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•21 min read•BrassTranscripts Team

Whisper Speaker Diarization: Complete Guide + Tutorial [2025]

Speaker IdentificationAi TranscriptionTranscribe Audio To Text

OpenAI's Whisper is remarkable at transcription—but it doesn't identify speakers. You get a perfect transcript of what was said, but not who said it. If you're transcribing meetings, interviews, or podcasts, this is a dealbreaker.

The good news? You can add speaker diarization to Whisper using open-source tools. This tutorial shows you exactly how, with complete Python code, troubleshooting tips, and an honest comparison to professional services like BrassTranscripts.

What you'll learn:

  • How to combine Whisper with Pyannote for speaker diarization
  • Step-by-step Python tutorial with working code
  • Pros and cons of DIY vs. professional services
  • When each approach makes sense
  • Common troubleshooting solutions

Who this is for: Developers and technical users comfortable with Python and command-line tools. If you're not technical, skip to the comparison section to see if a professional service makes more sense.

Time investment: 2-4 hours for initial setup, plus processing time for each file.

Quick Navigation

What is OpenAI Whisper?

OpenAI Whisper is an open-source automatic speech recognition (ASR) model released in September 2022. It's become the go-to choice for developers needing transcription capabilities.

What Whisper Does Well

Excellent transcription accuracy: Whisper achieves professional-grade transcription quality across diverse audio conditions.

Multilingual support: Handles 99+ languages, from English to Mandarin to Arabic.

Robust to real-world audio: Works well with accents, background noise, and varied audio quality—better than many commercial alternatives.

Punctuation and capitalization: Automatically adds proper punctuation and formatting, making transcripts readable without manual editing.

Free and open-source: No usage fees, no API limits, complete control over deployment.

Multiple model sizes: Choose between speed (tiny, base) and accuracy (large-v2, large-v3) based on your needs.

What Whisper Doesn't Do

❌ No speaker diarization: Whisper produces a continuous transcript without indicating who spoke when.

❌ No speaker labels: You can't tell which parts of the transcript came from which person.

❌ Can't answer "who said what": For multi-speaker recordings, this is a critical limitation.

Why This Matters

For single-speaker audio: Whisper is perfect as-is. Lectures, audiobooks, solo podcasts, and monologues work great.

For multi-speaker audio: You need to add speaker diarization. Meetings, interviews, multi-host podcasts, panel discussions, and focus groups all require knowing who spoke when.

That's where this tutorial comes in.

Why Add Speaker Diarization to Whisper?

Speaker diarization transforms Whisper from a speech-to-text tool into a complete multi-speaker transcription solution.

When Whisper Alone Is Enough

  • Podcasts with one host
  • Lectures with one speaker
  • Audiobooks
  • Solo video content
  • Voiceovers
  • Personal recordings

If your audio has one voice throughout, stick with Whisper. It's simpler and faster.

When You Need Speaker Diarization

  • Meetings with multiple participants: Know who committed to what, who made decisions, who raised concerns
  • Interviews: Separate interviewer questions from interviewee responses
  • Multi-host podcasts: Identify which host made which points
  • Panel discussions: Track individual panelist contributions
  • Focus groups: Analyze which participants said what
  • Customer service calls: Separate agent from customer speech

Anywhere "who said what" matters, you need speaker diarization.

The DIY Approach: Whisper + Pyannote

You can combine:

This gives you free, open-source speaker-labeled transcripts with full control over the process.

Tradeoffs:

  • ✅ Free and open-source
  • ✅ Full customization
  • ✅ Works offline
  • ✅ No usage limits
  • ❌ Requires technical setup (2-4 hours)
  • ❌ Slower processing
  • ❌ More troubleshooting

vs. Professional Service

Services like BrassTranscripts do both transcription and speaker diarization automatically:

Tradeoffs:

  • ✅ No setup (0 minutes)
  • ✅ Fast processing
  • ✅ Consistent results
  • ✅ Professional support
  • ❌ Costs money per minute
  • ❌ Less customization
  • ❌ Requires internet

We'll compare both approaches in detail later in this guide.

How to Combine Whisper with Speaker Diarization

Understanding the technical approach helps you troubleshoot issues and optimize results.

The Technology Stack

1. Whisper (Speech-to-Text)

  • Transcribes audio to text
  • Provides word-level timestamps
  • Handles punctuation and capitalization

2. Pyannote.audio (Speaker Diarization)

  • Detects speaker changes
  • Labels speaker segments as SPEAKER_00, SPEAKER_01, etc.
  • Determines how many speakers are present

3. WhisperX (Integration Layer)

  • Combines Whisper + Pyannote seamlessly
  • Aligns transcripts with speaker labels
  • Improves timestamp accuracy
  • Produces speaker-labeled final transcripts

The Process Flow

Step 1: Whisper transcribes audio

Output: "Let's start the meeting. I agree with that proposal."
Timestamps: [0.0s-2.5s], [2.5s-4.8s]

Step 2: Pyannote identifies speakers

0.0s-2.5s: SPEAKER_00
2.5s-4.8s: SPEAKER_01

Step 3: WhisperX combines both

SPEAKER_00 [0.0s-2.5s]: "Let's start the meeting."
SPEAKER_01 [2.5s-4.8s]: "I agree with that proposal."

The result: a complete transcript showing both what was said and who said it.

Whisper Speaker Diarization Tutorial (Python)

This tutorial uses WhisperX, which streamlines combining Whisper with Pyannote speaker diarization.

⚠️ Technical Level: Intermediate Python Required

You'll need:

  • Familiarity with pip and virtual environments
  • Command line comfort
  • 2-4 hours for initial setup
  • Troubleshooting patience

Not technical? Try BrassTranscripts instead—same result in 5 minutes with no setup.

Prerequisites

Before starting, ensure you have:

  • Python 3.8, 3.9, 3.10, or 3.11 (avoid 3.12+ for now due to compatibility)
  • pip package manager
  • 8GB+ RAM recommended
  • GPU optional (CPU works but much slower)
  • HuggingFace account (free) for Pyannote access
  • ffmpeg installed system-wide

Step 1: Install Dependencies

# Create virtual environment (recommended)
python3 -m venv whisper-diarization
source whisper-diarization/bin/activate  # On Windows: whisper-diarization\Scripts\activate

# Update pip
pip install --upgrade pip

# Install WhisperX (includes Whisper and Pyannote integration)
pip install whisperx

# Note: ffmpeg must be installed separately
# Mac: brew install ffmpeg
# Ubuntu/Debian: sudo apt update && sudo apt install ffmpeg
# Windows: Download from https://ffmpeg.org/download.html

Verify installation:

python -c "import whisperx; print('WhisperX installed successfully')"
ffmpeg -version

If you see version numbers, you're ready to proceed.

Common installation issues:

  • "No module named 'whisperx'": Virtual environment not activated
  • "ffmpeg not found": Install ffmpeg system-wide and add to PATH
  • GPU errors: Install PyTorch with CUDA support first (see PyTorch website)

Step 2: Get HuggingFace Access Token

Pyannote requires authentication via HuggingFace.

Create token:

  1. Sign up at https://huggingface.co/ (free)
  2. Go to Settings → Access Tokens
  3. Create new token with read permissions
  4. Save the token (you'll need it in the code)

Accept model agreements: Visit these pages and click "Agree":

Without accepting these agreements, diarization will fail with authentication errors.

Step 3: Basic Whisper + Diarization Script

Create a file called whisper_diarize.py:

import whisperx
import gc

# ======================
# CONFIGURATION
# ======================
device = "cuda"  # Use "cuda" for GPU, "cpu" for CPU
batch_size = 16   # Reduce if out of memory
compute_type = "float16"  # Use "int8" for CPU
audio_file = "your-audio.mp3"  # Your audio file path
hf_token = "YOUR_HUGGINGFACE_TOKEN"  # Replace with your token

print("🎤 Starting Whisper + Pyannote Speaker Diarization")
print(f"📁 Processing: {audio_file}")
print(f"💻 Device: {device}\n")

# ======================
# STEP 1: Load Audio
# ======================
print("1️⃣ Loading audio file...")
audio = whisperx.load_audio(audio_file)
print("✅ Audio loaded\n")

# ======================
# STEP 2: Transcribe with Whisper
# ======================
print("2️⃣ Loading Whisper model...")
model = whisperx.load_model(
    "large-v2",  # Options: tiny, base, small, medium, large-v2, large-v3
    device,
    compute_type=compute_type
)

print("3️⃣ Transcribing audio...")
result = model.transcribe(audio, batch_size=batch_size)
print(f"✅ Transcription complete ({result['language']} detected)\n")

# ======================
# STEP 3: Align Timestamps
# ======================
print("4️⃣ Aligning word-level timestamps...")
model_a, metadata = whisperx.load_align_model(
    language_code=result["language"],
    device=device
)
result = whisperx.align(
    result["segments"],
    model_a,
    metadata,
    audio,
    device,
    return_char_alignments=False
)
print("✅ Timestamps aligned\n")

# Clean up Whisper model to free memory
del model
gc.collect()

# ======================
# STEP 4: Speaker Diarization
# ======================
print("5️⃣ Loading speaker diarization model...")
diarize_model = whisperx.DiarizationPipeline(
    use_auth_token=hf_token,
    device=device
)

print("6️⃣ Performing speaker diarization...")
diarize_segments = diarize_model(audio)
print("✅ Speaker diarization complete\n")

# ======================
# STEP 5: Assign Speakers to Words
# ======================
print("7️⃣ Assigning speakers to transcript...")
result = whisperx.assign_word_speakers(diarize_segments, result)
print("✅ Speaker assignment complete\n")

# ======================
# STEP 6: Output Results
# ======================
print("=" * 60)
print(" TRANSCRIPT WITH SPEAKER LABELS")
print("=" * 60 + "\n")

for segment in result["segments"]:
    speaker = segment.get("speaker", "UNKNOWN")
    text = segment["text"]
    start = segment.get("start", 0)
    end = segment.get("end", 0)
    print(f"{speaker} [{start:.1f}s-{end:.1f}s]: {text}")

# ======================
# STEP 7: Save to File
# ======================
output_file = "transcript_with_speakers.txt"
with open(output_file, "w", encoding="utf-8") as f:
    for segment in result["segments"]:
        speaker = segment.get("speaker", "UNKNOWN")
        text = segment["text"]
        f.write(f"{speaker}: {text}\n")

print(f"\n✅ Saved to {output_file}")
print("🎉 Processing complete!")

Code explanation:

  • Lines 5-9: Configuration parameters you can customize
  • Line 24: Load Whisper model (large-v2 for best accuracy, or use medium/small for speed)
  • Line 30: Transcribe audio with Whisper
  • Lines 37-47: Align word-level timestamps (improves accuracy)
  • Lines 53-58: Load Pyannote diarization model with your HuggingFace token
  • Line 67: Assign speaker labels to transcript segments
  • Lines 74-80: Print results to console
  • Lines 85-90: Save transcript to file

Step 4: Run the Script

# Make sure virtual environment is activated
source whisper-diarization/bin/activate  # Skip on Windows

# Run the script
python whisper_diarize.py

Expected output:

🎤 Starting Whisper + Pyannote Speaker Diarization
📁 Processing: your-audio.mp3
💻 Device: cuda

1️⃣ Loading audio file...
✅ Audio loaded

2️⃣ Loading Whisper model...
3️⃣ Transcribing audio...
✅ Transcription complete (en detected)

4️⃣ Aligning word-level timestamps...
✅ Timestamps aligned

5️⃣ Loading speaker diarization model...
6️⃣ Performing speaker diarization...
✅ Speaker diarization complete

7️⃣ Assigning speakers to transcript...
✅ Speaker assignment complete

============================================================
 TRANSCRIPT WITH SPEAKER LABELS
============================================================

SPEAKER_00 [0.0s-2.5s]: Let's start the meeting.
SPEAKER_01 [2.5s-4.8s]: I agree, thanks for organizing this.
SPEAKER_00 [5.0s-8.2s]: First item on the agenda is the budget.
SPEAKER_02 [8.5s-12.3s]: I have some concerns about the timeline.

✅ Saved to transcript_with_speakers.txt
🎉 Processing complete!

Processing time expectations:

  • CPU: Roughly 1-2x the audio length (1 hour audio = 1-2 hours processing)
  • GPU: Roughly 0.1-0.3x the audio length (1 hour audio = 6-18 minutes)

If processing is very slow, see the troubleshooting section.

Step 5: Customize for Your Needs

Specify number of speakers (improves accuracy if you know speaker count):

diarize_segments = diarize_model(
    audio,
    min_speakers=2,  # Minimum expected speakers
    max_speakers=5   # Maximum expected speakers
)

Choose Whisper model size (speed vs. accuracy tradeoff):

Model Speed Accuracy Best For
tiny Fastest Lowest Quick tests, real-time needs
base Very fast Low Quick drafts
small Fast Moderate Balance for simple audio
medium Medium Good Best general-purpose choice
large-v2 Slow Excellent Highest accuracy needed
large-v3 Slowest Best Critical accuracy

Output in different formats:

# JSON format (structured data)
import json
with open("transcript.json", "w") as f:
    json.dump(result, f, indent=2)

# SRT format (for video subtitles)
def to_srt(segments):
    srt_content = []
    for i, seg in enumerate(segments, 1):
        speaker = seg.get("speaker", "UNKNOWN")
        start = format_timestamp(seg["start"])
        end = format_timestamp(seg["end"])
        text = f"{speaker}: {seg['text']}"
        srt_content.append(f"{i}\n{start} --> {end}\n{text}\n")
    return "\n".join(srt_content)

def format_timestamp(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    millis = int((seconds % 1) * 1000)
    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"

with open("transcript.srt", "w") as f:
    f.write(to_srt(result["segments"]))

DIY Whisper + Pyannote vs. BrassTranscripts: Honest Comparison

Let's compare the DIY approach to professional services objectively.

Setup & Ease of Use

Whisper + Pyannote (DIY):

  • ⏱️ 2-4 hours initial setup time
  • 📚 Requires Python knowledge and command-line skills
  • 🛠️ Troubleshooting Python environments, dependencies, GPU drivers
  • 💻 Command-line usage
  • 📖 Need to read documentation
  • Score: Difficult (not beginner-friendly)

BrassTranscripts:

  • ⏱️ 0 minutes setup
  • 📚 No technical knowledge required
  • 🖱️ Web interface with drag-and-drop
  • 📤 Upload file, download transcript
  • Score: Very easy (anyone can use it)

Winner: BrassTranscripts for ease of use (unless you enjoy technical projects)

Accuracy

Accuracy depends heavily on audio quality, number of speakers, and recording conditions. Both approaches perform well with good audio.

Transcription quality:

  • DIY Whisper: Professional-grade (Whisper is the same model used by many services)
  • BrassTranscripts: Professional-grade (uses optimized Whisper-based models)
  • Winner: Tie

Speaker diarization quality:

  • DIY Pyannote: Good baseline performance with default settings
  • BrassTranscripts: Optimized with proprietary improvements and fine-tuning
  • Winner: BrassTranscripts (optimization and testing advantage)

Both approaches work well for 2-3 speakers with clear audio. For 4+ speakers or challenging audio, professional services typically perform better due to optimization and testing.

Speed & Performance

Processing time for 1 hour audio:

  • DIY (CPU): 1-2 hours
  • DIY (GPU): 6-18 minutes
  • BrassTranscripts: 5-8 minutes

Your time investment:

  • DIY: 2-4 hours setup + processing time per file + troubleshooting
  • BrassTranscripts: 2 minutes (upload + download)

Winner: BrassTranscripts for most users (unless processing hundreds of files and setup time is amortized)

Cost Analysis

DIY Whisper + Pyannote:

  • Software: $0 (free and open-source)
  • Hardware: Your computer's electricity (~$0.10-0.50 per hour)
  • Time: 2-4 hours setup + processing time
  • Total: $0 software + (your hourly rate × hours spent)

BrassTranscripts:

  • Per minute pricing: $0.15/minute
  • 1 hour audio: $9.00
  • Setup time: $0
  • Processing: 5 minutes of your time
  • Total: $9.00 + 5 minutes

Break-even analysis:

If you value your time at $50/hour:

  • DIY cost for 1 hour audio: $0 + (3 hours × $50) = $150 (including setup)
  • BrassTranscripts: $9 + (5 minutes × $50/60) = $13.15

After processing ~15-20 hours of audio, DIY becomes more economical (setup cost amortized).

Winner: Depends on volume

  • Low volume (<10 hours total): BrassTranscripts
  • High volume (100+ hours): DIY may be worth it

Feature Comparison Table

Feature DIY Whisper BrassTranscripts Winner
Setup time 2-4 hours 0 minutes BrassTranscripts
Ease of use Hard Easy BrassTranscripts
Processing speed (GPU) 6-18 min/hour 5-8 min/hour BrassTranscripts
Cost per hour $0* $9.00 DIY
Language support 99+ 99+ Tie
Customization Full control Limited DIY
Offline processing Yes No DIY
Support Community forums Professional BrassTranscripts
Accuracy optimization Manual Automatic BrassTranscripts
Updates & maintenance You maintain it Automatic BrassTranscripts

*Plus your time and compute costs

Our Recommendation

Choose DIY Whisper + Pyannote if:

  • ✅ You're a developer who wants to learn how speaker diarization works
  • ✅ You have 100+ hours of audio to process (setup time amortized)
  • ✅ You value control and customization over convenience
  • ✅ You have time to troubleshoot and optimize
  • ✅ You need offline processing for privacy or security
  • ✅ You have GPU hardware available

Choose BrassTranscripts if:

  • ✅ You need results quickly (5 minutes vs. 3+ hours)
  • ✅ You're not technical or don't want to code
  • ✅ You need consistent, optimized accuracy
  • ✅ You process audio occasionally (not worth 4-hour setup)
  • ✅ Your time is valuable (cost analysis favors it)
  • ✅ You need reliable support

Honest conclusion: For most users, BrassTranscripts is the better choice. The DIY approach is educational and free, but the time investment makes it impractical for business use unless you're processing large volumes regularly. That said, if you're a developer with lots of audio to process, the DIY approach works well once set up.

Try BrassTranscripts free →

Other Ways to Add Speaker Diarization to Whisper

Beyond WhisperX, several alternative approaches exist.

What we used in this tutorial.

Pros:

  • Actively maintained
  • Good documentation
  • Combines Whisper + Pyannote seamlessly
  • Improves timestamp accuracy

Cons:

  • Requires HuggingFace token
  • More complex than basic Whisper

GitHub: https://github.com/m-bain/whisperX

Method 2: Whisper-Diarization (Simpler Alternative)

A simpler implementation that combines Whisper with Pyannote.

Pros:

  • Easier setup than WhisperX
  • Fewer dependencies

Cons:

  • Less actively maintained
  • Fewer features
  • May have accuracy tradeoffs

GitHub: https://github.com/MahmoudAshraf97/whisper-diarization

Method 3: Manual Pyannote Integration

Manually combine Whisper output with Pyannote diarization.

Pros:

  • Maximum control
  • Understand exactly how it works

Cons:

  • Most complex approach
  • Must handle timestamp alignment yourself
  • For advanced users only

See Pyannote documentation and Whisper documentation for details.

Method 4: Cloud APIs

Professional APIs that handle both transcription and speaker diarization:

BrassTranscripts (Recommended):

  • Easiest to use
  • Web interface + API
  • Optimized for accuracy
  • $0.15/minute

AssemblyAI:

  • Developer-focused API
  • Good documentation
  • Real-time options
  • Pricing varies by volume and features (check current pricing)

Deepgram:

  • Alternative to Whisper (proprietary model)
  • Streaming audio support
  • Pricing varies by volume and features (check current pricing)

Each has tradeoffs between ease of use, accuracy, speed, and cost. For most users, BrassTranscripts offers the best balance.

Whisper Speaker Diarization Troubleshooting

Common issues and solutions when running Whisper + Pyannote.

Installation Issues

Problem: pip install whisperx fails

Solutions:

  • Update pip: pip install --upgrade pip
  • Use Python 3.8-3.11 (avoid 3.12+ for now)
  • Install in virtual environment to avoid conflicts
  • Check internet connection (downloads models)

Problem: CUDA/GPU not detected

Solutions:

  • Install PyTorch with CUDA support first: Visit pytorch.org and follow instructions for your system
  • Verify CUDA installation: nvidia-smi (should show GPU)
  • Check CUDA version compatibility with PyTorch
  • Fall back to CPU if needed: Change device = "cpu"

Problem: ffmpeg not found

Solutions:

  • Install ffmpeg system-wide:
    • Mac: brew install ffmpeg
    • Ubuntu/Debian: sudo apt install ffmpeg
    • Windows: Download from ffmpeg.org
  • Add ffmpeg to system PATH
  • Verify: ffmpeg -version

Processing Issues

Problem: Out of memory error

Solutions:

  • Use smaller Whisper model (medium instead of large)
  • Reduce batch_size parameter (try batch_size = 8 or 4)
  • Close other applications to free RAM
  • Use CPU instead of GPU (paradoxically uses less RAM): device = "cpu"
  • Process shorter audio segments

Problem: Very slow processing

Solutions:

  • Use GPU if available (10-20x faster than CPU)
  • Reduce Whisper model size (medium or small)
  • Use compute_type="int8" for CPU processing
  • Ensure batch_size is appropriate for your hardware

Problem: HuggingFace authentication error

Solutions:

Accuracy Issues

Problem: Poor speaker separation (speakers merged or over-segmented)

Solutions:

  • Specify min_speakers and max_speakers if you know speaker count
  • Ensure audio quality is good (check for background noise, echo)
  • Try different Pyannote model versions
  • For complex audio, consider professional service (they handle these edge cases better)

Problem: Speaker labels keep switching mid-conversation

Solutions:

  • Known limitation of Pyannote (speaker ID consistency)
  • Use post-processing to merge consistent speakers
  • Manually verify and correct in critical sections
  • Professional services like BrassTranscripts handle this better with proprietary consistency algorithms

Problem: Timestamps not aligned with speech

Solutions:

  • Ensure alignment step runs (check code includes whisperx.align())
  • Update to latest WhisperX version: pip install --upgrade whisperx
  • Check audio file isn't corrupted

Whisper Speaker Diarization FAQ

Does OpenAI Whisper have built-in speaker diarization?

No, Whisper only performs transcription (speech-to-text). You need to add Pyannote or use a service like BrassTranscripts that combines both.

What is WhisperX?

WhisperX is an open-source tool that combines Whisper with speaker diarization (Pyannote) and improves word-level timestamp accuracy. It's the easiest way to add speaker diarization to Whisper.

Is Whisper + Pyannote speaker diarization free?

Yes, both Whisper and Pyannote are open-source and free to use. You only pay for compute resources (your electricity or cloud costs).

How accurate is Whisper with speaker diarization?

Accuracy depends on audio quality and number of speakers. With clear audio and 2-3 speakers, both transcription and speaker diarization perform well. Professional services typically achieve slightly higher accuracy through optimization and testing.

Can I use Whisper speaker diarization on Mac?

Yes, Whisper and WhisperX work on Mac (both Intel and Apple Silicon). Install via pip as shown in the tutorial. Apple Silicon Macs benefit from Metal acceleration for faster processing.

How long does Whisper speaker diarization take?

Processing time depends on your hardware:

  • CPU: 1-2x audio length (1 hour audio = 1-2 hours processing)
  • GPU: 0.1-0.3x audio length (1 hour audio = 6-18 minutes)
  • BrassTranscripts: ~5-8 minutes regardless of your hardware

What's the best Whisper model for speaker diarization?

large-v2 or large-v3 for highest accuracy. Use medium for faster processing with good accuracy. Avoid tiny and base unless speed is critical—accuracy suffers significantly.

Can Whisper identify speakers by name automatically?

No, Whisper labels speakers as SPEAKER_00, SPEAKER_01, etc. You must manually assign names afterward. See our Speaker Name Assignment Helper prompt to make this easier with AI.

Does Whisper speaker diarization work in real-time?

Not reliably. Whisper and Pyannote are designed for recorded audio, not streaming. Real-time speaker diarization requires different approaches with accuracy tradeoffs.

What are alternatives to Whisper + Pyannote for speaker diarization?

  • BrassTranscripts - Easiest, optimized accuracy, web interface + API
  • AssemblyAI - Developer-focused API
  • Deepgram - Real-time capabilities
  • Or use Whisper separately + other diarization tools

Should You Use DIY Whisper Speaker Diarization?

The answer depends entirely on your situation.

DIY Whisper + Pyannote Makes Sense If

  • ✅ You're technical (comfortable with Python and command-line tools)
  • ✅ You have time to set up (2-4 hours initial investment)
  • ✅ You're processing many files (setup cost amortized across volume)
  • ✅ You want to learn how speaker diarization works under the hood
  • ✅ You need offline processing (privacy or security requirements)
  • ✅ You have GPU hardware (makes processing practical)

If all or most of these apply, the DIY approach can work well.

BrassTranscripts Makes Sense If

  • ✅ You need results now (5 minutes vs. 3+ hours)
  • ✅ You're not technical (no coding required)
  • ✅ You need optimized accuracy (professionally tuned models)
  • ✅ You process audio occasionally (not worth 4-hour setup)
  • ✅ Your time is valuable ($9 vs. hours of your time)
  • ✅ You want reliable support (professional help when needed)

For most users, this is the better choice.

The Honest Truth

Setting up Whisper + Pyannote speaker diarization is a valuable learning experience for developers. You'll understand how modern speech recognition and speaker diarization work. The code is free and open-source.

But for practical use cases, the time investment (2-4 hours setup + slower processing + occasional troubleshooting) makes professional services more economical for most people. We built BrassTranscripts because even as developers, we found the DIY approach too time-consuming for production use.

Choose based on your priorities: education and control (DIY) or speed and convenience (professional service).

Get Started

Want to try the DIY approach?

Want results in 5 minutes instead of 4 hours?

Related guides:

Have questions or run into issues? The WhisperX GitHub discussions are helpful for community support, or contact us if you'd prefer professional assistance.

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