Audio Editing with Diffusion-Based Timbre and Duration Matching
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Solution Overview
Problem
Conventional text-to-speech technologies fail to adapt to the original timbre of audio and cannot control the length of generated audio, leading to misalignment and reduced authenticity in edited audio content.
Innovation Solution
A self-attention-based diffusion model is used to determine the original acoustic features of audio, generate a modified text with a different portion, and produce a target acoustic feature that maintains the same timbre as the original audio, ensuring consistency and authenticity of the edited audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional text-to-speech technology is used to generate audio from modified text, then the audio content can be changed, but the timbre and duration cannot match the original audio
Solution Approach 1:
The audio editing process is segmented into three main stages: (1) extracting acoustic features from the original audio, (2) generating target acoustic features from modified text while preserving timbre characteristics, and (3) synthesizing the final audio. This segmentation allows independent optimization of each stage to maintain timbre consistency while enabling content modification.
Solution Approach 2:
Acoustic features serve as an intermediary between the original audio and the modified text. The system extracts acoustic features from the original audio, uses these features as conditions to guide the generation of target acoustic features from modified text, and finally synthesizes audio that combines the modified content with the original timbre characteristics. This intermediary approach resolves the contradiction by decoupling content generation from timbre preservation.
2Adaptability or versatility
If conventional text-to-speech technology generates audio from modified text, then content modification is achieved, but the audio length cannot be controlled to match the original
Solution Approach 1:
The system performs preliminary extraction of acoustic features from the original audio before generating the target audio. This preliminary action captures the duration and temporal characteristics of the original audio, which are then used as constraints during the generation process to ensure the final audio matches the original duration even though the text content has been modified.
3Measurement precision
If the entire audio is regenerated from modified text, then content accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system applies local quality by focusing computational resources only on generating the acoustic features corresponding to the modified portions of the text, while preserving the acoustic characteristics of unchanged portions. This allows content accuracy to be improved in the modified sections without regenerating the entire audio, thereby reducing time consumption.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and apparatus for editing audio content, a device, and a product. The method further includes acquiring a modified text, where the modified text includes an original portion identical to an original text of the original audio and a modified portion different from the original text. The method further includes generating, based on the modified text and an original acoustic feature, a target acoustic feature corresponding to the modified portion of the modified text using a self-attention-based diffusion model. Additionally, the method further includes generating a target audio based on the original acoustic feature and the target acoustic feature.