TubeScribe turns any video into an accurate, timecoded transcript — then captions and translates it into more than 100 languages. Speaker labels, burned-in subtitles or SRT/VTT files, ready to publish.
Welcome back to the channel. Today we're looking at how small teams ship faster than large ones.
The short answer is fewer handoffs — and we have the data to back it up.
Right, and that matches what we saw last quarter across every team we studied.
Upload once. TubeScribe handles recognition, alignment, translation and export.
Drop in a video or audio file, or point TubeScribe at a URL. Long-form and batch uploads supported.
Google Cloud Speech-to-Text produces a word-level timecoded transcript with automatic speaker separation.
Gemini adapts tone, idiom and on-screen context — not a literal word swap — across every target language.
Download SRT, VTT or burned-in subtitles, or push captions straight to your publishing platform via API.
Everything a publishing team needs to localize a whole catalogue.
Automatic speaker labelling for interviews, podcasts and panels — each voice tracked separately.
Translate one transcript into every market at once, including right-to-left scripts and CJK typesetting.
Word-level timestamps keep subtitles locked to speech, with reading-speed limits applied per language.
Lock product names, people and jargon so they survive translation exactly as you spelled them.
Queue an entire back catalogue, or wire TubeScribe into an existing pipeline with a single REST call.
Review and correct any line before export. Corrections feed back into the glossary for future runs.
We chose Google Cloud as our sole provider for its speech, translation and generative AI models, and for the throughput to process long-form video at scale.
Google's recognition models produce word-level timestamps and speaker separation across our supported source languages — the foundation every other step depends on.
Gemini handles context-aware translation, tone matching, summarisation and chapter generation, using the full transcript rather than isolated lines.
Used alongside Gemini for high-volume language pairs where deterministic output and glossary enforcement matter more than stylistic adaptation.
Our API and web application run serverless on Cloud Run, scaling to zero between jobs and scaling out during batch localization runs.
Source media and generated caption assets live in multi-regional buckets and are delivered through Google's global edge network.
Vertex AI hosts our evaluation and quality-scoring models, which flag low-confidence segments for human review before anything is exported.
Open a channel to new markets without re-recording anything or hiring per-language editors.
Cut the first-pass subtitle work and keep translators focused on review instead of transcription.
Turn every episode into a searchable transcript, show notes and multi-language captions.
Make course libraries accessible and searchable, with accurate captions in every student's language.
TubeScribe is in active development. Join the waitlist and we'll let you know when it opens.
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