Pin It!

You don’t need deep learning to get deep insights — just smart Diarization.

There was a time I thought labeling who said what in a recording required machine learning models, paid APIs, or overpriced transcription software. Back then, I was working on a grassroots tech education project, capturing voice memos from team meetings in a noisy classroom with one shared mic — chaotic, overlapping speech, and zero budget.

That’s when I stumbled upon the power of diarization — and realized that with Python, a bit of open-source magic, and the right tools, it didn’t have to be complicated. No cloud dependencies. No licensing headaches. Just your terminal, some clean code, and a little patience.

In this tutorial, I’ll walk you through how to perform speaker diarization in Python using a free and powerful library: pyAudioAnalysis. Whether you’re transcribing interviews, organizing podcast episodes, or building your own voice-tagging tool, this guide keeps things practical and privacy-respecting — all powered by FOSS.

Let’s give your audio files the structure they deserve.

---

Table of Contents

Pin It!

Pin It!

• What is Speaker Diarization, and Why Should You Care?
• Getting Started with Speaker Diarization: Installation Primer
  • Requirements
  • Setting Up Your Environment
  • Diarization Script
  • Important Notes:
• Understanding the Sample Output from the Diarization Script
• Use Cases
• Audio Quality and Preprocessing Tips
  • Why Clean Audio Makes Diarization Work — And How to Get It
  • How to Know If Your Diarization Actually Worked
• Make Your Audio Work for You — No Black Boxes Required

Pin It!

---

What is Speaker Diarization, and Why Should You Care?

Speaker diarization is the process of automatically segmenting an audio recording by who is speaking when. Imagine you have a long meeting recording with multiple participants — diarization tells you “Speaker 1 talked here, then Speaker 2 took over, and Speaker 3 chimed in later.” It’s the digital equivalent of putting name tags on voices in a crowded room.

This is distinct from speech-to-text transcription, which focuses on converting spoken words into text but doesn’t tell you who said what. Diarization adds that layer of structure, making transcripts clearer, enabling better indexing, and unlocking new possibilities like speaker-specific sentiment analysis or voice biometrics.

Understanding diarization is critical if you want to analyze conversations, podcasts, interviews, or meetings where multiple voices overlap, especially in settings where manual labeling is impossible or impractical.

· · ─ ·𖥸· ─ · ·

Getting Started with Speaker Diarization: Installation Primer

Before diving into the world of speaker diarization with Python, it’s essential to have your environment set up correctly. The core of this tutorial relies on the open-source pyAudioAnalysis library, a lightweight yet powerful toolkit that handles audio feature extraction and segmentation with ease. Installing this library and its dependencies lays the foundation for running diarization smoothly on your machine—whether you’re using Linux, Windows, or macOS. By preparing your system upfront, you ensure a seamless experience as you move from raw audio to clear, labeled conversations.

Requirements

Before we dive into the code, ensure you have the following installed:

• Python 3.x
• pip (Python package installer)
• Ubuntu 24 LTS (for this article)

You will also need the following libraries:

• pyAudioAnalysis
• numpy
• matplotlib
• scikit-learn
• hmmlearn
• eyed3
• imblearn
• plotly

You can install these dependencies using the following command:

pip install pyAudioAnalysis numpy matplotlib scikit-learn hmmlearn eyed3 imblearn plotly

Setting Up Your Environment

1. Install the Required Libraries: Make sure to install all the necessary libraries mentioned in the Requirements section.
2. Prepare Your Audio File: Choose an audio file that you want to analyze. For demonstration purposes, you can use any file with multiple speakers. Make sure the audio is in a compatible format (WAV, MP3, etc.).

Diarization Script

Here’s a sample Python script that performs diarization using the pyAudioAnalysis library:

from pyAudioAnalysis import audioSegmentation as aS

# Replace 'your_audio_file.wav' with the path to your audio file
audio_file = 'your_audio_file.wav'

# Perform speaker diarization
[flags, classes, centers] = aS.speaker_diarization(audio_file, n_speakers=3)

# Output the segmentation
for i, flag in enumerate(flags):
    print(f"Segment {i}: Speaker {flag}")

Important Notes:

• n_speakers Parameter: Adjust the n_speakers parameter according to the number of speakers in your audio file. If your audio has more than 3 speakers, change this number accordingly.
• Output Interpretation: The script will output the segments along with the speaker identity. This allows you to see which speaker was active during which segments of the audio.

Pin It!

Understanding the Sample Output from the Diarization Script

After processing your audio file, the diarization script produces output that identifies speaker segments with timestamps and speaker IDs. For instance, the output might look like this:

Segment 1: [0.00s - 5.24s] Speaker 0  
Segment 2: [5.24s - 12.88s] Speaker 1  
Segment 3: [12.88s - 19.40s] Speaker 0  
Segment 4: [19.40s - 27.05s] Speaker 2  
Segment 5: [27.05s - 35.78s] Speaker 1  

Each segment shows the start and end time of speech and assigns a speaker label (Speaker 0, Speaker 1, Speaker 2, etc.). These labels don’t represent actual names but uniquely tag distinct voices throughout the audio.

You can use this output to pinpoint when each speaker talks and build transcripts or analytics around speaker turns. Listening to the segments while following these timestamps helps match speaker IDs to real people—a crucial step for projects like interviews, podcasts, or meeting summaries.

This structured output is the foundation for transforming raw, tangled audio into meaningful, searchable conversations—all powered by free, open-source Python tools.

· · ─ ·𖥸· ─ · ·

Use Cases

1. Meeting Transcriptions: Automate the transcription of business meetings by attributing spoken content to specific participants, enhancing the clarity and usability of meeting notes.
2. Podcast Production: Simplify the editing process for podcasts by clearly identifying who is speaking, allowing for more efficient content production and better audience engagement.
3. Research Interviews: Analyze interviews conducted in research studies by differentiating speakers, facilitating a more accurate representation of conversations in the research findings.
4. Voice Analytics: Utilize diarization in customer service settings to analyze customer interactions, improving service quality by understanding customer sentiments and behaviors.

· · ─ ·𖥸· ─ · ·

Audio Quality and Preprocessing Tips

Why Clean Audio Makes Diarization Work — And How to Get It

Not all audio is created equal, and your diarization results will only be as good as the input you feed into the system. Background noise, overlapping speech, low volume, or echo can confuse even the best algorithms.

Here are a few FOSS-friendly tips to get your audio diarization-ready:

• Use noise reduction tools like sox or audacity (both open source) to clean static or hiss.
• Normalize audio levels to ensure consistent volume across speakers, avoiding bias toward louder voices.
• Trim silent parts at the start and end — they can throw off segmentation.
• If possible, use separate audio channels for different microphones (e.g., stereo recordings) — it’s like giving the diarizer spatial clues.
• Keep your recordings in lossless or high-quality formats (WAV, FLAC) instead of compressed MP3s, which lose details important for voice recognition.

By prepping audio thoughtfully, you set yourself and your Python diarization script up for success—making the process smoother, faster, and more reliable.

· · ─ ·𖥸· ─ · ·

How to Know If Your Diarization Actually Worked

After running your Python script, you’ll get segments labeled by speaker — but how do you tell if those labels are correct?

In open-source diarization, perfect accuracy is rare. Here’s how to evaluate your results pragmatically:

• Listen to labeled segments to confirm speakers match the time slots.
• Compare diarization timestamps against a manual transcript if available.
• Calculate simple metrics like Diarization Error Rate (DER) — this measures missed speech, false alarms, and speaker confusion.
• Iterate by tuning parameters or cleaning audio further if the labels seem off.

Remember, diarization is a tool to speed up understanding and indexing of conversations, not a magic bullet. A “good enough” output often saves hours of manual work and unlocks downstream automation in transcription, analysis, or search.

· · ─ ·𖥸· ─ · ·

Make Your Audio Work for You — No Black Boxes Required

Speaker diarization doesn’t need to be a black-box process locked behind paywalls or proprietary tools. With Python and open-source libraries like pyAudioAnalysis, you can build your own workflows, keep full control over your data, and still get professional-grade results.

In this tutorial, you learned how to:

• Use pyAudioAnalysis to break audio into labeled speaker segments
• Apply a simple, transparent workflow that respects your system and your values
• Take your first steps into real-world diarization — without overengineering the process

Want more FOSS-powered, real-world guides like this one?

Subscribe to the DevDigest newsletter — where we turn minimalist tools and open-source code into powerful tech solutions for real-life problems.
👉 samgalope.dev/newsletter

Let’s keep building smarter, freer, and simpler.