Speaker Change Detection Model for Multi-Speaker Dubbing Sync
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Solution Overview
Problem
Conventional dubbing technologies face challenges in aligning dubbed speech with original speech due to speaker changes, leading to synchronization issues and reduced lip-sync accuracy, particularly in multi-speaker scenarios.
Innovation Solution
A dubbing engine utilizing a model trained with punctuation and speaker change datasets to generate synchronized audio tracks by fine-tuning a WavLM audio sub-model, mT5 text sub-model, and transformer model to accurately detect speaker changes and adjust pitch, tone, and pacing, enabling real-time or post-production dubbing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional dubbing technologies are used to translate and generate dubbed audio, then the dubbing process can be automated, but the dubbed speech does not align with the original speech timing and speaker changes, causing synchronization issues
Solution Approach 1:
The audio stream is segmented into separate speaker segments using speaker change detection. The system identifies transitions between different speakers and creates distinct segments for each speaker, allowing independent processing and timing alignment of each segment with the corresponding dubbed audio, thereby resolving synchronization issues while maintaining automation.
2Manufacturing precision
If speaker change detection is implemented to improve synchronization, then lip-sync accuracy can be enhanced, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The speaker change detection functionality is merged with the existing automatic speech recognition and dubbing generation pipeline. The system combines speaker segment identification, timing analysis, and audio synchronization in a unified processing framework, reducing overall system complexity while improving lip-sync accuracy through integrated multi-speaker awareness.
Data Source
AI summary
A method and a server for training a model are provided. The method comprises: acquiring a punctuation training dataset including a first input and a first label, the first input including audio data and textual data representative of an utterance, the first label including a sequence of ground-truth tokens, training the model using the punctuation training dataset, thereby generating a punctuation trained model; acquiring a speaker change training dataset including a second input and a second label, the second input including second audio data and second textual data, the second label including a second sequence of ground-truth tokens, fine-tuning the punctuation trained model using the speaker change training dataset, thereby generating a speaker change model; acquiring an in-use textual data and corresponding in-use audio data; and generating, using the speaker change model, the second in-use sequence of tokens based on the in-use audio data and the in-use textual data.