How to Transcribe Your Podcast Backcatalog With Ease

January 18, 2026

You launched your podcast in 2019. You've published 187 episodes. Your current episodes get transcripts automatically through your podcast host.

But episodes 1-186? No transcripts. Google can't index them. Screen readers can't access them. Potential listeners searching for topics you covered years ago will never find those episodes.

You know you need to fix this. You've thought about hiring someone to transcribe everything manually. You've considered using free AI tools and uploading one episode at a time, editing each transcript individually.

Both options feel impossible. Manual transcription of 187 episodes would cost thousands of dollars. One-at-a-time AI transcription means 187 separate uploads, 187 editing sessions, 187 file downloads. You'd burn out by episode 30.

There's a better way. You can transcribe your entire backcatalog in a weekend without losing your mind. But it requires treating this as a batch operation, not 187 individual tasks.

Why Your Old Episodes Are Invisible

Untranscribed podcast episodes are functionally invisible to search engines. Google can't listen to audio—search engines index text, not audio.

This means:

Your episode about "startup pricing strategies" won't show up when someone Googles that exact phrase
Listeners who prefer reading can't access your content
Screen readers can't help visually impaired people discover your show
Your show notes page has no searchable content

Every untranscribed episode is a missed opportunity for discovery. If you've published 150 episodes, that's 150 pieces of content that might as well not exist to anyone searching Google.

And as podcasting matures, accessibility isn't optional anymore. Transcripts are becoming expected, not exceptional. Major directories now support transcripts. Audiences expect them. If you're competing with newer shows that transcript everything, you're at a disadvantage.

Why Podcasters Put Off Backcatalog Transcription

The math is brutal.

If you have 150 episodes averaging 45 minutes each, that's 112 hours of audio. Manual transcription typically takes 4-5x the audio length. You're looking at 450-550 hours of work. Even at minimum wage, that's $3,000-$4,000 in labor costs.

Free AI transcription tools exist, but most don't handle bulk uploads. You'd upload one file, wait, download the transcript, upload the next file, repeat. For 150 episodes, that's 150 separate operations—even if the transcription is automated, the workflow isn't.

And even automated transcripts need editing. AI messes up:

Speaker labels (especially multi-host shows with guests)
Product names, brand names, company names
Technical jargon specific to your industry
Guest names and titles
Show-specific terminology you use repeatedly

Editing 150 transcripts one at a time, each in a separate document, is tedious beyond belief. You lose track of which episodes you've cleaned. You forget which recurring errors you need to fix. Version control becomes a nightmare.

So most podcasters just… don't do it. The backcatalog stays untranscribed.

The Batch Processing Mindset

Transcribing 150 episodes individually is impossible. Transcribing them as a batch is manageable.

The difference:

Individual: 150 separate uploads → 150 separate edits → 150 separate downloads
Batch: one bulk upload → systematic editing of common errors → bulk export

When you treat this as a batch operation, patterns emerge that save enormous time.

Example: Your co-host is named "Priya Sharma." The AI consistently transcribes it as "Priya Sharman," "Pria Sharma," or "Speaker 2." If you're editing episode-by-episode, you fix this error 150 times. If you're batch processing, you identify the pattern once and create a systematic approach.

How to Actually Transcribe 150+ Episodes

Here's the workflow that doesn't destroy your weekend.

Step 1: Organize your files first

Before uploading anything, get your episode files organized locally. Create a folder structure:

Podcast_Backcatalog/
  2019/
    episode-001.mp3
    episode-002.mp3
    ...
  2020/
    episode-053.mp3
    ...
  2021/
    ...

Consistent file naming matters. If your files are named "recording_final_v2.mp3" and "episode_tuesday.mp3," you'll lose track of what's what when you're working with 150 transcripts.

Name them: episode-XXX-short-title.mp3

Step 2: Batch upload everything

Don't upload one episode at a time. Select all 150 files and upload them together.

Most transcription services process sequentially—you upload one, wait, upload another. That's the bottleneck. You want parallel processing where all 150 files get transcribed simultaneously.

This is where Alfie's design matters. Upload your entire folder. Processing happens in parallel. You get all 150 transcripts back together, not 150 separate jobs.

Step 3: Identify recurring errors before editing anything

Before you start editing individual transcripts, scan 5-10 random episodes and note:

How are speakers labeled? "Speaker 1/2" or garbled names?
What names get consistently misspelled?
What technical terms does the AI struggle with?
What product/brand names are wrong?

Make a list. These are your systematic corrections.

For a marketing podcast, the list might look like:

"Speaker 1" → "Sarah Chen" (host)
"Speaker 2" → varies (guest names)
"HubSpot" often transcribed as "hub spot" or "Hubspot"
"SEO" sometimes transcribed as "CEO"
"Canva" sometimes transcribed as "canvas"

Step 4: Edit systematically, not sequentially

Now you have 150 transcripts. Don't edit episode 1, then episode 2, then episode 3.

Instead:

Fix speaker labels globally across all episodes
Correct recurring brand/product name errors
Spot-check 10% of episodes for quality
Fix only the episodes where errors affect usability

With Alfie's in-browser editing, you can:

Search for "Speaker 1" across all transcripts and relabel to your host's name
Jump to specific episodes when spot-checking
Make corrections without downloading/re-uploading files

You're not aiming for perfection. You're aiming for usable. If someone reads the transcript, can they follow the conversation? Can they find the episode through Google? That's the bar.

Step 5: Export for different uses

Not all transcripts have the same destination. You need different formats:

For SEO (website publishing): Export as TXT or HTML. Publish full transcripts on your website. This is the primary SEO value—searchable text on your domain.

For accessibility (podcast directories): Some podcast hosts accept transcript files for accessibility. Export as TXT and upload to your podcast host if they support it.

For video (YouTube uploads): If you're uploading old episodes to YouTube for video discovery, export as SRT (subtitle format). YouTube will automatically display captions.

For content repurposing: If you're extracting quotes for social media or turning episodes into blog posts, TXT works best. Copy-paste the sections you need.

Alfie exports to all these formats. You choose what fits each use case. One transcript, multiple formats.

Worked Example: 150-Episode Marketing Podcast

You run a marketing podcast. Two hosts, weekly guests. 150 episodes from 2019-2023. Average 50 minutes per episode. You want transcripts for SEO and to republish old episodes on YouTube with captions.

Friday evening (2 hours):

Download all 150 MP3 files from your podcast host
Organize into folders by year
Upload all 150 files to Alfie in one batch
Go to dinner—transcripts process while you're away

Saturday morning (4 hours):

Scan 10 random transcripts to identify patterns
Notice: speakers labeled "Speaker 1" and "Speaker 2" consistently
Notice: guest names often misspelled but hosts correct
Notice: "HubSpot" transcribed as "hub spot" in 40+ episodes
Notice: "Mailchimp" transcribed as "mail chimp" or "MailChimp"
Create systematic correction list

Saturday afternoon (3 hours):

Fix speaker labels: relabel "Speaker 1" to "Alex" (host), "Speaker 2" to "Jordan" (co-host)
Guest labels stay as-is (each episode has different guest, manual fixing not worth it)
Search all transcripts for "hub spot" → replace with "HubSpot"
Search for "mail chimp" → replace with "Mailchimp"
Spot-check 15 episodes (~10%) to verify quality

Sunday morning (2 hours):

Export all 150 transcripts as TXT for website publishing
Export all 150 as SRT for YouTube captions
Export and manage your transcripts with privacy-first defaults

Sunday afternoon (variable):

Upload TXT transcripts to your website (or have developer add them programmatically)
Upload SRT files to YouTube when republishing video versions

Total time: ~11 hours across a weekend for 150 episodes.

Compare to:

Manual transcription: 600+ hours, $4,000+ cost
One-at-a-time AI + editing: 75+ hours of tedious uploads/downloads

What to Fix vs. What to Ignore

You don't need perfect transcripts. You need good enough transcripts.

Fix these:

Speaker labels (critical for readability)
Brand names, product names (affects credibility)
Guest names (respect for your guests)
Technical terms that change meaning when wrong (e.g., "SEO" vs "CEO")

Don't waste time on:

Filler words like "um," "uh," "like" (natural speech, not errors)
Minor grammar issues in conversational speech
Perfect punctuation (comma vs period doesn't affect usability)
Exact timestamps (unless you're creating jump links)

The goal: someone reading the transcript can follow the conversation, understand key concepts, and attribute quotes correctly. That's it.

Privacy Advantage for Backcatalog Projects

When you upload 150 episodes at once, you're temporarily storing a lot of audio. With most transcription services, that audio lives on their servers indefinitely.

Alfie's privacy-first approach means you maintain control over your data. This matters for:

Old episodes with outdated info: Conversations from 2019 might include product details, company info, or personal details that are no longer current
Guest privacy: Guests from years ago deserve to have their audio handled responsibly
Data minimization: You only need the transcript, not permanent audio storage

When This Workflow Works

This batch approach makes sense when:

You have 30+ episodes to transcribe
Episodes have consistent hosts (same speakers across episodes)
You need transcripts for SEO, accessibility, or content repurposing
You want systematic editing, not one-off corrections

It doesn't make sense when:

You only have 5-10 episodes (just do them individually)
Every episode has completely different speakers (no patterns to exploit)
You're fine with raw AI output as-is

Start With Your First 10 Episodes

Don't try to transcribe 200 episodes on your first attempt. Start with 10.

Upload episodes 1-10. Edit them systematically. Export them. See if the workflow fits how you work.

If cleaning 10 episodes takes 90 minutes instead of 5+ hours, you've found a scalable process. Then do the next 20. Then the next 50.

Alfie offers a 2-hour free trial—no credit card required. Enough to transcribe ~8-10 typical podcast episodes and test the editing workflow.

Try Alfie with your first batch of old episodes. See if batch processing beats the one-at-a-time grind.