DEV Community: zerone01

Repurpose a Podcast into Blog Posts and Clips: A Workflow That Sticks

zerone01 — Tue, 15 Sep 2026 03:30:13 +0000

Every podcaster has run this experiment: take the episode, feed it to a tool, get "content" back. And every podcaster has seen the result — a blog post that reads like a terms-of-service summary, clips chosen by some virality score, tweets nobody would retweet.

The r/Podcasting threads say it plainly. Someone asking to "repurpose Youtube videos/podcasts as blog posts" (reddit_1ae6sg5). Someone else, having tried the tools: "Nothing I've tried has stuck" (1spl4rv). And a third describing the manual half-measure: "run the transcript through chat.gpt for clip recommendations" (reddit_1q8byod) — using the model as a clip scout, then doing everything else by hand.

"Nothing has stuck" is the signal. Repurposing tools fail not because generation is hard but because the workflow around generation doesn't fit how anyone actually works. Fixing that is possible. Here's the honest breakdown.

Why repurposing is worth the trouble

A 60-minute episode contains maybe 8,000-10,000 words of spoken content. That's a decent blog post's worth of material, five to ten short clips, a newsletter, and several social posts — all from content you already made. Creators who repurpose consistently report the derived assets drive meaningful discovery: blog posts rank in search long after the episode's release week, clips pull new listeners, show notes feed subscribers.

The failure isn't in the concept. It's in three specific gaps.

Where the current tools fail

Gap 1: They summarize instead of writing. Paste a transcript into a summarizer and you get an abstract of the episode — "the hosts discussed X, then Y" — which is accurate and unreadable. A blog post needs a point of view, structure, and the actual specific stories from the episode, not a neutral itinerary of topics.

Gap 2: Clip selection without ground truth. Tools that pick "viral moments" from audio energy or generic scores pick loud moments and punchlines out of context. The reddit_1q8byod approach — asking a model with the transcript for clip recommendations — works better because the model can read what was actually said. But then you're hand-building the transcript pipeline and hand-cutting the clips anyway.

Gap 3: One-off outputs. Each tool run starts from zero. Your show's vocabulary, recurring segments, guest names, tone — reconfigured every time. This is the "nothing has stuck" root cause: the per-episode setup tax exceeds the per-episode value.

The DIY path: a repurposing pipeline you own

For a working free version, structure it like this:

Get a corrected transcript first. Whisper or auto-captions, then fix proper nouns. Every downstream asset inherits these errors, so this step is load-bearing.
Blog post with a real prompt, not "summarize":

"Here is a podcast transcript. Write a 1,200-word blog post: pick the single strongest argument or story from the episode and build the post around it. First-person voice, short paragraphs. Include one specific story from the transcript with its details intact. Structure: hook naming a concrete problem, the story, the principle it illustrates, what to do about it. Don't cover every topic — depth over coverage. Embed the episode player where the post references it."

Clips from the transcript, not the waveform. Ask a model: "Identify 5-7 self-contained 30-90 second passages from this timestamped transcript that each make one complete, surprising point." Then cut those time ranges in your editor. Timestamped transcript is required — a model guessing timings gives you unusable cut points.

Social copy pulled from the best lines verbatim. Quote-format posts outperform generated "insights" because they carry the speaker's actual voice.

Batch it. Do all of this the day after the episode ships, in one sitting. The tools that "stuck" for people share one trait: they're attached to a fixed calendar slot, not used ad hoc.

Total DIY cost: 45-90 minutes per episode after setup. Workable, and many podcasters run exactly this. The recurring costs are transcript quality (accents and names corrupt everything downstream) and the timestamp trust problem in clip selection.

How postwriter.cn does it

The repurposing flow at postwriter.cn starts where the DIY path starts — an accurate, corrected transcript — and removes the assembly work. Upload the episode audio or video once. You get: the transcript, the blog-ready description and long-form draft, chapters with audio-true timestamps, title options, and social copy drawn from the transcript.

The clip-recommendation side works off timestamped text rather than audio heuristics, so suggestions map to real cut points you can jump to and verify. And the personal dictionary means your show's recurring names and terms are already correct in episode 40's transcript without you re-fixing them — the pipeline accumulates knowledge of your show instead of resetting weekly.

It's free during the beta period, $39 for 3 years at founder pricing after. The pricing philosophy is the same as the workflow philosophy: repurposing is an hour of leveraged work per episode, and the tool's job is to shrink that hour, not to rent it back to you at $29/month.

FAQ

Does Google index and rank content repurposed from podcasts?
Yes, if it's genuine text on a page. Transcript-dumped pages rank poorly; edited posts with structure and headers built from episode content rank like normal blog content. The difference is editorial effort on top of generation.

How many clips per episode?
5-7 from a 60-minute episode is the practical range. Fewer and you leave reach behind; more and quality control slips. Each clip should stand alone without episode context.

Blog post from the full transcript or just part of it?
Part. The strongest single thread, written properly, beats a complete summary. Save full transcripts for a separate "episode transcript" page if you want the long-tail search coverage — that page and the curated post serve different queries.

Does this work for video podcasts and YouTube-first shows?
Yes — upload the video and the same pipeline yields SRT captions, chapters, description, and clips. The YouTube-facing outputs and the blog-facing outputs come from one corrected transcript.

What about non-English podcasts?
The current beta is tuned for English audio including heavily accented speech. If your show is bilingual, the accented-English handling covered earlier in this series applies, and the personal dictionary learns show-specific terms regardless of accent origin.

How to Bulk Transcribe a YouTube Playlist or an Entire Back Catalog

zerone01 — Tue, 15 Sep 2026 03:24:15 +0000

There's a specific kind of regret that hits around video #300. You look back at years of uploads — interviews, tutorials, vlogs — and realize none of them have transcripts. Old videos with transcripts surface in search for long-tail queries. Old videos without them are invisible.

On Hacker News, the requests are blunt: someone asking to "convert a whole playlist in bulk" (hn_38236198), and someone describing the extreme case — "1,800 video diaries... don't have transcripts" (hn_34039362). Eighteen hundred. Even at a generous 30 seconds of handling per video, that's 15 hours of pure clicking before any transcription happens.

Bulk is a different problem from single-video transcription. Here's how to think about it.

Why back-catalog transcription pays

Search engines index text, not speech. YouTube auto-captions help somewhat, but they're error-prone, not downloadable as clean text, and not under your control. A corrected transcript on your own site — or a corrected caption file on the video — turns each old video into a page that can rank.

The math for a 300-video catalog: if transcripts add even 5-10 long-tail search visits per video per month, that's 1,500-3,000 incremental views monthly from content you already made. The work is one-time; the traffic compounds.

The catch is the word "one-time" — at scale, it's still 300 × (download + transcribe + correct + format + upload). Every step multiplied.

The options at scale

One video at a time via web tools. Most transcription SaaS is built for the single-upload workflow. Doing it 300 times means 300 uploads, 300 waits, 300 downloads — and at $12-29/month subscription tiers with per-minute caps, the pricing is designed against bulk.

Agencies and human transcription. At $1-3/audio minute, a 300-video catalog averaging 12 minutes each runs $3,600-$10,800. Non-starter for most creators.

YouTube auto-captions, en masse. Free and already there. Quality varies from okay to disgraceful, they live only on YouTube, and you can't cleanly export corrected versions.

The DIY path: a real bulk pipeline

For technical users, this is a solved problem if you write ~20 lines of glue:

Fetch the playlist. yt-dlp --flat-playlist -J "<playlist_url>" lists every video ID. For your own channel, the uploads playlist works the same way.
Download audio only, in a loop. yt-dlp -x --audio-format mp3 -o "%(id)s.%(ext)s" "<playlist_url>". Audio-only downloads are small — a 15-minute video is ~10-15 MB — so 300 videos fit on a modest drive.
Batch transcribe. Locally with Whisper large-v3 (slow on CPU: budget 1-2x real-time per video, so a full day of compute for a big catalog) or via an API like Groq's Whisper endpoint, which processes a 15-minute file in well under a minute and costs roughly a cent. A 300-video catalog via API: a few dollars.
Generate SRT files (whisper --output_format srt) and upload them to the corresponding videos — YouTube Studio allows caption upload per video, and there's an API for it if you're doing hundreds.
The correction problem. Here's where bulk DIY cracks. Step 3's output has the same proper-noun errors as single-video transcription, multiplied by 300. Nobody corrects 300 transcripts. In practice, DIY bulk means choosing between shipping known-wrong captions or never finishing.

That last step is the actual hard part of back-catalog work, and it's the one most tooling ignores.

How postwriter.cn handles bulk

Bulk mode is one of the three things postwriter.cn was built around. Paste a playlist URL — or upload a folder of files for off-YouTube content — and the whole thing queues. Each video comes back with transcript, SRT, description, chapters, titles, and social copy. You're reviewing outputs, not babysitting uploads.

The personal dictionary does the heavy lifting at scale. Correct "Marques" or "framework" once, and correction applies across the batch — including the videos you haven't reviewed yet, since later runs draw on everything learned from earlier ones. For the 1,800-video-diary extreme, that convergence is the difference between a catalog that gets more accurate as you process it and a catalog that's uniformly 85% accurate forever.

Pricing is built for bulk economics too: free during beta, $39 for 3 years at founder pricing. Compare a $19/month tool over three years — $684 — for the same batch capability with per-minute caps, and the arithmetic is blunt.

FAQ

Can I transcribe videos that aren't mine?
Technically yes for personal research use; the copyright rules for republishing transcripts of others' content are restrictive. For your own catalog, it's your content — no issue.

Does adding captions to old videos actually improve their performance?
Yes, typically. Caption files add indexable text, and old videos with new metadata often see search rediscovery within weeks. It's one of the highest-ROI actions available on a back catalog.

How long does bulk transcription take?
With API-based processing, roughly real-time for the whole batch divided by concurrency — a 40-hour catalog can process in under an hour of wall time. Local CPU Whisper is far slower; GPU changes that. The human review pass is usually the bottleneck, which is why dictionary-based convergence matters at scale.

What about podcast back catalogs?
Same pipeline, audio-only. RSS feeds make fetching even easier than YouTube playlists, and the transcripts feed show notes and blog repurposing directly.

Is $39/3 years sustainable pricing or a teaser?
It's a founder-tier price for early users, stated plainly: the first cohort pays $39, locking three years, and the point is to price against the $12-29/month standard rather than match it. Whether the product earns renewal in year four is the bet we're making.

How to Bulk Transcribe a YouTube Playlist or an Entire Back Catalog

zerone01 — Tue, 15 Sep 2026 03:18:11 +0000

Bulk is a different problem from single-video transcription. Here's how to think about it.

Why back-catalog transcription pays

The catch is the word "one-time" — at scale, it's still 300 × (download + transcribe + correct + format + upload). Every step multiplied.

The options at scale

Agencies and human transcription. At $1-3/audio minute, a 300-video catalog averaging 12 minutes each runs $3,600-$10,800. Non-starter for most creators.

YouTube auto-captions, en masse. Free and already there. Quality varies from okay to disgraceful, they live only on YouTube, and you can't cleanly export corrected versions.

The DIY path: a real bulk pipeline

For technical users, this is a solved problem if you write ~20 lines of glue:

Fetch the playlist. yt-dlp --flat-playlist -J "<playlist_url>" lists every video ID. For your own channel, the uploads playlist works the same way.
Download audio only, in a loop. yt-dlp -x --audio-format mp3 -o "%(id)s.%(ext)s" "<playlist_url>". Audio-only downloads are small — a 15-minute video is ~10-15 MB — so 300 videos fit on a modest drive.
Batch transcribe. Locally with Whisper large-v3 (slow on CPU: budget 1-2x real-time per video, so a full day of compute for a big catalog) or via an API like Groq's Whisper endpoint, which processes a 15-minute file in well under a minute and costs roughly a cent. A 300-video catalog via API: a few dollars.
Generate SRT files (whisper --output_format srt) and upload them to the corresponding videos — YouTube Studio allows caption upload per video, and there's an API for it if you're doing hundreds.
The correction problem. Here's where bulk DIY cracks. Step 3's output has the same proper-noun errors as single-video transcription, multiplied by 300. Nobody corrects 300 transcripts. In practice, DIY bulk means choosing between shipping known-wrong captions or never finishing.

That last step is the actual hard part of back-catalog work, and it's the one most tooling ignores.

How postwriter.cn handles bulk

FAQ

What about podcast back catalogs?
Same pipeline, audio-only. RSS feeds make fetching even easier than YouTube playlists, and the transcripts feed show notes and blog repurposing directly.

YouTube Chapters Generator: Timestamps That Match the Audio

zerone01 — Sun, 13 Sep 2026 03:32:10 +0000

Somewhere in the past year, chapters went from nice-to-have to default expectation. Viewers skim them. YouTube surfaces them as key moments in search results. And every creator learned the same tedious truth: making them means scrubbing.

On r/NewTubers, one user described their workaround with visible relief: "I've even managed to get reliable chapters with timings from claude.io" (reddit_122k4xs) — the phrasing "managed to" doing a lot of work. On Hacker News, a commenter reviewing a transcript-based tool put it more directly: "光为chapters就愿意用" — roughly, "I'd use it for the chapters alone" (HN thread 30454639).

Chapters alone. That's the whole product for some people. Here's why they're painful and how to stop paying the scrubbing tax.

What chapters actually require

A chapter is a timestamp plus a label, formatted 00:00 Intro in the description, with at least three chapters and the first at 00:00. The hard part isn't the label — an LLM writes labels fine. The hard part is the timestamp being true.

A timestamp that's 40 seconds off is worse than no chapters. Viewers click "Setting up the API key," land mid-sentence in an unrelated section, and bounce. YouTube's key-moments surfacing in search amplifies the damage — the wrong timestamp gets shown to people who haven't even clicked yet.

And true timestamps are exactly what a chat interface can't give you. Paste a transcript into any LLM and ask for chapters, and it will produce confident, plausible, frequently wrong times. The model doesn't hear the audio. It pattern-matches where introductions usually are.

Current options

Manual scrubbing. The standard. For a 20-minute video, expect 20-40 minutes of marking in-and-out points, writing labels, and re-checking. Per video. Forever.

YouTube's automatic chapters. Free, zero effort, and noticeably vague — labels like "Introduction" and "Main content" that carry no keywords and no click appeal. Frequently disabled for smaller channels anyway.

AI chat with pasted transcript. Good labels, unreliable times, for the reason above. The "managed to get reliable chapters" crowd has usually constructed some careful pipeline where the timestamps come from somewhere else and only the labels come from the model.

Dedicated chapter tools. Exist, work, and cost $12-29/month on subscription — a strange price for a task that takes a script five minutes.

The DIY path: chapters with honest timestamps, free

The reliable pattern is to make timestamps come from data, not from a language model's guess:

Generate a timestamped transcript. Whisper emits segment-level and word-level timings: whisper audio.mp3 --model large-v3 --word_timestamps True. YouTube's auto-caption VTT (via yt-dlp --write-auto-sub) also carries timings, at lower text accuracy.
Cut the transcript into 30-90 second windows using the segment boundaries. Don't cut mid-sentence — merge to sentence ends.
Feed the windows to an LLM with this instruction: "Here are timestamped windows from a transcript. Group adjacent windows into 4-10 chapters. Return each chapter as MM:SS Label using the timestamp of its first window. Labels: specific, under 40 characters, keyword-bearing. Do not alter timestamps."
Spot-check the first and last chapter boundaries by jumping to those times in your editor. This is the 60 seconds of QA that catches grouping mistakes.
Paste into the description with 00:00 as the first entry (YouTube requires the first chapter at zero).

That pipeline produces trustworthy chapters for the cost of a Whisper run and a couple of API calls. It's genuinely fine. Its failure point is setup friction — getting from "I have a video" to "I have a word-timestamped transcript" is still the expensive step, especially if your audio has accents or unusual names that corrupt the segmentation.

How postwriter.cn does it

Chapters are one output of the main pipeline at postwriter.cn. Upload the video once; the audio alignment produces word-level timings, chapter breaks are chosen from those timings, labels are written from the corrected transcript. The timestamps are read from the audio, not guessed, which is the entire difference between chapters that help and chapters that mislead.

Since the transcript feeding the chapter labels is the same corrected transcript that drives your description, SRT, and social copy, the proper nouns stay consistent across everything — the chapter says the same guest name the description says.

The correction loop matters here too: when you fix a misheard term on the review page, it enters your personal dictionary, so your next upload's chapters are built on a transcript that's already more accurate. Chapters for video 30 take less of your attention than chapters for video 3.

Free in beta. $39 for 3 years at founder pricing — and per the HN commenter above, chapters alone can carry that value.

FAQ

Do chapters affect YouTube SEO?
Indirectly but really. YouTube can surface chapters as "key moments" in search results, effectively multiplying your titles. Chapters also improve average view duration on longer videos by letting viewers skip to relevant sections, and watch-time signals feed ranking.

How many chapters should a video have?
One per 1-3 minutes of video as a rule of thumb, minimum three (YouTube's floor for enabling chapters), each chapter at least 10 seconds long. A 15-minute video: 6-12 chapters.

Why do my chapters not show up?
The three most common causes: first timestamp isn't 00:00, fewer than three chapters, or a timestamp in the list doesn't match a real moment (YouTube validates loosely, but formatting errors like 0:00 vs 00:00 in mixed styles can break parsing).

Are auto-generated chapters from YouTube good enough?
For search signal, marginally. For viewers, no — generic labels get few key-moment clicks, and you don't control what YouTube picks. Specific, keyword-bearing labels out-perform.

Can I get chapters from just an audio file, no video?
Yes. The pipeline only needs audio. Podcasters use the same chapter output for show notes and episode markers.

Accented English Transcription: Subtitles for Non-Native Speakers That Don't Mangle Your Words

zerone01 — Sat, 12 Sep 2026 03:36:00 +0000

A creator on r/Transcription posted a test that's become something of a benchmark: a video in a strong Scottish accent, where the big transcription engines came back at "50-55% accuracy" (reddit_135ljil). Half the words, wrong. On a different thread, a Dutch YouTuber quoted a viewer comment — "thats why god invented subtitles" — as the reason they subtitle everything, including their own English (reddit_1fqxxug).

Scroll any creator subreddit and you'll find the same anxiety in at least six variations: Indian, Nigerian, Filipino, French, Korean, Eastern European accents, all asking versions of "will auto-captions butcher my English?" The comments under accented videos are blunter: "couldn't understand a word, unsubbed."

Here's the uncomfortable truth, then the workable path.

The problem is structural, not your accent

Modern speech recognition is trained on data that skews toward standard American and British English. The error rate on accented speech runs 2-3x higher across the board — this shows up consistently in published benchmarks on Whisper and the major cloud APIs. Proper nouns make it worse. A Korean-accented video mentioning "Seongsu" or a Nigerian creator saying "Abuja" gets both problems stacked: accent plus out-of-vocabulary vocabulary.

Add the math of manual fixing: a 15-minute video at 50-55% accuracy means roughly 1,200 wrong words. Fixing that by hand takes longer than editing the video did.

So non-native creators end up choosing between three bad options.

The current options, ranked by pain

YouTube auto-captions. Free, instant, and for accented speech frequently unusable. There's no way to teach it your vocabulary — you can correct the captions YouTube stores, but the recognizer itself never learns from your fixes.

Generic AI transcription. Whisper large-v3 is genuinely better on accents than YouTube's engine, and some cloud APIs have accent-optimized models. But the proper-noun problem stays. Every transcript comes back with "Seoul's shoe district" or whatever your recurring names get mangled into, and you fix the same errors in every single video.

Human transcription. $1-3 per audio minute. A 20-minute video costs $20-60. Accurate, and completely uneconomic for a channel publishing weekly.

No subtitles at all. The option many pick. It costs you viewers — deaf and hard-of-hearing audiences, people watching on mute, and the large share of non-native English speakers who read along while listening. For a channel targeting an international audience, subtitles usually raise watch time, not lower it.

The DIY path: squeezing accuracy out of Whisper

If you're technical, you can get accented English transcription to usable quality without paying anyone:

Use large-v3, not the smaller models. The accuracy gap on accents between large-v3 and medium is significant. If you can't run it locally, run it through an API — Groq's Whisper endpoint does large-v3 at real-time-ish speeds for pennies.
Feed an initial_prompt. Whisper accepts a 200-ish character hint that biases decoding. Put your recurring vocabulary in it: "A video about mobile phones in Seongsu, Seoul. Brands: Samsung, Apple. Guests: Kim Minjun." This alone fixes a surprising share of proper-noun errors.
Set condition_on_previous_text=False for long videos. Otherwise one bad segment contaminates the next, and errors compound.
Fix the transcript once, then reuse it. The corrected text drives your captions, description, chapters, and social posts. Never correct twice for two outputs.
Ship SRT, don't burn in. Uploaded SRT files can be fixed later; burned-in captions can't, and they degrade your video for re-edits.

That workflow gets you to maybe 90-95% accuracy on a strong accent. The remaining 5-10% is the recurring-name tax again: the same fifteen words, wrong, every video, forever.

How postwriter.cn deals with it

postwriter.cn was built partly by people who speak English as a second language, for exactly this reason.

The review page aligns the transcript to your audio word by word. Wrong word → click → type the right one → done. The correction goes into your personal dictionary. The next video you upload starts from everything the system learned from the last one. Your accent, your names, your recurring guests, your niche vocabulary — the error rate drops with use instead of resetting. That's the core thing generic engines can't offer: they re-mishear you every time; this converges on you.

Output is everything at once: corrected SRT for upload, description, chapters with real timestamps, titles, social copy. Free during beta. Founder pricing after: $39 for 3 years. One month of a typical competitor at $19/month costs more than a year of this.

FAQ

Why are auto-captions so bad with accents?
Training data imbalance. The major models learned from hundreds of thousands of hours dominated by standard American/British speech. Accent variations get mapped onto the closest familiar sound patterns, which produces confident nonsense.

Do subtitles help or hurt non-native creators?
Help, in most measured cases. You keep viewers who'd bounce on audio comprehension alone, and you keep the mute-scrollers. The audience reading your subtitles is disproportionately international and high-retention.

Burned-in captions or SRT files?
SRT. YouTube treats uploaded caption files as additional text signal, you can correct them post-publish, and your source video stays clean for repurposing. Burn-ins only for platforms without caption support, like some short-form feeds.

Can I fix YouTube's auto-captions instead?
You can edit them in YouTube Studio, and it's worth doing for old videos with traffic. But the fixes don't teach the engine anything — video 200 starts from the same mistakes as video 1. That's the loop worth escaping.

YouTube Description Generator from Transcript: What Actually Works

zerone01 — Fri, 11 Sep 2026 03:24:04 +0000

There's a small ritual most creators know by heart. You finish editing at 1 a.m., hit upload, and YouTube shows you an empty description box. You stare at it. You type "In this video, I..." — delete it — type something else — delete that too.

On r/NewTubers, someone described their workaround: "adding the subtitles into the query for context and it generates... descriptions" — meaning they pull the auto-captions, paste them into an AI chat, and ask for a description (reddit_17fy762). Another thread asked the simpler question: "What do you personally put in your description?" The answers ranged from keyword soup to two-line summaries to "nothing, honestly."

That gap — between knowing the description matters and actually writing one at midnight — is why description generators exist. Most of them don't work well. Here's why, and what to do instead.

Why the description box is harder than it looks

The first 150 characters of your description appear in search results and next to the title on watch pages. Everything below the fold still gets crawled for keywords. YouTube's own guidance says descriptions contribute to search ranking — not as heavily as title and thumbnail, but enough that a blank description is a real handicap for search traffic.

So a good description has to do three jobs at once:

Hook a browser in the first two lines
Carry the keywords people actually search for
Stay accurate to what's in the video

Job 3 is where most tools fail. And it's the one that matters, because a description that promises content the video doesn't deliver hurts retention, not just rankings.

The current options, and their failure modes

Write it manually. Best quality, worst economics. A decent description takes 10-15 minutes per video if you're disciplined. If you publish daily, that's over an hour a week on text nobody thanks you for.

Template copy-paste. "Hey guys, welcome back to my channel!" repeated across 200 videos. Viewers skim past it, and it carries zero video-specific keywords.

Paste the transcript into a general AI chat. This is the reddit_17fy762 approach. It works, sort of. Three problems:

Context limits. A 30-minute video transcript runs 4,000-5,000 words. Long videos get truncated, and the model summarizes the first half.
Hallucinated details. Ask for timestamps and chapters in the same pass and the model invents them. LLMs can't hear audio; they guess plausible-looking times like "02:45 Introduction" whether or not that's when the introduction happens.
Generic output. The model doesn't know which parts of your video were the interesting parts. You get an even-weighted summary.

Dedicated description generator tools. SaaS products in this space charge $12-29/month. Many of them are a thin wrapper around the exact paste-transcript workflow above, with a nicer form. If you publish 4 videos a month, that's $3-7 per description.

The DIY path (no product needed, honest version)

If you want to do this properly for free, here's a workflow that holds up:

Get the transcript. yt-dlp --write-auto-sub --skip-download <url> pulls YouTube's auto-captions as a .vtt file. For better accuracy, run the audio through Whisper locally (whisper audio.mp3 --model large-v3) or an API.
Fix the proper nouns before generating anything. Auto-captions will mangle your product names, guest names, and technical terms. Two minutes of fixing here saves you from publishing a description with the wrong product name in it.
Prompt with structure, not vibes. A prompt that produces usable output looks like:

"Here is a transcript with timestamps. Write a YouTube description: first 2 lines = a specific hook naming the concrete outcome or problem (under 150 chars total). Then 3-5 sentences summarizing what's covered. Then a keyword line: 5-8 search phrases a viewer would type. Only reference topics that appear in the transcript. Do not invent timestamps."

Keep a personal template for the evergreen part — links, socials, disclosure — and paste the generated part above it.

Total time after setup: 5-8 minutes per video. The failure point is step 2. Do it for ten videos and you'll notice you're fixing the same ten misrecognized names every single time. That repetition is the tax that never goes down.

How postwriter.cn handles it

We built postwriter.cn around that exact repetition problem. Upload the video; you get the transcript, a description draft, chapter timestamps, title options, social copy, and an SRT file in one pass.

The part that matters for descriptions is the review page. The transcript is aligned to the audio word by word, so you click any wrong word, fix it, and the fix goes into your personal dictionary. If the engine misheard "Vercel" as "for sell" in video 1, you fix it once, and in videos 2 through 50 it's already right. The system converges instead of making you re-correct the same names every upload.

Descriptions are generated from that corrected transcript, so what's in the box matches what's in the video. Timestamps are read from the audio alignment, not guessed. It's free during beta, and the founder price after that is $39 for 3 years — priced against the reality that this is a 5-minute task, not a $29/month task.

FAQ

Does the description affect YouTube SEO?
Yes, modestly. Title, thumbnail, and watch time matter more, but descriptions are crawled for keywords and the first 150 characters show in search. A blank or generic description leaves ranking signal on the table.

How long should a YouTube description be?
200-400 words is the practical range. The first 2 lines do the CTR work; the rest carries keywords and links. Past ~500 words, you're writing for robots that don't reward it.

Should I put keywords as a comma list at the bottom?
A short natural phrase list is fine and common. A 500-tag wall looks like spam to viewers who expand the description, and YouTube has said keyword stuffing in descriptions is treated as spam.

Can't I just use ChatGPT for free?
You can, and the DIY section above is exactly that workflow done carefully. What you're paying a tool to remove is the transcript-wrangling, the timestamp hallucinations, and the repeated proper-noun fixes. Whether that's worth it depends on your volume.

Does it work for languages other than English?
The current beta focuses on English content, including heavily accented English — that's covered in detail in the next section of this series.

A Descript Alternative That's Cheaper: Full Breakdown

zerone01 — Thu, 10 Sep 2026 03:24:01 +0000

A redditor on r/VideoEditing didn't hedge: "descript is by far the absolute worst… far better ones… much cheaper and more accurate" (reddit.com/r/VideoEditing/comments/17z5bkh). The thread underneath is the usual mix — defenders, deserters, and people listing what they switched to.

Strong opinions aside, the underlying complaint is consistent across every one of these threads: Descript costs $12-24/month depending on tier, transcription accuracy on real-world audio still needs a manual cleanup pass, and if you stop paying, your workflow stops. This is for people who like text-based editing and transcription but not the bill. Honest options below, including the cheap ones and the free ones.

What Descript actually charges for

Current published tiers run roughly $12/month (Hobbyist, annual) to $24/month (Creator) and beyond for teams. What you get: transcription, text-based audio/video editing, filler-word removal, studio sound, screen recording. It's genuinely good software. The complaints are about price durability and accuracy:

The transcription misrecognizes names and niche terms, and re-misrecognizes them on every future project. Corrections don't accumulate.
The features people actually use weekly — transcribe, edit text, export — are maybe 20% of the product. The rest is paid dead weight for many users.
Monthly pricing compounds: 3 years at $12-24/month is $432-864.

The cheaper alternatives, honestly compared

CapCut. Free tier with text-based editing and auto-captions, Pro tier cheap. The tradeoffs: the free tier keeps shrinking (see every 2024-2026 Reddit thread on the subject), exports have restrictions, and cloud dependency makes long-term reliability a question. Good for short-form; long-form podcast editing is cramped.

Gling. AI-first video editor aimed at YouTubers — cuts silences and bad takes from raw footage, exports to Premiere/Final Cut/Resolve. Cheaper than Descript, does the rough-cut job well, but it's an editing assistant, not a transcription/show-notes pipeline.

Firecut. Premiere plugin doing silence removal, captions, chapter markers inside your existing editor. If you live in Premiere, this keeps your timeline and your costs down. Same gap: no show-notes/description output, no persistent corrections.

Pure DIY (Whisper + editor). Free and accurate-ish: faster-whisper for the transcript, ffmpeg or your NLE for edits, Subtitle Edit for caption cleanup, an LLM for show notes. Cost: $0. Bill: paid in minutes — realistically 30-60 minutes per video of glue work, every video, forever.

postwriter.cn. The new entrant, currently in free beta. It doesn't try to be an editor — no timeline, no filler-word removal. It covers the production side around the edit: upload the video, get transcription, SRT subtitles, chapter timestamps, description draft, title options, and social copy.

Two things it does differently from everything above. First, the review page aligns every word to the audio, and every correction you make goes into a personal dictionary. Episode 20's transcript needs far fewer fixes than episode 1 — accuracy converges on your vocabulary instead of resetting. Second, batch mode processes playlists and back catalogs in one job, which is where per-video tools and DIY scripts both get tedious.

Pricing, when the paid tier opens: $39 for 3 years at founder pricing, against $432-1,044 for three years of the $12-29/month tools. Free beta before that.

Matching the tool to the actual job

The right pick depends on which part of Descript you were using:

Most used text-based editing of raw footage? Gling or Firecut, or stay in your NLE with a transcription plugin.
Most used captions/subtitles? Any of the above plus a dedicated generator; see our subtitle and video-to-SRT guides for the free paths.
Most used the transcript as raw material — show notes, chapters, social posts, clips? That's postwriter.cn's exact lane: one upload produces all of it from one corrected transcript, and the dictionary means the correction work shrinks over time.
Using Studio Sound / remote recording? None of the cheap alternatives fully replace those; pair Riverside/SquadCast free tiers with any transcription pipeline.

The stack most cost-sensitive creators land on

From the thread patterns: record in whatever you already have, edit in a cheap or owned NLE, and generate transcripts/captions/show notes with a low-cost pipeline. The expensive all-in-ones get dropped first, and what people miss least is the subscription itself — what they miss is having to re-fix the same names every episode. Any tool that fixes that (persistent dictionary, or your own scripted pipeline with a custom vocabulary list) closes the real gap.

FAQ

Is postwriter.cn an editing tool like Descript?
No. It handles what surrounds the edit — transcript, SRT, chapters, description, titles, social copy — from one upload. Pair it with any editor.

What does it cost after beta?
$39 for a 3-year founder term. Descript-comparable monthly tools run $12-29/month.

Does it really get more accurate over time?
The personal dictionary stores every correction you make in the audio-aligned review page. Recurring terms — names, products, jargon — come out right on later uploads without re-fixing.

Can it batch process an existing channel?
Yes — playlist and back-catalog batch mode is one of the core features, built for exactly the "80 old videos, no captions" situation.

What about speaker separation?
Two-speaker shows work well; large roundtables degrade. Verify labels during review — the audio alignment makes that a click-per-word job rather than scrubbing a timeline.

How to Generate Show Notes from a Transcript (Without Copy-Pasting Into ChatGPT 40 Times)

zerone01 — Thu, 10 Sep 2026 03:18:03 +0000

From the same r/podcasting thread on transcription workflows (reddit.com/r/podcasting/comments/135ljil), a reply that describes half the industry's process: "Show notes are done by feeding portions of my transcript into Chat GPT."

If you've done this, you know the pain. A 60-minute episode produces a 9,000-word transcript. That's over the context window you want to use, or at least over the amount you trust the model to actually read. So you chunk it. Paste 2,000 words, ask for notes, paste the next chunk, ask again, then paste the summaries together and ask for a merge. Forty minutes of clipboard work per episode, and the output still says "in this episode, we delve into" every third paragraph.

This is a solved problem if you structure it right. Here's the honest breakdown.

What good show notes actually contain

Before automating anything, know the target. Show notes that get used:

A 2-3 sentence summary — what the listener walks away with, not "we had a great conversation."
Timestamped chapters — "04:30 Why the pricing failed — 17:12 The Singapore launch." The single highest-value block; listeners and apps both use it.
Key takeaways — 3-6 bullets, each self-contained.
Guest bio + links — one line, links to their stuff.
Resources mentioned — list with URLs.
A subscribe CTA — one line, not three paragraphs.

That's it. Most show notes fail by being either a raw transcript dump or AI foam with no timestamps.

The DIY pipeline: transcript to show notes

This works with any transcript source and any decent LLM. The trick is chunking with overlap and extracting structure, not prose.

Step 1: Get an accurate transcript. Everything downstream inherits its errors. If the guest's name and company are wrong in the transcript, they're wrong in your notes, your title, and your social posts. Whisper locally, or a transcription tool — the important part is fixing names before generation, not after.

Step 2: Chunk with context. Split the transcript into ~10-minute segments at natural topic boundaries. Include the previous segment's last few lines as overlap so the model has continuity.

Step 3: Extract per-chunk. For each chunk, prompt for: topics covered, timestamp ranges, standout quotes (verbatim, with speaker), and any resources mentioned. Extraction prompts beat "summarize this" prompts because they return structure you can verify against the audio.

Step 4: Merge. Paste all chunk outputs into a final prompt: assemble chapters, pick the 3-6 strongest takeaways, draft a 2-sentence summary, list resources. Give it your format template so output is consistent week to week.

Step 5: Verify quotes and timestamps. This is the step everyone skips. LLMs drift timestamps and quietly edit quotes. Spot-check at least the quotes you plan to use in social clips.

Time cost once practiced: 30-45 minutes per episode. The clipboard juggling is real, but it's free and you control the output format exactly.

The recurring costs that push people to tools

Running that pipeline weekly, three things grind:

Transcript cleanup repeats. Same names, same jargon, same fixes, every episode.
The pipeline is manual. Chunking, pasting, merging — none of it is hard, all of it is minutes you pay weekly.
Timestamps drift. If your transcript timestamps aren't word-accurate, every chapter marker you publish is off by a few seconds, and listeners notice.

This is the gap an AI show notes generator is supposed to fill. postwriter.cn's version works from one upload: video or audio in, and you get the transcript, chapter timestamps, description, title options, social copy, and the show notes structure — in one pass.

Two design choices matter more than the feature list. First, the review page aligns text to audio word by word: click any word, hear exactly what was said, correct it. Corrections feed a personal dictionary, so your guests' names and recurring terms stop being errors after the first fix. Second, everything is generated from the same corrected transcript, so your chapters, quotes, and social posts all agree with each other.

It's free during beta, with founder pricing at $39 for 3 years when paid tiers open. For the ChatGPT-chunking crowd, the math is mostly about the 40 minutes per episode of clipboard work, plus $20/month if you were paying for a chat subscription mainly for this.

One template to steal

If you stay DIY, use this merge prompt (adjust to your show):

You are assembling show notes from segment extractions of one podcast episode. Input: per-segment outputs below. Output: (1) 2-sentence summary, (2) chapter list as MM:SS — title, merging duplicate topics, (3) 3-6 key takeaways as bullets, each ≤20 words, (4) up to 3 verbatim quotes with speaker attribution, (5) resources list. Do not invent anything not present in the input. Use plain language, no filler phrases.

The last two sentences do most of the work.

FAQ

How long should show notes be?
150-400 words plus timestamps and links. Long-form show notes (800+) work for some niches, but skim data shows most readers use the chapters and stop.

Do timestamps help SEO?
They give apps and Google structured entry points to your episode, and they appear in search snippets for podcast results. Cheap to include, non-zero return.

Can I generate show notes from YouTube auto-captions?
You can, but you inherit their errors — names and niche terms especially. Clean the transcript first or the mistakes propagate everywhere.

What does postwriter.cn cost after beta?
Founder pricing is $39 for a 3-year term. Comparable tooling in this space runs $12-29/month.

Podcast Transcription and Timestamps: A Workflow That Doesn't Eat Your Week

zerone01 — Wed, 09 Sep 2026 03:30:01 +0000

On r/podcasting, someone asked the question every podcaster eventually hits: "how do you handle your podcasts transcription and timestamps" (reddit.com/r/podcasting/comments/135ljil). The thread ran 25 replies deep, and the answers split into camps: manual note-takers, Descript users, and people piping transcripts into ChatGPT. One reply stood out: "Show notes are done by feeding portions of my transcript into Chat GPT."

Another number from that thread and others like it: podcasters using Descript reported spending 1-2 hours per episode cleaning up the transcript before it was usable. For a weekly show, that's a full workday a month on cleanup alone.

Here's the full picture: why transcripts and timestamps matter, what the options cost, and how to spend less than an hour per episode on all of it.

Why transcription and timestamps are worth doing at all

Three concrete reasons:

Search. Podcast apps now index transcripts. Apple Podcasts auto-generates them from iOS 17 onward; Spotify does something similar. An accurate transcript makes your episode findable by content, not just title.
Skimmability. Listeners deciding whether to commit 60 minutes scan chapters first. Timestamps in the shownotes let them jump to the segment they care about — which paradoxically increases total listening.
Repurposing. Every quote, thread, and clip you post comes from the transcript. No transcript, no clips, no threads, no newsletter. The transcript is the raw material for everything else.

The current approaches and their failure modes

Manual transcription

Nobody does this anymore except for short segments. A 60-minute episode takes 4-5 hours to type by hand. Dead on arrival.

Descript

Good editor, real text-based audio editing. But the subscription runs $12-24/month depending on tier, and transcription accuracy on names and niche terms still needs that 1-2 hour cleanup pass users describe. You're paying monthly for software whose accuracy doesn't improve with use — every episode starts the same name-mangling from zero.

Free auto-transcription in hosting platforms

Some hosts (Buzzsprout, Transistor, others) auto-generate transcripts. Accuracy is decent on clean audio. But they're flat text — no timestamps you can turn into chapters, no way to fix errors that then persists, no export pipeline into show notes.

Whisper locally

Free, accurate, yours. But the output needs post-processing: speaker labels, chapter breaks, formatting. You become your own QA department.

The DIY workflow (fully free, ~40 min/episode once practiced)

If you want to build this yourself:

Transcribe: faster-whisper locally, or whisper.cpp if you're on CPU. A 60-minute episode takes 5-10 minutes on a recent GPU.
Find chapter points: Scan the transcript for topic shifts. If your episodes follow a segment structure, note the timestamps at each segment boundary. Some podcasters tap a marker in Audacity or Reaper while recording — chapter points captured live are more accurate than ones reconstructed later.
Embed chapters: Use a tool to write ID3 CHAP frames into the MP3 (aacgain's chapter tools, or the Chapters tool in Hindenburg / Ubercaster). Apps that support CHAP display chapters natively.
Timestamps in show notes: Paste the list into your episode page — "02:14 Guest intro — 15:40 The pricing story — 38:12 Q&A" works even where CHAP isn't supported.
Show notes: Feed transcript chunks to an LLM with a consistent prompt template (structure covered in detail in our show notes guide).

The recurring cost is steps 2 and 5 — judgment work the machine can't do — plus the cleanup of misrecognized names and jargon that no model gets right on the first pass.

Where a tool earns money: corrections that stick

The structural problem with every option above is that they're amnesiacs. Descript, hosting-platform transcripts, raw Whisper — none of them remember that your co-host's name is spelled "Siobhán," that your show is about "Sigma lenses" not "sigma lessons," that "the K8s rollback story" is a real segment. You fix the same errors on episode 30 that you fixed on episode 3.

postwriter.cn approaches this differently. The review page aligns every word to the audio — click a word, hear it, fix it. Each correction goes into a personal dictionary attached to your account. Next episode, those terms come out right the first time. Error cleanup shrinks episode over episode instead of resetting.

One upload also returns, alongside the transcript: chapter timestamps, description draft, title options, and social copy. It's in free beta. For back catalogs, the batch mode processes whole playlists rather than one upload at a time — relevant if you're adding transcripts to 80 old episodes.

When the paid tier opens, the founder pricing is $39 for 3 years. That math against a $12-29/month tool over the same 3 years is roughly $39 vs $432-1,044.

FAQ

Do podcast apps show chapters automatically?
Apple Podcasts and several others read ID3 CHAP frames. Spotify reads its own chapter format via its creator tools. Always mirror timestamps in the show notes text as a fallback.

How long does transcription take?
With modern ASR backends, a 60-minute episode processes in minutes. The human time is in review: 10-20 minutes with a dictionary-corrected transcript, longer starting from raw output.

Speaker labels?
Diarization (who spoke when) is the hardest part of podcast ASR. Most tools, including ours during beta, handle 2-speaker shows well and degrade on roundtables. Verify labels during review.

Is a transcript enough for SEO, or do I need timestamps?
Both. The transcript gives apps and Google text to index; timestamps give skimmers entry points. Episodes with both consistently outperform transcript-only in browse features in most podcasters' reporting.

Free Subtitle Generator for YouTube: 6 Options That Still Work

zerone01 — Wed, 09 Sep 2026 03:24:02 +0000

A creator on r/VideoEditing posted a simple question: "What's a good free app to add subtitles to footage?" (reddit.com/r/VideoEditing/comments/1b891um). The top-voted frustration in the replies wasn't about features. It was about money: "Capcut is not free anymore."

That thread is two years old and the situation has gotten worse, not better. Tools that were free in 2023 now watermark exports, cap you at 3 exports a month, or move basic features behind a Pro badge mid-project. So here's an honest map of what's actually free right now, what each option costs you in time, and where the traps are.

Why burned-in subtitles matter for YouTube (briefly)

Retention data from most creator analytics dashboards tells the same story: a large share of viewers watch on mute. Feed preview thumbnails autoplay silently. If your first 3 seconds have no text on screen, a chunk of your audience never hears you at all.

YouTube also indexes caption text for search. A clean SRT file uploaded to your video gives you keyword-bearing text that the algorithm can read. Auto-generated captions do this too, but they mangle product names and niche terms, which means the indexed text is partially garbage.

So the goal is two things: readable on-screen text, and an accurate caption file. Ideally from one pass.

The current free options, ranked by annoyance

1. YouTube's built-in caption editor

Free, built in, zero setup. YouTube auto-generates captions within hours of upload, and the caption editor lets you fix them line by line.

The problems: accuracy on accents, background music, and niche vocabulary runs anywhere from "fine" to "embarrassing." Fixing a 15-minute video by hand takes 30-60 minutes. And you can't style the captions — they look like every other auto-caption on the platform. Fine for search indexing, bad for brand.

2. CapCut's free tier

Still usable, but shrinking. Exports carry restrictions that have tightened over time, and features move to Pro without warning. If you edit in CapCut anyway, the auto-caption feature is right there. If you're choosing a workflow specifically around it, building on a shrinking free tier is a risk. Several replies in that same Reddit thread were people who built their workflow on CapCut and got stranded.

3. Free online subtitle generators

Do a search and you'll find a dozen. Most follow the same pattern: upload video, get captions, then discover the free plan gives you a watermarked file, a 5-minute length cap, or 2 exports. Some are fine for one-off short clips. For a weekly upload schedule, they fall over fast.

4. Local Whisper (the free option nobody wants to set up)

OpenAI's Whisper models run on your own machine. faster-whisper on a mid-range GPU transcribes a 15-minute video in 2-4 minutes, free, unlimited, no upload. On CPU it's slower but works.

The catch: you get raw text with timestamps, not a finished subtitle track. Segmentation is clumsy — lines break at odd points, too long for comfortable reading. You'll spend time in a subtitle editor fixing line breaks and reading speed. If you're comfortable in a terminal, this is the cheapest solid option. If not, keep reading.

5. Subtitle Edit (free, Windows)

A proper desktop subtitle editor. It can run Whisper from inside the app, shows waveforms, and fixes timing visually. The interface looks like 2009. It's powerful and it's genuinely free, but the learning curve is real. Expect a couple of hours before you're fast in it.

6. A dedicated generator with a review step

This is the category postwriter.cn sits in. You upload the video, get back transcription plus subtitle-ready SRT, and — the part that matters — a review page where the text is aligned to the audio, so you click any word, hear exactly what was said, and fix it in place.

Why that review step is the whole ballgame: the errors Whisper-type models make are repetitive. Your channel name, your guests' names, the products you review, local place names. You fix the same 30 words every single video. A review page that records your corrections into a personal dictionary means episode 20 needs far fewer fixes than episode 1. The error rate converges instead of resetting to zero every time.

postwriter.cn is in free beta right now. One upload returns the transcription, an SRT file, chapter timestamps, a description draft, and title options. The batch mode handles playlists, which matters if you have a back catalog you're trying to caption retroactively.

The DIY path, spelled out

If you'd rather not use any service, here's the zero-dollar stack:

Install faster-whisper (pip install faster-whisper).
Transcribe with word timestamps on.
Convert the output to SRT — there are free converter scripts on GitHub, or use Subtitle Edit to import and re-segment.
Fix line breaks so no line exceeds ~42 characters, 2 lines max per subtitle.
Upload the SRT to YouTube Studio under Subtitles, or burn it in with ffmpeg: ffmpeg -i in.mp4 -vf subtitles=out.srt out_subbed.mp4.

Total cost: $0. Total time after you're practiced: maybe 20 minutes per video, every video, forever. The recurring time is the hidden cost. Free tools charge you in minutes instead of dollars.

Which one should you pick?

One-off video, don't care about styling: YouTube's caption editor.
Already editing in CapCut: use its captions, export before the next pricing change.
Terminal-comfortable, upload weekly: local Whisper + Subtitle Edit.
Weekly uploads, niche vocabulary, back catalog to fix: a generator with a review step and dictionary, like postwriter.cn during the free beta.

FAQ

How accurate is automatic subtitling?
Clean audio with a standard accent: 92-97% word accuracy. Music, crosstalk, or heavy accents push it lower. The gap between 95% and 99% is exactly where review tools earn their keep.

SRT file or burned-in subtitles?
Upload SRT to YouTube for search indexing and let viewers toggle it; burn text in for the first hook section if your analytics show mute playback. Many creators do both.

Does postwriter.cn watermark free-beta exports?
No. The beta is the full pipeline — SRT, description, chapters, titles. Founder pricing later is $39 for 3 years, versus $12-29/month for comparable tools.

What languages are supported?
Check the site's current list during beta; multilingual support is expanding. Whisper-family backends cover 90+ languages with varying accuracy.

Video to SRT: How to Generate an SRT File From Any Video File

zerone01 — Tue, 08 Sep 2026 08:49:15 +0000

Scattered across r/VideoEditing are variations of the same request — threads like 1b891um ("What's a good free app to add subtitles to footage?"), 1i86giq, and 131e4ij all contain people trying to get from "I have a video file" to "I have an SRT file" without paying a subscription for the privilege.

It's a five-minute task if you know the path and an hour of tab-hopping if you don't. This is the complete map.

What an SRT file actually is

A plain-text file with numbered subtitle blocks:

1
00:00:01,240 --> 00:00:03,890
So the first thing to check
is the relay panel.

2
00:00:03,950 --> 00:00:06,100
It's on the left side, behind the battery.

Sequence number, start and end timestamps (hours:minutes:seconds,milliseconds), text. That's the whole format. No metadata, no styling. Every player, editor, and platform from VLC to YouTube to Premiere reads it.

Its sibling is WebVTT (.vtt), which YouTube also accepts and which supports styling. If a tool offers both, take the SRT for compatibility; convert to VTT later if needed — the conversion is trivial.

Why "just use YouTube" isn't the full answer

The obvious free path: upload to YouTube (unlisted), let auto-captions generate, download the caption file as SRT from YouTube Studio, delete the video. It works, and it's free. Problems: the caption generation can take hours; accuracy on names and jargon is mediocre; and you're shipping your unreleased video through a third party before it's public. For sensitive client work, that last one is a dealbreaker.

The DIY route: local Whisper to SRT

The robust free path runs through Whisper-family models on your own machine.

Option A — faster-whisper (Python):

from faster_whisper import WhisperModel
model = WhisperModel("medium", device="cuda", compute_type="float16")
segments, info = model.transcribe("input.mp4", word_timestamps=True)
with open("output.srt", "w", encoding="utf-8") as f:
    for i, seg in enumerate(segments, 1):
        f.write(f"{i}\n{fmt(seg.start)} --> {fmt(seg.end)}\n{seg.text.strip()}\n\n")

A 20-minute video takes 2-5 minutes on a mid-range GPU, longer on CPU. The medium model is the accuracy/speed sweet spot for English; large-v3 if you have the VRAM.

Option B — Subtitle Edit (GUI): Open the video, use Audio-to-text (Whisper) from the menu, and it produces a subtitle track you can edit against the waveform, then save as SRT. Slower to learn, easier to fix timing visually.

Option C — ffmpeg for audio extraction first: If a tool chokes on your container, extract audio once: ffmpeg -i input.mov -vn -ac 1 -ar 16000 audio.wav. Mono 16kHz WAV is what the models want anyway.

The catch with all local routes: raw Whisper output has subtitle-hostile segmentation. Lines run long, break mid-clause, and exceed comfortable reading speed (aim: max ~42 characters per line, 2 lines, ~1.5-4 seconds per subtitle). Post-processing the segmentation is where the real time goes. There are GitHub scripts (whisper-srt segmentation helpers, whisper-standalone) that do the re-segmentation for you.

Where a converter service fits

The DIY route assumes you want a pipeline. Many people just have videos — a batch this week, another next month — and want the SRT without adopting a toolkit.

postwriter.cn covers video-to-SRT as part of its pipeline: upload the video, get back the SRT plus the transcript, chapter timestamps, description draft, and titles. Two things distinguish the SRT specifically.

First, the review page. Every word aligns to the audio — click a word, hear it, fix it. Names, product terms, places: fix once. Second, those fixes persist in a personal dictionary, so the next video's SRT comes out cleaner. SRT quality is mostly proper-noun quality, and proper-noun errors are the same 20-40 words every video for most creators. A dictionary that learns them is the difference between fixing every export and fixing almost none.

Batch mode handles playlists and back catalogs — one job instead of 40 uploads.

Free during beta. Founder pricing after: $39 for 3 years.

Common SRT problems and fixes

Timestamps drift late in the file. Usually a variable-frame-rate source. Re-encode to constant frame rate first: ffmpeg -i in.mp4 -r 30 -c:v libx264 crf_v.mp4.

YouTube rejects the file. Check the timestamp format — commas before milliseconds, not periods. Also check for a missing blank line between blocks or a BOM in the encoding. Save as UTF-8 without BOM.

Foreign language input. Whisper-family models handle 90+ languages with varying quality; specify the language flag rather than letting auto-detect guess on short files.

FAQ

SRT or VTT for YouTube?
Both accepted. SRT for universal compatibility, VTT if you want styled/positioned captions.

Can I burn the SRT into the video afterwards?
Yes: ffmpeg -i in.mp4 -vf subtitles=out.srt out_burned.mp4. Upload the SRT separately too, so mobile viewers can toggle captions off.

How accurate is automatic SRT generation?
92-97% on clean single-speaker audio; lower with music, crosstalk, or accents. The gap closes fastest when corrections persist — the dictionary approach — rather than re-fixing each export.

What's the file size limit on postwriter.cn during beta?
Check the upload page for current limits; long-form handling is supported and limits are being raised through beta.