Large models handle multilingual subtitles in videos through a combination of speech recognition (ASR), machine translation, and text-to-text generation. Here’s a breakdown of the process with examples, along with relevant cloud services for implementation:
The model first transcribes the audio from the video into text using Automatic Speech Recognition (ASR). This step converts spoken language into written text in the source language.
The transcribed text is then translated into the target language(s) using neural machine translation (NMT) models. These models are trained on multilingual datasets to ensure accurate and context-aware translations.
The translated text is aligned with the video’s timing (timestamps) to ensure subtitles appear and disappear at the correct moments. The model also formats the text (font, size, duration) for readability.
Large models leverage their multilingual pretraining to handle nuances like idioms, cultural references, or technical jargon, improving translation quality.
By integrating these steps, large models automate the creation of accurate, synchronized, and contextually appropriate multilingual subtitles, enhancing accessibility for global audiences.