encoder

Related Skills

• audio-editing - Manual editing fundamentals
• transcription-to-content - Processing transcripts
• podcast-production - Full podcast workflow

Skill Metadata (Internal Use)

name: pydub-automation
category: audio
subcategory: automation
version: 1.0
author: MKTG Skills
source_expert: PyDub Library
source_work: jiaaro/pydub
difficulty: intermediate
estimated_value: Hours saved per batch (5-50 hours depending on scale)
tags: [python, automation, audio, batch-processing, pydub]
created: 2026-01-26
updated: 2026-01-26