Method and system for AI-based real-time transcription of audio data

A method for an automated real-time transcription and editing of audio data using interim text, comprising:

• acquiring, by an audio transcription server (ATS) node, audio data from the at least one audio source entity;
• parsing out, by the ATS node, the audio data to derive features for beam forming and features for speaker diarization;
• generating, by the ATS node, a set of classifiers based on the features for beam forming and the features for speaker diarization;
• providing, by the ATS node, the set of classifiers to a machine learning module configured to generate a predictive model for producing at least one speaker identification parameter;
• identifying, by the ATS node, the speaker based on the at least one speaker identification parameter;
• continuously transcribing, by the ATS node, the audio data to generate an interim text associated with the identified speaker;
• deriving, by the ATS node, a plurality of labels from the audio data to generate a feature vector;
• passing, by the ATS node, the feature vector into the ML module configured to generate a predictive model for producing at least one interim text editing parameter;
• and
• providing, by the ATS node, at least one interim text editing parameter to user-entity node for editing the interim text.

A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform:

• acquiring audio data from the at least one audio source entity;
• parsing out the audio data to derive features for beam forming and features for speaker diarization;
• generating a set of classifiers based on the features for beam forming and the features for speaker diarization;
• providing the set of classifiers to a machine learning module configured to generate a predictive model for producing at least one speaker identification parameter;
• identifying the speaker based on the at least one speaker identification parameter;
• continuously transcribing the audio data to generate an interim text associated with the identified speaker;
• deriving a plurality of labels from the audio data to generate a feature vector;
• passing the feature vector into the ML module configured to generate a predictive model for producing at least one interim text editing parameter;
• and
• providing at least one interim text editing parameter to user-entity node for editing the interim text.