BrassTranscripts logo
BrassTranscripts
BY COPPER SUN
← Back to Blog
•17 min read•BrassTranscripts Team

How to Transcribe Multiple Speakers [Complete Guide]

Transcribe Audio To TextAudio TranscriptionSpeaker Identification

Transcribing audio with multiple speakers presents unique challenges: overlapping speech, similar-sounding voices, and the need to identify who said what. Whether you're transcribing meetings, interviews, podcasts, or group discussions, this guide covers everything you need to know.

Quick Navigation

Why Multi-Speaker Transcription is Challenging

The Core Problem

Single-speaker transcription is straightforward: the AI recognizes speech and converts it to text. Multi-speaker transcription requires an additional step called speaker diarization - the process of detecting and labeling different speakers.

Technical challenges:

  • Overlapping speech: Multiple people talking simultaneously
  • Similar voices: Distinguishing between speakers with similar vocal characteristics
  • Variable audio quality: Different microphone distances, echo, background noise
  • Speaker turns: Detecting when one person stops and another begins
  • Speaker consistency: Maintaining the same label for each speaker throughout

What You're Really Asking For

When you search "how to transcribe multiple speakers," you need two things:

  1. Speech-to-text transcription: Converting spoken words into written text
  2. Speaker identification: Labeling which person said which words

Most basic transcription tools do #1 well but struggle with #2. This guide focuses on solutions that handle both effectively.

BrassTranscripts: Automatic Multi-Speaker Transcription

Best for: Meetings, interviews, podcasts, group discussions requiring accurate speaker separation

How it works:

  1. Upload your audio or video file (MP3, MP4, WAV, etc.)
  2. AI automatically transcribes speech
  3. AI automatically identifies and labels different speakers
  4. Download transcript with speaker labels in multiple formats

Process:

  1. Visit brasstranscripts.com
  2. Upload your multi-speaker audio (up to 2 hours, 250MB max)
  3. AI processing completes in minutes
    • WhisperX large-v3 model for transcription
    • Automatic speaker diarization with advanced algorithms
    • Language auto-detection (99+ languages)
  4. Preview 30 words free to verify speaker separation quality
  5. Pay only if satisfied ($2.25 for files 1-15 minutes, $0.15/minute for 16+ minutes)
  6. Download in 4 formats: TXT, SRT, VTT, JSON

Output example (TXT format):

[00:00:03] Speaker 0: Welcome everyone to today's product meeting.

[00:00:07] Speaker 1: Thanks for having me. I'd like to start by discussing the Q4 roadmap.

[00:00:15] Speaker 0: Great, let's dive in. What are the top priorities?

[00:00:19] Speaker 1: The analytics dashboard redesign is our primary focus.

Advantages:

  • No technical setup required
  • Accurate speaker separation on clean audio
  • Multiple export formats included
  • No subscription - pay per file
  • Works on any device (desktop, mobile, tablet)
  • Privacy: files deleted after 24 hours

Limitations:

  • Requires internet connection
  • 2-hour file limit (can split longer files)
  • Speakers labeled as "Speaker 0, Speaker 1" (requires manual name assignment)

Cost analysis:

  • 30-minute interview: $4.50
  • 1-hour meeting: $9.00
  • 2-hour podcast: $18.00

When to Use This Method

Choose professional AI transcription when:

  • You need results quickly (minutes, not hours)
  • You lack technical expertise for open-source tools
  • Audio quality is reasonable (clear speech, minimal background noise)
  • You're transcribing business-critical content (meetings, interviews, depositions)
  • You want multiple export formats
  • You need speaker identification accuracy

Method 2: Free Open-Source Tools

Whisper + Pyannote Speaker Diarization

Best for: Technical users comfortable with Python, cost-sensitive projects, developers integrating transcription into applications

Requirements:

  • Python 3.8+ installed
  • Basic command-line knowledge
  • Time to set up and troubleshoot
  • Hugging Face account (free)

Setup process:

Step 1: Install Dependencies

# Install Whisper (OpenAI's speech recognition model)
pip install -U openai-whisper

# Install Pyannote for speaker diarization
pip install pyannote.audio

Step 2: Get Hugging Face Access Token

  1. Create free account at huggingface.co
  2. Visit pyannote/speaker-diarization-3.1
  3. Accept user agreement
  4. Generate access token in settings

Step 3: Transcribe with Whisper

import whisper

# Load Whisper model
model = whisper.load_model("large-v3")

# Transcribe audio
result = model.transcribe("meeting.mp3")

# Save transcript
with open("transcript.txt", "w") as f:
    f.write(result["text"])

This gives you a transcript without speaker labels. For speaker separation, add Pyannote:

Step 4: Add Speaker Diarization

from pyannote.audio import Pipeline

# Load diarization pipeline
pipeline = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-3.1",
    use_auth_token="YOUR_HUGGINGFACE_TOKEN"
)

# Perform diarization
diarization = pipeline("meeting.mp3")

# Print speaker segments
for turn, _, speaker in diarization.itertracks(yield_label=True):
    print(f"{speaker}: {turn.start:.1f}s - {turn.end:.1f}s")

Full tutorial: See our Whisper Speaker Diarization Guide for complete Python implementation.

Advantages:

  • Completely free (no per-file costs)
  • Full control over processing
  • Works offline after initial setup
  • Can process unlimited files
  • Can customize and tune parameters

Limitations:

  • Requires technical expertise
  • Time-consuming setup process
  • Limited support (community forums only)
  • Manual troubleshooting required
  • No user interface (command-line only)
  • Requires combining Whisper output with Pyannote diarization manually

Time investment:

  • Initial setup: 1-3 hours
  • Per-file processing: 10-30 minutes (depending on file length and computer speed)
  • Learning curve: 3-5 hours for beginners

When to Use This Method

Choose open-source tools when:

  • You're transcribing many files regularly (cost savings over time)
  • You have technical skills (Python, command-line)
  • You're building a custom application
  • You need offline processing capability
  • Time investment is acceptable

Method 3: Commercial Platforms

Otter.ai, Rev, Descript, and Others

Commercial transcription platforms offer varying levels of speaker identification:

Otter.ai

Pricing: Free plan (600 minutes/month), Pro ($16.99/month), Business ($30/user/month)

Speaker identification:

  • Automatic speaker detection
  • Can assign names during or after meeting
  • Works best with Otter's own recording features
  • Quality varies with uploaded audio

Best for: Teams already using Otter for live meeting transcription

Limitations:

  • Monthly subscription required for upload capability
  • Speaker identification accuracy varies
  • Limited control over processing

Rev.com

Pricing: $1.50/minute (human transcription), AI transcription pricing varies

Speaker identification:

  • Human transcriptionists label speakers
  • Highest accuracy but expensive
  • 12-24 hour turnaround

Best for: Legal, medical, or business-critical transcripts requiring guaranteed accuracy

Descript

Pricing: Free plan (limited), Creator ($15/month), Pro ($30/month)

Speaker identification:

  • Automatic speaker detection
  • Built into video editing workflow
  • Can assign speaker names

Best for: Content creators editing podcasts or videos who need transcription as part of editing workflow

Commercial Platform Comparison

Platform Monthly Cost Speaker ID Quality Best Use Case
BrassTranscripts Pay-per-file ($0.15/min) High accuracy One-time files, occasional use
Otter.ai $16.99-30/month Good for live meetings Regular meeting transcription
Rev.com $1.50/min (human) Highest (human verification) Legal, medical, critical accuracy
Descript $15-30/month Good Video/podcast editing workflow

Note: Pricing subject to change - check official websites for current rates.

Method 4: Manual Transcription

When You Should Consider Manual Work

Manual transcription makes sense only in specific scenarios:

  • Very short files (under 5 minutes)
  • Extremely poor audio quality where AI fails completely
  • Highly specialized terminology requiring domain expertise
  • Legal requirements for human verification

Manual Process

  1. Use transcription software with playback controls

    • Express Scribe (free)
    • oTranscribe (browser-based, free)
    • Transcribe by Wreally (paid)

  2. Set up foot pedal controls (optional but helpful)

    • Play/pause with foot control
    • Keeps hands free for typing

  3. Transcribe in passes:

    • Pass 1: Transcribe all speech without speaker labels
    • Pass 2: Add speaker labels and timestamps
    • Pass 3: Review and correct errors

Time required:

  • Experienced transcriptionist: 4-6 hours per 1 hour of audio
  • Beginner: 6-10 hours per 1 hour of audio
  • Multi-speaker audio: Add 25-50% more time

Cost if hiring:

  • Professional transcriptionists: $1.00-2.50 per audio minute
  • 1-hour file: $60-150

Why Manual Transcription is Impractical for Most Cases

Unless you have specific requirements for manual work, AI transcription is:

  • 10-20x faster (minutes vs hours)
  • More cost-effective ($9/hour vs $60-150/hour)
  • Consistent quality (no transcriptionist fatigue)
  • Scalable (can process multiple files simultaneously)

Recording Tips for Better Speaker Separation

The quality of speaker identification depends heavily on your original audio recording. Follow these best practices:

Microphone Setup

Best approach: Individual microphones per speaker

  • USB microphones for each participant
  • Separate audio tracks if possible
  • Prevents voice overlap and cross-talk

Acceptable approach: Single omnidirectional microphone

  • Position equidistant from all speakers
  • Use high-quality microphone (not laptop built-in)
  • Minimize background noise

Avoid: Laptop built-in microphones

  • Poor quality for multi-speaker scenarios
  • Difficulty separating voices
  • Picks up excessive background noise

Room Environment

Ideal conditions:

  • Quiet room with minimal echo
  • Soft furnishings (carpets, curtains) reduce echo
  • Close windows to minimize outside noise
  • Turn off fans, AC, or other noise sources during recording

Seating arrangement:

  • Speakers at similar distances from microphone
  • Avoid one person much closer/farther than others
  • Face microphone when speaking

Recording Settings

File format:

  • WAV or FLAC for highest quality
  • MP3 at 192 kbps or higher (acceptable)
  • Avoid over-compression

Sample rate:

  • 44.1 kHz minimum
  • 48 kHz recommended
  • Higher sample rates don't significantly improve speech recognition

Channels:

  • Stereo is fine for single microphone
  • Multi-channel if recording separate tracks per speaker

Speaking Guidelines

Instruct participants:

  • Speak one at a time when possible
  • Avoid interrupting or talking over others
  • Speak at normal volume (not too quiet or too loud)
  • Minimize filler words like "um," "uh" (AI handles these but clean speech improves accuracy)
  • State names at beginning: "This is John speaking..."

For interviews:

  • Interviewer introduces themselves and guest at start
  • Helps AI learn voice patterns
  • Makes manual name assignment easier later

Post-Processing: Assigning Names to Speaker Labels

Most automatic transcription systems label speakers as "Speaker 0," "Speaker 1," etc. To assign real names:

Method 1: Manual Find-and-Replace

Simple approach for short transcripts:

  1. Listen to the first 30 seconds of your audio
  2. Identify which speaker label corresponds to which person
    • "Speaker 0" is Jane
    • "Speaker 1" is Michael
  3. Use find-and-replace in your text editor:
    • Find: "Speaker 0"
    • Replace: "Jane"
    • Replace all instances

Works well for:

  • 2-3 speakers
  • Speakers with distinct voices
  • Short to medium files (under 1 hour)

Method 2: AI-Assisted Name Assignment

Use AI to help identify speakers based on context:

See our Speaker Identification Complete Guide for an AI prompt that analyzes transcript context to suggest which speaker label corresponds to which person.

How it works:

  1. Upload your transcript to ChatGPT or Claude
  2. Provide context (meeting type, participant names)
  3. AI analyzes speech patterns and content to identify speakers
  4. Review and confirm AI suggestions
  5. Perform find-and-replace based on AI analysis

Accuracy depends on:

  • Distinct speaking styles between participants
  • Contextual clues in conversation (addressing each other by name)
  • Length of transcript (more context = better accuracy)

Method 3: Time-Coded Manual Review

Most accurate but time-intensive:

  1. Open transcript and audio side-by-side
  2. Play audio from beginning
  3. Note which speaker label corresponds to which voice
  4. Use find-and-replace once confirmed

Time required: 5-10 minutes for typical meeting

Troubleshooting Common Issues

Issue 1: Speakers Not Separated Correctly

Symptoms:

  • All speech attributed to single speaker
  • Random speaker label switching mid-sentence
  • Two different people both labeled "Speaker 0"

Possible causes:

  • Poor audio quality (muffled, echo, background noise)
  • Similar-sounding voices
  • Speakers too close to single microphone
  • Low-quality transcription service

Solutions:

  1. Re-record with better audio setup if possible
  2. Try a different transcription service - speaker diarization quality varies significantly
  3. Use speaker-separated audio tracks if available
  4. Accept limitations and manually correct if audio quality cannot be improved

Issue 2: Too Many Speaker Labels

Symptoms:

  • Expected 2 speakers, transcript shows "Speaker 0, 1, 2, 3, 4"
  • Same person incorrectly split into multiple speaker labels

Possible causes:

  • Background noise interpreted as additional speaker
  • Significant voice changes (coughing, laughing, raised voice)
  • AI oversensitivity to voice variations

Solutions:

  1. Manually merge speaker labels - Use find-and-replace to consolidate
  2. Improve audio quality for future recordings
  3. Review and correct manually - Time-consuming but necessary for accuracy

Issue 3: Speaker Labels Switching Mid-Conversation

Symptoms:

  • "Speaker 0" suddenly becomes "Speaker 1" halfway through
  • No actual speaker change occurred

Possible causes:

  • Audio quality drop at that point
  • Overlapping speech confusing the AI
  • Voice characteristics changing (speaker moved closer/farther from mic)

Solutions:

  1. Check audio at that timestamp - Look for quality issues
  2. Manually correct labels - Use find-and-replace for segments
  3. Re-process with different service - Some handle these cases better

Issue 4: Overlapping Speech Not Transcribed

Symptoms:

  • Sections where multiple people talk simultaneously are missing words
  • Garbled or incorrect transcription during cross-talk

Reality: This is a limitation of current AI technology. No transcription system handles overlapping speech perfectly.

Solutions:

  1. Accept limitations - Overlapping speech is inherently difficult
  2. Re-record if critical - Avoid cross-talk in future recordings
  3. Manually fill in gaps - Listen to audio and add missing words
  4. Use video conferencing platforms that separate tracks - Some systems record each participant's audio separately

Choosing the Right Method for Your Needs

Decision Framework

Use professional AI transcription (BrassTranscripts) if:

  • You need results quickly (minutes, not hours)
  • You're transcribing occasionally (not daily)
  • You lack technical skills for open-source tools
  • You want reliable speaker separation without manual work
  • You need multiple export formats

Use free open-source tools (Whisper + Pyannote) if:

  • You're transcribing regularly (10+ files/month)
  • You have Python and command-line skills
  • You can invest 3-5 hours learning the setup
  • You need offline processing
  • You're building custom applications

Use commercial platforms (Otter, Rev, Descript) if:

  • You're already using them for other features (video editing, live meeting notes)
  • Your team needs collaborative transcription features
  • You require human-verified accuracy (Rev)
  • Monthly subscription fits your budget and usage patterns

Use manual transcription if:

  • Files are very short (under 5 minutes)
  • AI completely fails due to audio quality
  • Legal requirements mandate human transcription
  • Specialized terminology requires domain expertise

Cost Comparison Example (20 hours of audio per month)

Method Monthly Cost Time Investment Speaker ID Quality
BrassTranscripts $180 (pay-per-file) ~1 hour (upload/download) High
Whisper + Pyannote $0 ~5 hours setup + 10 hours processing High (with tuning)
Otter.ai Pro $16.99 ~2 hours Good
Rev (human) $1,800 ~1 hour Highest
Manual $1,200-3,000 (if hiring) 80-120 hours (if doing yourself) Highest

Frequently Asked Questions

How accurate is automatic multi-speaker transcription?

Accuracy depends on audio quality and speaker characteristics:

Clean audio (professional microphones, quiet environment):

  • Speech-to-text: High accuracy for clear speech
  • Speaker identification: Generally reliable for distinct voices

Conference calls, meetings with background noise:

  • Speech-to-text: Good accuracy with some errors
  • Speaker identification: Moderate accuracy, may confuse similar voices

Poor audio (laptop mic, echo, overlapping speech):

  • Speech-to-text: Moderate accuracy with frequent errors
  • Speaker identification: Difficult, may require manual correction

Specific accuracy percentages vary by service and audio conditions. Always use preview features to check quality before paying.

Can AI transcribe more than 2 speakers?

Yes, modern speaker diarization systems can identify and label many speakers:

  • 2-4 speakers: Generally handled well
  • 5-8 speakers: Possible but accuracy decreases
  • 9+ speakers: Challenging, frequent mislabeling likely

Best practices for large groups:

  • Use individual microphones per speaker if possible
  • Record separate audio tracks (some video conferencing platforms support this)
  • Accept that manual correction will be necessary
  • Consider whether you truly need to identify all speakers, or if grouping is acceptable

How long does multi-speaker transcription take?

Professional AI services (BrassTranscripts):

  • Processing time: ~2-5 minutes per hour of audio
  • Total workflow: 5-10 minutes (upload, process, download)

Free open-source tools (Whisper + Pyannote):

  • Processing time: 10-30 minutes per hour of audio (depends on computer speed)
  • First-time setup: 1-3 hours

Manual transcription:

  • 4-10 hours per hour of audio (depending on skill level and audio quality)

What audio formats work for multi-speaker transcription?

Most transcription services support common formats:

Supported formats:

  • MP3 (most common)
  • MP4 (video files - audio extracted automatically)
  • WAV (high quality, large file size)
  • M4A (Apple audio format)
  • FLAC (lossless compression)
  • AAC, OGG, Opus, WebM, MPEG

BrassTranscripts specifically supports: MP3, MP4, M4A, WAV, AAC, FLAC, OGG, Opus, WebM, MPEG, MPGA (up to 250MB, 2 hours)

Do I need to tell the AI how many speakers are in the audio?

Most modern systems automatically detect the number of speakers. You don't need to specify in advance.

How it works:

  1. AI analyzes the entire audio file
  2. Identifies distinct voice patterns
  3. Assigns speaker labels based on detected voices
  4. Labels speakers as "Speaker 0," "Speaker 1," etc.

Exception: Some older systems or specialized tools allow you to manually specify speaker count, which can improve accuracy if you know the exact number.

Can I transcribe audio where speakers have accents?

Yes, modern AI transcription handles accents, though accuracy varies:

Well-supported accents:

  • Standard American English
  • Standard British English
  • Common international accents (Indian, Australian, etc.)

Challenges:

  • Heavy regional accents may reduce accuracy
  • Non-native speakers with strong accents may have more errors
  • Mixing multiple languages in same conversation

Solutions:

  • Use transcription services supporting multiple languages
  • Expect to manually correct more errors
  • Consider services with human review for critical accuracy

What if my transcript shows too many or too few speakers?

Too many speakers (expected 2, got 5):

Cause: Background noise, voice variations, or AI oversensitivity

Solution: Manually merge speaker labels using find-and-replace

Too few speakers (expected 3, got 1):

Cause: Voices too similar, poor audio quality, or AI undersensitivity

Solution: Try a different transcription service, or manually split and label speakers

Getting the right number:

  • High-quality audio with distinct voices → More accurate speaker count
  • Professional transcription services generally perform better than basic tools

Can I transcribe a video file with multiple speakers?

Yes, transcription services automatically extract audio from video files:

Supported video formats:

  • MP4 (most common)
  • MOV (Apple format)
  • WebM
  • MPEG

Process:

  1. Upload video file (same as audio)
  2. Service automatically extracts audio track
  3. Transcribes speech and identifies speakers
  4. Provides transcript (video is not included in output)

Note: You're transcribing the audio portion only - visual information is not analyzed.

Conclusion

Transcribing multiple speakers is no longer a manual, time-intensive process. Modern AI transcription services automatically separate speakers and generate accurate transcripts in minutes.

Key takeaways:

  1. Professional AI transcription (BrassTranscripts) offers the best balance of speed, accuracy, and ease of use for most users
  2. Free open-source tools (Whisper + Pyannote) work well for technical users with time to invest in setup
  3. Audio quality is critical - invest in good recording practices for better results
  4. Speaker identification accuracy depends on distinct voices and clean audio
  5. Manual name assignment is typically required (speaker labels vs actual names)
  6. Preview before paying to verify speaker separation quality

Next steps:

  1. Assess your audio quality and speaker count
  2. Choose the method that fits your technical skills and budget
  3. Upload a test file to verify speaker separation quality
  4. Establish a workflow for regular transcription needs

For professional multi-speaker transcription with automatic speaker identification, visit BrassTranscripts - upload your file and preview the first 30 words free to check speaker separation quality.

Related Guides:

Ready to try BrassTranscripts?

Experience the accuracy and speed of our AI transcription service.

Start Transcribing →Read More Articles