podcast-splitter

name	podcast-splitter
description	Split audio files by detecting silence gaps. Auto-segment podcasts into chapters, remove long silences, and export individual clips.

Podcast Splitter

Automatically split audio files into segments based on silence detection. Perfect for dividing podcasts into chapters, creating clips from long recordings, or removing dead air.

Quick Start

from scripts.podcast_splitter import PodcastSplitter

# Auto-split by silence
splitter = PodcastSplitter("podcast_episode.mp3")
segments = splitter.split_by_silence()
splitter.export_segments("./chapters/")

# Remove long silences
splitter = PodcastSplitter("raw_recording.mp3")
splitter.remove_silence(min_length=2000)  # Remove silences > 2 seconds
splitter.save("clean_recording.mp3")

Features

• Silence Detection: Configurable threshold and duration
• Auto-Split: Divide audio at natural breaks
• Silence Removal: Remove or shorten long pauses
• Chapter Export: Save individual segments as files
• Preview Mode: List detected silences without splitting
• Batch Processing: Process multiple files

API Reference

Initialization

splitter = PodcastSplitter("audio.mp3")

# With custom settings
splitter = PodcastSplitter(
    "audio.mp3",
    silence_thresh=-40,    # dBFS threshold
    min_silence_len=1000,  # Minimum silence length (ms)
    keep_silence=300       # Silence to keep at segment edges (ms)
)

Silence Detection

# Detect silence regions
silences = splitter.detect_silence()
# Returns: [(start_ms, end_ms), (start_ms, end_ms), ...]

# Print silence summary
splitter.print_silence_report()

Splitting

# Split at all detected silences
segments = splitter.split_by_silence()

# Split at silences longer than threshold
segments = splitter.split_by_silence(min_silence_len=3000)

# Limit number of segments
segments = splitter.split_by_silence(max_segments=10)

Silence Removal

# Remove silences longer than threshold
splitter.remove_silence(min_length=2000)  # Remove >2s silences

# Shorten silences to max length
splitter.shorten_silence(max_length=500)  # Cap at 500ms

# Remove leading/trailing silence only
splitter.strip_silence()

Export

# Export all segments
splitter.export_segments(
    output_dir="./chapters/",
    prefix="chapter",       # chapter_01.mp3, chapter_02.mp3
    format="mp3",
    bitrate=192
)

# Export specific segments
splitter.export_segment(0, "intro.mp3")
splitter.export_segment(3, "conclusion.mp3")

# Save modified audio
splitter.save("output.mp3")

CLI Usage

# Split podcast into chapters
python podcast_splitter.py --input episode.mp3 --output-dir ./chapters/

# Detect and list silences (no splitting)
python podcast_splitter.py --input episode.mp3 --detect-only

# Remove long silences
python podcast_splitter.py --input raw.mp3 --output clean.mp3 --remove-silence 2000

# Custom sensitivity
python podcast_splitter.py --input episode.mp3 --output-dir ./chapters/ \
    --threshold -35 --min-silence 2000 --keep-silence 500

CLI Arguments

Argument	Description	Default
--input	Input audio file	Required
--output	Output file (for silence removal)	-
--output-dir	Output directory for segments	-
--detect-only	Only detect/report silences	False
--threshold	Silence threshold (dBFS)	-40
--min-silence	Minimum silence to detect (ms)	1000
--keep-silence	Silence to keep at edges (ms)	300
--max-segments	Maximum segments to create	None
--remove-silence	Remove silences longer than (ms)	-
--shorten-silence	Cap silence length at (ms)	-
--prefix	Output filename prefix	segment
--format	Output format	mp3
--bitrate	Output bitrate (kbps)	192

Examples

Split Interview into Q&A Segments

splitter = PodcastSplitter(
    "interview.mp3",
    silence_thresh=-35,     # Less sensitive (louder threshold)
    min_silence_len=2000,   # Only split on 2+ second pauses
    keep_silence=400        # Keep some silence for natural feel
)

segments = splitter.split_by_silence()
print(f"Found {len(segments)} segments")

splitter.export_segments("./questions/", prefix="qa")

Remove Dead Air from Recording

splitter = PodcastSplitter("raw_recording.mp3")

# Show what would be removed
splitter.print_silence_report()

# Remove silences longer than 3 seconds
splitter.remove_silence(min_length=3000)

# Cap remaining silences at 1 second
splitter.shorten_silence(max_length=1000)

splitter.save("clean_recording.mp3")

Create Highlight Clips

splitter = PodcastSplitter("episode.mp3")
segments = splitter.split_by_silence(min_silence_len=5000)

# Export only segments longer than 30 seconds
for i, segment in enumerate(segments):
    duration = segment['end'] - segment['start']
    if duration > 30000:  # > 30 seconds
        splitter.export_segment(i, f"highlight_{i+1}.mp3")

Batch Process Episodes

import os
from scripts.podcast_splitter import PodcastSplitter

episodes_dir = "./raw_episodes/"
output_dir = "./processed/"

for filename in os.listdir(episodes_dir):
    if filename.endswith('.mp3'):
        filepath = os.path.join(episodes_dir, filename)
        splitter = PodcastSplitter(filepath)

        # Remove long silences
        splitter.remove_silence(min_length=2000)

        # Save cleaned version
        output_path = os.path.join(output_dir, filename)
        splitter.save(output_path)
        print(f"Processed: {filename}")

Preview Silence Detection

splitter = PodcastSplitter("episode.mp3")

# Get detailed silence info
silences = splitter.detect_silence()

print("Detected Silences:")
for i, (start, end) in enumerate(silences):
    duration = (end - start) / 1000
    start_time = start / 1000
    print(f"  {i+1}. {start_time:.1f}s - {duration:.1f}s silence")

# Print summary
splitter.print_silence_report()

Detection Settings Guide

Audio Type	Threshold	Min Silence	Notes
Quiet studio	-50 dBFS	500ms	Very sensitive
Normal podcast	-40 dBFS	1000ms	Default
Noisy recording	-35 dBFS	1500ms	Less sensitive
Music with breaks	-30 dBFS	2000ms	For spoken breaks

Adjusting Sensitivity

• More splits: Lower threshold (e.g., -50), shorter min_silence
• Fewer splits: Higher threshold (e.g., -30), longer min_silence
• Natural feel: Longer keep_silence (500-1000ms)
• Tight edits: Shorter keep_silence (100-200ms)

Dependencies

pydub>=0.25.0

Note: Requires FFmpeg installed on system.

Limitations

• Works best with speech content (not music)
• Very noisy recordings may need threshold adjustment
• Long files use significant memory
• No automatic chapter naming (manual rename needed)