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Improve Speaker Diarization Stability, Sequential Speaker Ordering & Spectrogram Analysis

Vemuri priya requested to merge speaker-diarization into spectogram-analysis

Description

This merge request introduces multiple enhancements and stability improvements to the speaker diarization and audio analysis pipeline. The update improves long-audio processing reliability, ensures consistent speaker ordering, and adds spectrogram-based audio analysis support.

Changes Implemented

1. WAV Normalization Retry for Long Audio Diarization Failures

Implemented a fallback retry mechanism for Pyannote diarization failures caused by compressed audio chunk mismatches.

Improvements

  • Detects decoder-related chunk mismatch errors
  • Automatically converts problematic audio into normalized WAV format
  • Retries diarization on the cleaned WAV file
  • Removes temporary WAV files after processing

Benefits

  • Improved handling for long MP3/M4A/WhatsApp audio files
  • Better diarization stability
  • Reduced failures caused by compressed audio seeking inaccuracies

2. Sequential Speaker Index Ordering

Implemented consistent sequential ordering for speaker labels.

Improvements

  • Ensures speakers are labeled in ordered sequence
  • Normalizes speaker indexing across diarization results
  • Improves transcript readability and UI consistency

Example

Before:

Speaker_2
Speaker_5
Speaker_1

After:

Speaker_1
Speaker_2
Speaker_3

3. Spectrogram Analysis Implementation

Added spectrogram analysis support for uploaded audio files.

Features

  • Audio spectrogram generation
  • Frequency spectrum visualization support
  • Audio analysis preprocessing improvements
  • Enhanced audio inspection capabilities

Benefits

  • Better debugging and analysis of uploaded audio
  • Improved visualization support for frontend integration
  • Foundation for future audio intelligence features

Technical Enhancements

  • Improved error handling for long audio workflows
  • Added fallback processing pipeline
  • Enhanced audio preprocessing stability
  • Improved backend robustness for production workloads
  • Better handling of compressed audio formats

Impact

This MR significantly improves:

  • Long audio processing reliability
  • Speaker transcript consistency
  • Audio analysis capabilities
  • Backend stability for diarization workflows

Validation

  • Tested with long-duration audio files
  • Verified retry fallback execution
  • Validated sequential speaker ordering
  • Confirmed spectrogram generation workflow
  • No breaking changes introduced

Outcome

The backend is now more stable and production-ready for handling:

  • long-duration recordings
  • compressed audio uploads
  • consistent speaker diarization workflows
  • spectrogram-based audio analysis

Closes #24 (closed)

Edited by Vemuri priya

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