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Optimize Audio Diarization Reliability, Speaker Sequencing & Waveform Analysis

Vemuri priya requested to merge spectogram-analysis into develop

Description

This merge request introduces major improvements to the audio processing and speaker diarization pipeline by enhancing long-audio reliability, implementing consistent speaker sequencing, and replacing traditional spectrogram analysis with waveform-based audio visualization. The update improves backend stability for compressed audio formats, enhances transcript readability, and introduces a cleaner audio waveform representation aligned with modern audio processing standards.

1. Enhanced Speaker Diarization Stability

Implemented a robust fallback mechanism to improve diarization reliability for long-duration compressed audio files such as:

  • MP3
  • M4A
  • WhatsApp audio recordings

Improvements

  • Detects Pyannote chunk/sample mismatch failures
  • Automatically normalizes problematic audio into WAV format
  • Retries diarization on normalized audio
  • Cleans temporary files after processing

Benefits

  • Improved handling of long audio uploads
  • Reduced decoder-related diarization failures
  • Increased backend processing stability

2. Sequential Speaker Ordering

Implemented normalized sequential speaker indexing to maintain consistent transcript structure.

Improvements

  • Standardized speaker labels
  • Sequential ordering of detected speakers
  • Improved transcript readability and UI consistency

Example

Before:

Speaker_5
Speaker_2
Speaker_7

After:

Speaker_1
Speaker_2
Speaker_3

3. Transition from Spectrogram to Waveform Visualization

Replaced spectrogram-based analysis with waveform-based audio visualization.

Previous Implementation

The earlier implementation used STFT-based spectrogram analysis, which represented:

  • frequency distribution
  • intensity heatmaps
  • frequency-domain visualization

While technically useful for signal analysis, the visualization was not intuitive for standard audio representation.

New Implementation

The system now generates waveform-based amplitude visualization by directly plotting raw audio samples over time.

Benefits

  • Cleaner and more user-friendly audio representation
  • Visualization similar to professional audio tools
  • Improved frontend integration support
  • Better representation of audio amplitude over time

Technical Enhancements

  • Improved audio preprocessing workflows
  • Better exception handling for long-duration files
  • Added fallback recovery mechanisms
  • Refactored waveform generation service
  • Removed legacy spectrogram implementation
  • Improved maintainability of audio analysis modules

Validation

The implementation was validated against:

  • long-duration audio uploads
  • compressed audio formats
  • waveform rendering workflows
  • speaker sequencing consistency
  • fallback retry execution scenarios No breaking changes were introduced to existing functionality.

Outcome

This enhancement significantly improves:

  • audio processing reliability
  • diarization stability
  • transcript consistency
  • waveform visualization quality
  • backend maintainability and scalability The backend is now more stable and production-ready for handling real-world audio processing workloads.

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