How to Clean and Label Interview Transcripts at Scale

January 16, 2026

You've just finished 20 user interviews for your research project. You have 18 hours of audio. You need clean transcripts for analysis by Friday.

You run the audio through an AI transcription service. It's fast. It's cheap. You get 20 transcripts back in an hour.

Then you open the first file.

Every speaker is labeled "Speaker 1" or "Speaker 2." Product names are misspelled. Overlapping speech is garbled. Technical terms your participants used perfectly are transcribed as nonsense.

You realize you're about to spend two days in Google Docs, manually fixing speaker names, correcting terminology, and untangling crosstalk. You have 19 more files to go.

This is the hidden cost of bulk transcription. The AI gives you something, but not something you can actually use. And at scale, cleaning transcripts manually doesn't work.

Why Raw Audio Doesn't Scale

You can't analyze spoken content directly. You can't search it. You can't code it thematically. You can't feed it to analysis tools.

Listening to 20 hours of interviews sequentially would take weeks. Even at 1.5x speed, you're looking at 12+ hours of focused listening just to extract key themes.

Transcripts solve this. They turn time-bound audio into searchable, analyzable text. But only if the transcripts are clean enough to work with.

Why Poor Transcripts Fail at Scale

A transcript with "Speaker 1" and "Speaker 2" throughout is technically accurate but operationally useless. If you're doing thematic coding, you need to know who said what. "Speaker 1" tells you nothing.

If you're using qualitative analysis software like NVivo, Atlas.ti, or Dedoose, inconsistent speaker names break your coding structure. "Sarah," "Sara," and "Sarah M." get treated as three different people.

If you're exporting quotes for a report, you need proper attribution. "As Speaker 2 mentioned" doesn't work in a published paper or client presentation.

And if multiple people on your team are editing transcripts separately, version control becomes a nightmare. Who has the latest file? Which corrections have been applied? Did anyone fix the part where the interviewer's question got cut off?

The Real Workflow: Bulk Upload, In-Browser Editing, Clean Export

Here's how to handle 15–50 interview transcripts without losing your mind.

Step 1: Upload everything at once

Don't transcribe one file at a time. Upload your entire batch. If you have 20 MP3 files from Zoom interviews, drag them all into the transcription tool together. Processing happens in parallel. You get all 20 transcripts back at once.

This matters because you need to see patterns across files. Maybe the AI consistently misspells a product name. Maybe it struggles with a particular participant's accent. You won't notice this transcribing one file every few days.

Step 2: Edit transcripts in-browser, not in downloaded files

The old workflow: download a transcript, open it in Word, make changes, save it, upload it somewhere else for your team, hope nobody else edited it simultaneously.

The better workflow: edit directly in the transcription interface. Fix speaker labels inline. Correct terminology as you read. Adjust timestamps if needed. All changes save immediately. No file juggling.

This is where Alfie's design matters. The editing UI is built for working transcripts, not static documents. You can:

Relabel speakers globally (change every instance of "Speaker 1" to "Sarah Chen" in one click)
Jump to specific timestamps and fix garbled sections
Search across the transcript for specific terms
Export your edited version whenever you're ready

You're not downloading, editing offline, and re-uploading. You're treating the transcript as a living document until it's clean.

Step 3: Fix what matters, ignore what doesn't

You don't need perfect transcripts. You need usable transcripts.

Fix these:

Speaker labels (critical for analysis)
Key terminology (product names, technical terms, participant-specific language)
Garbled sections where meaning is unclear

Don't waste time on:

Minor grammar issues that don't affect meaning
Filler words like "um" and "uh" (unless your research specifically requires them)
Perfect punctuation in casual speech

The goal is a transcript you can search, code, and quote from. Not a polished document ready for publication.

Worked Example: 15-Interview UX Research Project

You're conducting user research for a B2B SaaS product. You've interviewed 15 customers about their workflow challenges. Each interview is 45–60 minutes. You need to identify common pain points and extract quotes for a report.

Day 1: Transcription and first-pass editing

Upload all 15 MP4 files to Alfie
Transcripts complete in ~2 hours
Spend 3–4 hours doing first-pass editing:
- Relabel "Speaker 1" to participant name, "Speaker 2" to interviewer name
- Fix the product name (the AI kept transcribing "Clarion" as "clarion" or "Clarity")
- Correct a few technical terms participants used

Day 2: Quality check and export

Scan through transcripts looking for patterns
Notice the AI struggled with one participant's accent—spend 20 minutes cleaning that transcript more thoroughly
Export all 15 transcripts as TXT files (plain text works best for your thematic coding tool)
Also export 3 key interviews as SRT files (you're creating a highlight reel video and need timed captions)

Day 3–5: Analysis

Import TXT files into your qualitative analysis software
Begin thematic coding: tag mentions of "time savings," "integration issues," "learning curve," etc.
Pull direct quotes for the final report, properly attributed by participant name
Alternatively: paste cleaned transcripts into ChatGPT and ask it to identify recurring themes as a starting point (then verify manually)

The transcripts are the foundation. Everything downstream depends on them being clean and correctly labeled.

Export Formats Matter

You don't want transcripts locked in one format. Different downstream uses need different structures.

TXT (plain text): Best for thematic coding software, simple text analysis, or pasting into ChatGPT for initial pattern recognition.

SRT (subtitle format): Needed if you're creating video clips with captions, or if you want timestamped segments for reference.

JSON: Useful if you're building custom analysis scripts or integrating transcripts into your own database.

DOCX: For team members who need to review and comment in Word.

Alfie exports to all of these. You choose what fits your workflow. The transcript data is the same—just formatted differently.

Alfie's Role: The Transcription Backbone

Alfie doesn't analyze your interviews. It doesn't extract themes or generate summaries. It doesn't replace your qualitative analysis tools.

It does one thing well: turns audio/video into clean, editable, exportable transcripts.

You upload files. You edit speaker labels and fix errors. You export in the format your downstream tools expect.

That's it. No lock-in. No forced AI analysis you didn't ask for. Just reliable transcription that scales.

If you want to analyze transcripts with ChatGPT, run sentiment analysis, or feed them into specialized research tools—do that. Alfie gives you the clean input layer. What you do with it is up to you.

When This Workflow Works

This approach makes sense when:

You're managing 10+ interviews or recordings per project
You need consistent speaker labeling across files
Multiple people need access to clean transcripts
You're exporting to different tools (coding software, video editors, analysis scripts)
Data privacy matters

It doesn't make sense when:

You're transcribing one or two casual recordings
You don't need speaker labels (e.g., solo podcast episodes)
You're fine with raw AI output as-is

Try It With Your Next Batch

If you're starting a research project, don't transcribe interviews one at a time and clean them later. Upload the batch, edit as you go, and export when you're ready.

Alfie offers a 2-hour free trial—no credit card required. Enough to transcribe 3–4 interviews and see if the editing workflow fits how you work.

The first project is the test. If cleaning 15 transcripts takes you 4 hours instead of 2 days, you've found a workflow that scales.

Try Alfie with your next interview batch.