Teach You a Lesson
subtitle burner.
Bake subtitles into every episode of Teach You a Lesson — diarized, translated if needed, and burned permanently into the picture so no platform can strip them.
- Free up to 60 min
- ·
- 100% word accuracy
- ·
- Speaker labels included
- ·
- 99+ languages
- ·
Drag & Drop
MP3, MP4, M4A, MOV, AAC, WAV, OGG, OPUS, MPEG, WMA, WMV
Episode Burn Pipeline
Watch the exact pipeline that runs when you upload media to Pxlify.
Upload & Extract
explainer_video.mp4
Whisper Speech AI
Converting audio to words...
Studio Transcripts
Synced SRT & VTT Exports
explainer_video.mp4
Extracting high fidelity audio streams...
Hardsub render for Teach You a Lesson
Per-character colours. Permanent burn. Cloud-side render.
Timed Highlights
Aligns audio signals with precise segment timestamps, ensuring transcripts fit video timelines perfectly.
Whisper Speech Model
Leverages neural transcription frameworks to capture speech patterns, technical terms, and complex vocabulary.
Multi-Format Exports
Export to SRT, WebVTT, Advanced SSA (.ass), JSON, Word (.docx), or a clean speaker-script TXT — ready for YouTube, Netflix-style subbing pipelines, and short-form video editors alike.
Interactive Playback
Click any word or timestamp in the transcript to jump the video directly to that spoken segment.
Privacy Secured
Local preprocessing allows you to play and test files locally in the browser sandbox before uploads are triggered.
Inline Studio Editor
Refine and update text segments directly on the dashboard with instantaneous state synchronization.
Hardsub an episode in 3 steps
Upload, refine captions, render.
Upload your video
Drag in a local file (.mp4, .webm, .mov) or pick an existing recording from your library.
Auto-generate timestamps
Pxlify analyzes the audio, splits it into speech segments, and timestamps every line automatically.
Refine & export
Search segments, edit lines inline, sync playback timings, then export to SRT, VTT, ASS, JSON, DOCX, or speaker-script TXT.
Episode burner FAQs
Yes. Hardsubs are part of the picture frame.
Yes. Assign colours per character and the renderer applies them consistently.
It renders one language per pass. Render multiple passes for multiple language tracks.
Typically faster than the source duration on the cloud GPU pool.