Platform · Video transcript extraction

Video Transcript Data

A transcript is either the platform's caption track or a machine's guess. The record says which.

Video transcript extraction produces text from public videos in scope. Where a video shows a caption track, that is used and labelled — creator-supplied or automatic. Otherwise speech-to-text is run and labelled machine-generated, with its model and date. YouTube's API only releases captions to a video's owner, so it is not the route. Transcripts are for analysis; rights stay with the creators, and training use is not asserted. Only publicly visible content is collected, without accounts or logins, commenters are handled in aggregate, and every empty field carries a reason.

Machine transcripts mishear brand names. Brand detection on them carries a confidence score.

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Free pilot on your own Video transcript extraction list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

transcripts_sample.jsonl LIVE FEED

{"post_id":"yt-3301","transcript_source":"creator_captions"} {"post_id":"tt-55012","transcript_source":"our_stt","stt_model":"as recorded","run_at":"2026-09-22"} {"brand_terms":"brand name, confidence 0.71"}

illustrative sample rows · public videoswhose words, whose text · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Video transcript extraction or its owners. Video transcript extraction and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Video transcript extraction at a glance

How we handle Video transcript extraction specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Source: Public videos in scope
Captions: Creator or automatic, labelled
Otherwise: Speech-to-text, labelled machine-made
YouTube API: Captions only for owners; not used
So: transcript_source on every transcript
Accuracy: Brand names often misheard; confidence kept
Rights: Stay with creators
Refresh: On new videos

Platform specifics

Whose words, whose text

These are the reasons a Video transcript extraction dataset needs its own handling rather than a shared retail schema.

Transcript source

Captions and machine transcripts differ in accuracy; both carry timestamps per segment.

transcript_source — creator_captions, auto_captions, our_stt.
stt_model and run_at for machine transcripts.
segments with start and end times.
language detected.

Rights, and what we do not do

Rights

Transcripts are delivered for analysis under the client's own rights assessment. Mentions in transcripts feed our brand mention page.

Empty fields

Every empty field says why — not_published, not_obtainable or platform_restricted — as the social media hub describes.

What we do not do

Download videos for redistribution, republish transcripts, assert training rights, transcribe private videos, or identify private individuals speaking.

Scope

What we collect on Video transcript extraction, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Caption tracks where shown
Speech-to-text labelled
Model and run date
Timestamped segments
Language detected
Confidence on brand terms
Transcript source on every record
null_reason on empty fields
Refresh on new videos

❌ What we do not, and why

Video redistribution
Transcripts republished
Training rights asserted
Private videos
Identification of private speakers

Core Video transcript extraction fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
post_id / platform	Video
transcript_source	creator_captions, auto_captions, our_stt
stt_model / run_at	Where ours
language	Detected
segments	Start, end, text
word_confidence	Where available
brand_terms	With confidence
duration	As shown
posted_at	UTC
captured_at	Timestamp
null_reason	Every empty field

Use cases

What teams do with Video transcript extraction data

Spoken brand mentions

What creators say, not just write.

Content analysis

Topics across videos.

Sponsorship checks

Spoken disclosures.

Search within video

Timestamped segments.

The 24-hour sample — run on your sources, not ours

Send us a Video transcript extraction item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Video transcript extraction is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Video transcript extraction data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what social media data covers, and a Video transcript extraction-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Video transcript extraction data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Published caption tracks where shown; otherwise our speech-to-text, labelled.

Only to the video's owner, so we do not use it for others' videos.

Mostly, but brand names are often misheard. Confidence is kept.

That depends on creators' rights and platform terms. We do not assert it.

No.

It depends on video volume and length.

Talk to us.

See real Video transcript extraction data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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