LMS integration
TalentLMS captions upload: the SRT flow, the gotchas, and how to not re-upload videos
TalentLMS is one of the most common LMS picks for 50–500-employee SaaS, engineering, and healthcare orgs — the exact segment GlossCap is built for. This is the caption-upload flow from inside a course, the two gotchas teams hit when retrofitting captions across a large library, and how to hand TalentLMS an SRT it will render the first time.
TL;DR
In TalentLMS, captions are attached per video unit, not per course. Open the course, edit the video unit, use the video-player settings (sometimes labelled "Captions" or "Subtitles", depending on plan and theme) to attach an .srt file. The subtitle lives alongside the video and persists through unit updates as long as you don't replace the video itself. Retrofit across a course library by exporting SRTs from GlossCap with filenames matching each unit's video, then uploading in a single admin session. The two things teams get wrong: assuming a course-level caption track (there isn't one), and re-uploading video to attach a caption track (you don't have to).
Where the caption upload actually lives
TalentLMS's units system treats video as a unit type. When you create or edit a video unit, you upload the video file — and then the caption option appears in the unit's settings alongside the video player. The exact UI label has shifted between theme versions (some deployments call it "Captions", older themes "Subtitles"), but the control is consistently on the video-unit edit screen, not the course dashboard.
A cleaner mental model: the course has many units; a video unit has one video file and zero-or-more caption tracks. Each track is a subtitle file (typically SRT) with a language label. TalentLMS's player exposes them in the CC button on the video player at learner time.
What this means for retrofit work: you cannot bulk-upload captions at the course level and have them fan out to every video unit. You attach per unit. If your course has 20 video units, that is 20 uploads. The good news is they are fast — drag-and-drop, no re-rendering, no re-encoding.
What format to hand TalentLMS
TalentLMS's player is built on HTML5 video with a <track> element under the hood. Both SRT and WebVTT should render, but SRT is the safer default — the upload form is typically keyed to .srt, and the conversion TalentLMS does server-side is well-tested on that path. If your source is VTT and you are confident the exported file has the WEBVTT header and period-separated milliseconds, that also works. TTML and EBU-STL are not TalentLMS formats; don't try.
For WCAG 2.1 AA compliance, the content rules still apply: verbatim dialogue, speaker labels, non-speech sound cues, synchronized timing, ≈99% accuracy. TalentLMS's player is WCAG-neutral — the compliance posture comes from the caption content, not from the player. If your caption file hits the spec, the audit passes; if it has kubectl as "cube control", the audit fails regardless of which LMS is rendering it.
The retrofit workflow for a large course library
Teams retrofitting ADA Title II compliance (deadline now live as of 2026-04-24) or EAA-driven coverage (EAA) for a library of 50-500 internal training videos usually follow this shape:
- Inventory the course tree. Export from TalentLMS's courses page the list of video units per course. A spreadsheet row per unit with course-name / unit-name / video-filename is what you want.
- Batch-caption in GlossCap. Upload the source videos to a GlossCap batch; attach your company glossary once for the batch (Notion / Confluence / Google Docs sync, or paste the term list). GlossCap emits SRTs with filenames that mirror the source video names.
- Spot-check accuracy on the high-terminology units first. Engineering onboarding, any module with a product name, any medical content — these are where glossary bias pays off and where auditors sample. Expect near-zero hand-correction; if you see mangles, that is a glossary-scope gap, not a model-accuracy gap.
- Bulk-upload the SRTs in TalentLMS. Open each video unit in admin; attach the matching SRT. For a 100-unit library this is a focused half-day; there is no bulk API path that covers captions in the current TalentLMS tier, so the UI is the way.
- Verify on one learner account. Log in as a test learner, open 3-5 of the retrofitted modules, confirm the CC button renders the captions. This catches the single-most-common error: uploading the SRT to the wrong unit.
The two gotchas that trip every team
Gotcha 1: course-level captioning doesn't exist. The instinct is to look for a "captions" setting on the course. There isn't one. Every video-unit caption is independent. The implication for large libraries is real — you cannot set a single "use these glossary-aware captions across the whole course" switch. The good news is that GlossCap's glossary is customer-level, so the captions themselves are consistent across every unit you caption; it is the attachment step that is per-unit.
Gotcha 2: don't re-upload the video to attach captions. A surprisingly common mistake: admins open a video unit, don't see an obvious caption field, delete the unit, re-add it with the original video file, hope the caption control will appear. It won't — the control is always there, sometimes tucked under an advanced-settings expander depending on the theme. Re-uploading the video wastes time and may break enrolment state on learners who had started the unit. Look for the CC / subtitles control on the existing unit before doing anything destructive.
Why GlossCap is the right upstream for TalentLMS
The mechanic is the same one-liner on the homepage: captions that know your jargon. You paste in or sync your company glossary once. Every training video you caption gets Whisper-large with glossary-biased logit boosts, so kubectl stays kubectl, tirzepatide stays tirzepatide, your product names stay themselves, and SDK acronyms round-trip verbatim. The output is a WCAG-compliant SRT with speaker labels, non-speech-sound markers, and ≤160 wpm reading-speed pacing, formatted exactly the way TalentLMS's video unit wants it.
For a 50-person engineering-SaaS team, a 30-hour course library is ~30 hours of hand-correction at YouTube-auto-caption quality. GlossCap compresses that to minutes and eliminates the per-module accuracy variance. That is the whole pitch.
Related questions
Does TalentLMS support multi-language captions?
Yes — each video unit can have multiple caption tracks, one per language. The player's CC button lets learners switch. GlossCap exports one language per run (source language); translation-to-multiple-languages is on the v2 roadmap.
Can I bulk-upload captions via the TalentLMS API?
TalentLMS's public API is unit-oriented; caption-upload isn't a documented top-level endpoint at the time of writing. For large retrofits, the admin UI is the path. If the API changes, we'll document it.
What happens if the video file and SRT have timing drift?
Drift in the source is a caption-authoring bug, not a TalentLMS issue. GlossCap's exports are aligned to the source audio at sub-second precision; if you've edited the source video after captioning, re-caption rather than hand-shifting timecodes.
Does TalentLMS count toward WCAG 2.1 AA by itself?
The TalentLMS player surfaces captions correctly, which is part of the compliance story. Content compliance — dialogue accuracy, speaker labels, non-speech cues, audio description for visual-only content — is on your caption files. Our WCAG 2.1 AA page walks through the full surface.