LLM-based semantic processing of subtitles
KrillinAI's subtitling process employs a two-stage intelligent system: first transcribing the subtitle text through the Whisper speech recognition engine, and then applying a big language model for semantic analysis. Unlike traditional time-sliced subtitle tools, the innovation is:
- Dynamic Break Algorithm: Dynamically adjusts subtitle duration based on language pauses, semantic integrity and text complexity
- Context-awareness: maintain the integrity of dialog passages and avoid splitting a complete semantics into multiple subtitles
- Intelligent line break mechanism: automatically optimizes text layout according to video resolution to ensure readability
The system integrates OpenAI's GPT model for semantic analysis by default, and also supports switching to the locally deployed FasterWhisper model. Test data shows that this method reduces 27% subtitle display errors compared to the traditional fixed duration slicing method, which is especially advantageous when dealing with complex academic content or fast dialog scenes.
In terms of technical implementation, the developer has established a semantic coherence scoring model, so that when punctuation, intonation or logical connectives are detected, the system will automatically extend the subtitle duration until the semantic unit is complete.
This answer comes from the articleKrillinAIThe
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