Content Transformation using Deep Learning

Content Transformation using Deep Learning

The Challenge

Making tutorial videos are difficult and time consuming. Moreover as the speaker speaks only in one language it is hard to reach a larger audience due to lack of multilanguage version. Tatras was tasked by a leading EdTech company to create a solution that will transform the tutorial videos into a similar video but with the speaker speaking in a different language. The original tone, sentiment and expressions of the tutor will be preserved in transformed content so that it appears natural to the audience.

Hypothesis

  • The objective is to separately transform video, audio and text content of a video lecture in a particular source language and transform the same to a target language.
  • The transformation algorithm is capable of generating a synthetic video where same speaker is speaking in a different target language in his/her own voice and it is also capable of rendering the scene text in the same target language synchronized with the video stream

Execution

  • Built an ASR pipeline to transcribe the audio from the video
  • Used LLM to translate the transcription
  • Developed a Transfer learning based TTS pipeline to generate translated audio in original voice
  • Developed a synthetic video generator with lip-sync matching to match translated audio to original video
  • Implemented a image generator that replace text in original video with translated text

Outcomes

  • Given a video tutorial we generate a translated content in a target language specified by the user
  • Generate a video similar to original video but with translated content reflecting change in video, audio and text
  • The pipeline has been integrated into the Edtech platform and being demonstrated as a low cost approach to making content accessible to new multilingual catchments

Project highlights

  • Deployed system has created new client product and increased revenue.