podcast-splitter

Original：🇺🇸 English
Translated
1 scriptsChecked / no sensitive code detected

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

2installs
Sourcedkyazzentwatwa/chatgpt-skills
Added on

NPX Install

npx skill4agent add dkyazzentwatwa/chatgpt-skills podcast-splitter

Tags

Translated version includes tags in frontmatter
audio-splittingpodcast-processingsilence-detectionpython-toolcli-utility

SKILL.md Content

View Translation Comparison →

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

python
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

python
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

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

# Print silence summary
splitter.print_silence_report()

Splitting

python
# 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

python
# 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

python
# 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

bash
# 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

ArgumentDescriptionDefault
--input
Input audio fileRequired
--output
Output file (for silence removal)-
--output-dir
Output directory for segments-
--detect-only
Only detect/report silencesFalse
--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 createNone
--remove-silence
Remove silences longer than (ms)-
--shorten-silence
Cap silence length at (ms)-
--prefix
Output filename prefixsegment
--format
Output formatmp3
--bitrate
Output bitrate (kbps)192

Examples

Split Interview into Q&A Segments

python
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

python
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

python
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

python
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

python
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 TypeThresholdMin SilenceNotes
Quiet studio-50 dBFS500msVery sensitive
Normal podcast-40 dBFS1000msDefault
Noisy recording-35 dBFS1500msLess sensitive
Music with breaks-30 dBFS2000msFor 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)