English, Hinglish, and Telgish caption workflows
Handling mixed-language speech in Indian content.
The challenge of code-switching
Indian English content frequently involves code-switching — the practice of alternating between two or more languages within the same conversation or even the same sentence. A creator might speak Hindi and English in the same breath (Hinglish), or Telugu and English (Telgish). Standard caption tools trained on a single language often fail at these transitions, producing garbled or incorrect text.
Capinsta's language modes
Capinsta offers dedicated modes to handle this reality:
- English — for content spoken primarily or entirely in English. Best for podcasts, tutorials, and presentations in English.
- Hinglish — for content where the speaker alternates between Hindi and English. The model recognizes both languages and transcribes each word in the appropriate script or romanized form.
- Telgish — for content mixing Telugu and English.
- Auto-detect mixed Indian-language mode — when you are not sure which mix applies, or when a single video contains multiple language combinations. The model detects the dominant languages and adjusts.
Choosing the right mode
If your video is entirely in one language, choose that language directly for the best accuracy. If your content mixes languages, pick the Hinglish or Telgish mode that matches your primary mix. If you are captioning a video with complex or shifting language use, use auto-detect.
Editing mixed-language captions
Even with the correct mode, you may want to adjust how mixed-language words appear. For example, you might prefer Hindi words romanized rather than in Devanagari script. Capinsta's editor lets you edit any caption text directly. Adjust spelling, change script representations, or rewrite phrasing to match your audience's expectations.
Word-level timing across languages
One of the advantages of Capinsta's word-level timing is that it preserves language switching accurately. When a speaker switches from Hindi to English mid-sentence, each word is still timed individually. This means active-word highlighting stays accurate even during language transitions — the highlight follows the speaker regardless of which language is being spoken.