Two-Pass Diarization for Audio Transcript Generation
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Solution Overview
Problem
Current speech recognition and diarization systems face challenges in efficiently processing long audio files with multiple speakers, leading to increased CPU time and turnaround time for transcript generation.
Innovation Solution
A method and system that divide an audio file into chunks, perform speech-to-text conversion and first-pass diarization in parallel, followed by second-pass diarization to merge speaker identification across chunks, resulting in a final transcript.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-pass diarization is performed on the entire audio file, then speaker identification accuracy is maintained, but processing time increases significantly for long audio files
Solution Approach 1:
The audio file is divided into multiple overlapping chunks, each processed independently through diarization. This segmentation allows parallel processing of chunks while maintaining speaker identification accuracy through cross-chunk correlation in the second pass, thereby reducing overall processing time without sacrificing precision.
Solution Approach 2:
The first pass diarization performs preliminary speaker identification on each chunk independently before the second pass correlates speakers across chunks. This preliminary action on smaller units enables faster initial processing while the subsequent correlation step ensures global accuracy.
2Productivity
If the audio file is divided into chunks and processed in parallel, then processing speed increases, but the complexity of merging speaker identification across chunks increases
Solution Approach 1:
The system segments the audio file into overlapping chunks that can be processed in parallel. The overlap region contains anchor segments that serve as reference points for correlating speakers across different chunks, enabling efficient merging without requiring complex global analysis of the entire audio file.
Solution Approach 2:
Anchor segments in the overlap regions act as intermediaries between adjacent chunks. These anchor segments provide reference speaker identities that facilitate the correlation process during the second pass, simplifying the merging operation by providing natural connection points between independently processed chunks.
3Measurement precision
If overlapping chunks with anchor segments are used, then speaker correlation accuracy improves, but the amount of data to be processed increases
Solution Approach 1:
The overlap regions are given special local quality by containing anchor segments with predetermined speaker identities. These anchor segments are processed with higher importance as they serve as reference points for correlation, while the non-overlap portions are processed more efficiently without the same level of detail, optimizing the balance between accuracy and data volume.
Data Source
AI summary
In one embodiment, a method for transcript generation includes receiving an audio file and dividing it into a plurality of chunks. The method further includes sending each instance of the plurality of chunks to a speech service module. The method further includes converting speech to text for each instance of the plurality of chunks and returning the text for each instance of the plurality of chunks. The method further includes merging the text for each instance of the plurality of chunks to yield an audio file transcript and sending the audio file and chunks to a diarization module. The method further includes performing first pass diarization on the chunks to yield a plurality of diarized chunks and performing second pass diarization on the plurality of diarized chunks and the audio file to yield a diarized audio file. The method further includes merging the files to yield a final transcript.