Diarization System Design¶

Detailed architecture for the diarization pipeline, covering segmentation, track extraction, and transcript remapping.

1. System Overview¶

The diarization system identifies and separates different speakers in audio content, enabling speaker-specific processing of transcripts and audio. The system has evolved from the successful proof-of-concept implementation, which demonstrated effective speaker separation, audio track extraction, and timeline mapping for SRT generation.

The system follows these high-level steps: 1. Process audio through remote diarization service (PyAnnote) 2. Identify and merge speaker segments 3. Extract speaker-specific audio tracks with timeline mapping 4. Generate and process transcriptions for each speaker 5. Remap transcription timelines to original audio (for integrated presentation)

Core Design Principles¶

Modularity: Independent components with clear responsibilities
Single-Action Methods: Each method performs a single logical operation
Timeline Integrity: Precise tracking of timestamps across transformations
Extensibility: Support for multiple output formats and processing pipelines

2. System Architecture¶

graph TD
    A[Audio File] --> B[PyAnnote Client]
    B --> |DiarizationResult| C[Diarization Processor]
    C --> |Speaker Segments| D[Speaker Track Manager]
    D --> |Speaker Tracks| E[Audio Extractor]
    E --> F1[Speaker Audio 1]
    E --> F2[Speaker Audio 2]
    F1 --> G1[Transcription Engine]
    F2 --> G2[Transcription Engine]
    G1 --> H1[Speaker SRT 1]
    G2 --> H2[Speaker SRT 2]
    D --> |Timeline Maps| I[SRT Timeline Mapper]
    H1 --> I
    H2 --> I
    I --> J[Mapped SRTs]

    subgraph "External Components"
        G1
        G2
    end

3. Core Components¶

3.1 PyAnnote Client¶

Handles communication with the PyAnnote API for speaker diarization:

Responsibilities:
Authentication and session management
Media upload and URL generation
Job submission and status tracking
Result retrieval and parsing
Key Classes:
PyannoteClient: Main interface to the API
PyannoteConfig: Configuration constants

3.2 Diarization Models¶

Core data structures representing diarization information:

Key Classes:
DiarizationSegment: Individual speaker segment with timing
DiarizationResult: Complete result set with metadata
SpeakerTrack: Collection of segments for a single speaker
Responsibilities:
Define consistent data structures
Maintain segment information
Support conversion between formats

3.3 Diarization Processor¶

Coordinates the diarization workflow:

Responsibilities:
Orchestrate the diarization process
Handle result processing
Manage speaker segment merging
Generate speaker tracks
Key Methods:
diarize(): Complete diarization process
process_segments(): Process raw segments
merge_speaker_segments(): Combine adjacent segments
build_speaker_tracks(): Generate timeline maps

3.4 Speaker Track Manager¶

Manages speaker-specific audio tracks and timeline mapping:

Responsibilities:
Create and manage speaker track objects
Build timeline mapping tables
Export track metadata
Key Classes:
SpeakerTrack: Represents a speaker's audio track
TimelineMap: Maps between original and track timelines

3.5 Audio Extractor¶

Handles extraction of speaker-specific audio:

Responsibilities:
Extract audio segments from original file
Combine segments into speaker tracks
Handle silence/gap insertion
Export audio tracks
Key Methods:
extract_segments(): Extract speaker segments
build_track(): Combine segments into a track
save_track(): Export audio track to file

3.6 SRT Timeline Mapper¶

Transforms SRT files between different timelines:

Responsibilities:
Parse SRT files
Map timestamps between timelines
Generate remapped SRT files
Key Methods:
parse_srt(): Parse SRT file into objects
map_timeline(): Apply timeline mapping
generate_srt(): Create SRT file with new timestamps

4. Process Flow¶

4.1 Diarization Process¶

sequenceDiagram
    participant User
    participant PyannoteClient
    participant DiarizationProcessor
    participant SpeakerTrackManager
    participant AudioExtractor

    User->>DiarizationProcessor: diarize(audio_file)
    DiarizationProcessor->>PyannoteClient: upload_audio(audio_file)
    PyannoteClient-->>DiarizationProcessor: media_id
    DiarizationProcessor->>PyannoteClient: start_diarization(media_id)
    PyannoteClient-->>DiarizationProcessor: job_id
    DiarizationProcessor->>PyannoteClient: poll_until_complete(job_id)
    PyannoteClient-->>DiarizationProcessor: diarization_result

    DiarizationProcessor->>DiarizationProcessor: process_segments(result)
    DiarizationProcessor->>DiarizationProcessor: merge_speaker_segments()
    DiarizationProcessor->>SpeakerTrackManager: build_speaker_tracks()
    SpeakerTrackManager-->>DiarizationProcessor: speaker_tracks

    DiarizationProcessor->>AudioExtractor: extract_speaker_audio(speaker_tracks)
    AudioExtractor-->>DiarizationProcessor: exported_tracks
    DiarizationProcessor-->>User: speaker_tracks

4.2 SRT Transformation Process¶

sequenceDiagram
    participant User
    participant SRTTimelineMapper
    participant TranscriptionEngine

    User->>TranscriptionEngine: transcribe_tracks(speaker_tracks)
    TranscriptionEngine-->>User: speaker_srt_files

    User->>SRTTimelineMapper: transform_srt_files(speaker_srt_files, timeline_maps)
    SRTTimelineMapper->>SRTTimelineMapper: parse_srt_files()
    SRTTimelineMapper->>SRTTimelineMapper: remap_timestamps()
    SRTTimelineMapper->>SRTTimelineMapper: generate_output_files()
    SRTTimelineMapper-->>User: mapped_srt_files

5. Data Models¶

5.1 DiarizationSegment¶

Represents a single speaker segment with timing information:

speaker: String identifier for the speaker
start: Start time in seconds
end: End time in seconds
duration: Calculated property for segment duration

5.2 SpeakerTrack¶

Collection of segments belonging to a single speaker:

speaker: Speaker identifier
segments: List of DiarizationSegment objects
timeline_map: Maps original timeline to track timeline
Methods for adding segments and exporting audio

5.3 TimelineMap¶

Maps between original and track timelines:

intervals: List of TimelineInterval objects
Methods for mapping timestamps and exporting mapping data

5.4 SRTEntry¶

Represents a single subtitle entry:

index: Entry index number
start_time: Start time in SRT format (HH:MM:SS,mmm)
end_time: End time in SRT format (HH:MM:SS,mmm)
text: Subtitle text
Properties for time conversion between formats

6. Integration Points¶

6.1 Audio Processing Integration¶

The system integrates with existing audio processing tools:

Integration with pydub for audio manipulation
Support for various audio formats and codecs
Extraction and combination of audio segments

6.2 Transcription Integration¶

The system allows integration with different transcription engines:

Configurable transcription engine selection
Support for different output formats
Integration with existing transcription workflows

6.3 CLI Integration¶

Integration with the audio-transcribe CLI:

Diarization options as command-line flags
Configuration for speaker gap handling
Output format selection options
Integration with existing processing pipelines

7. Extension Points¶

7.1 Alternative Diarization Services¶

Support for different diarization services through an abstract interface:

Common API for different diarization providers
Standardized result processing
Pluggable service implementations

7.2 Custom Speaker Merging Strategies¶

Customizable segment merging through strategy pattern:

Configurable merging algorithms
Different gap threshold strategies
Speaker-specific merging rules

7.3 Timeline Mapping Extensions¶

Support for different timeline mapping strategies:

Various mapping algorithms
Handling of edge cases
Bidirectional mapping support

8. Implementation Considerations¶

8.1 Error Handling¶

In the production implementation:

Graceful Recovery: Handle API failures with retries
Validation: Ensure segment integrity before processing
User Feedback: Provide clear error messages
Safe Defaults: Use reasonable defaults when possible

8.2 Performance Optimization¶

For handling large audio files:

Streaming Processing: Process large files in chunks
Parallel Processing: Extract speaker tracks in parallel
Memory Management: Avoid loading entire audio into memory
Caching: Cache intermediate results where appropriate

8.3 Testing Strategy¶

Comprehensive testing approach:

Unit Tests: Test individual components in isolation
Integration Tests: Test component interactions
Acceptance Tests: Test end-to-end workflows
Performance Tests: Verify handling of large files

8.4 Documentation Strategy¶

Documentation requirements:

API Documentation: Document public interfaces
Usage Examples: Provide common usage patterns
Integration Guide: Document integration points
Configuration Reference: Document configuration options

9. Future Considerations¶

9.1 Speaker Identification¶

Future support for speaker identification:

Integration with voice recognition systems
Speaker profile management
Consistent speaker labeling across files

9.2 Multi-language Support¶

Enhanced language handling:

Language detection per speaker
Language-specific transcription models
Cross-language speaker tracking

9.3 Advanced Audio Processing¶

Future audio processing capabilities:

Background noise reduction
Audio quality enhancement
Speaker audio normalization

9.4 Interactive Visualization¶

User interface enhancements:

Interactive timeline visualization
Speaker track playback controls
Waveform display with speaker highlighting

9.5 Batch Processing¶

Support for batch operations:

Process multiple files
Generate consistent speaker IDs across files
Aggregate statistics across multiple recordings