Multistage Audio-Transcript Alignment for Chunked AI Training Data
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Solution Overview
Problem
Existing AI training data alignment methods struggle with efficiently matching audio chunks to corresponding transcript portions, especially when audio files contain multiple languages or transcripts include non-auditory information, lacking precise timing information.
Innovation Solution
A multistage alignment process involving an audio to token predictor and text to token predictor, with a sliding window alignment engine, assigns timing labels to transcript units and generates alignment confidence, improving the matching of audio and transcript chunks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If audio files are broken into chunks for efficient training, then training efficiency is improved, but determining corresponding transcript portions becomes difficult without timing information
Solution Approach 1:
The alignment system performs preliminary action by computing timing information and mapping audio chunks to transcript portions before the actual training process. This pre-computation of alignment data enables efficient chunking without losing the correspondence between audio segments and their transcript portions, thus maintaining both training efficiency and information integrity.
2Measurement precision
If alignment systems account for non-matching portions (multiple languages, headers, etc.), then alignment accuracy is improved, but system complexity increases
Solution Approach 1:
The alignment system applies segmentation by dividing the alignment process into distinct stages: first aligning audio chunks with transcript portions, then separately handling non-matching portions such as multiple languages, headers, and footers. This segmented approach improves alignment accuracy for matching portions while managing the complexity of non-matching portions through dedicated handling mechanisms.
Solution Approach 2:
The system introduces an intermediary alignment layer that mediates between audio files and transcripts. This intermediary alignment data structure stores timing information and mapping relationships, enabling the system to handle non-matching portions (such as headers, footers, and multiple languages) without directly complicating the core alignment mechanism. The intermediary layer absorbs the complexity while maintaining accurate alignment for trainable portions.
Data Source
AI summary
Artificial intelligence (AI) training data includes pairs of known input and output. Training an AI model includes generating an output from the model, comparing it against a known output, and modifying the model parameters until the model generates an output close to the known output. For speech recognition AI models, the training data includes pairs of audio files and corresponding transcripts. Dividing the audio files into chunks can be beneficial for the training of a speech recognition AI model. However, transcripts may not include timing or alignment data to find a corresponding transcript portion when dividing an audio file. A first stage alignment can generate timing labels for the speech tokens in a transcript. When dividing an audio file into audio file chunks, the timing labels can be used to find corresponding transcript portions to audio file chunks. A second stage alignment can improve the accuracy of the timing labels.