Speaker Identification and Memorization Across Multiple Audio Segments
Author(s)
- Bishwanath Jana
Submission Date
2024-10-17
Version History
| Version | Date | Changes | Author |
|---|---|---|---|
| 1.0 | 2024-10-07 | Initial draft | Bishwanath Jana |
| 2.0 | 2024-10-17 | Updated with improved implementation | Bishwanath Jana |
Objective
The purpose of this Proof of Concept (POC) is to demonstrate an effective method for speaker identification across multiple chunks of audio using the Pyannote model from Hugging Face. The goal is to ensure consistent speaker recognition while processing large audio files, with improved accuracy and efficiency.
Scope
This POC focuses on the following key functionalities:
- Implementing speaker diarization to segment audio by speaker.
- Extracting and comparing speaker embeddings for consistent identification across multiple audio chunks.
- Providing a mechanism for continuous improvement through speaker profile updates.
- Generating timestamped transcriptions with speaker labels.
Related Features
- Audio Processing Pipeline: Integration with existing audio processing workflows.
- Speaker Memorization: Mechanism to remember and identify speakers across multiple audio files.
- Transcription System: Automatic speech recognition for generating text from audio.
- Data Storage: Management of speaker profiles and embeddings using in-memory structures.
Technical Approach
The technical approach utilizes the following tools and technologies:
- Pyannote Library: For speaker diarization and embedding extraction.
- Hugging Face Transformers: For accessing pre-trained models, including Whisper for speech recognition.
- Python: As the primary programming language for implementation.
- SciPy: For cosine similarity calculations between speaker embeddings.
- Pydub: For audio file manipulation and preprocessing.
- Pysrt: For generating SRT files with speaker-labeled transcriptions.
Development Tasks & Estimates
| No | Task Name | Estimate (Hours) | Dependencies | Notes |
|---|---|---|---|---|
| 1 | Set up Pyannote and other required libraries | 2 hours | None | Install necessary libraries |
| 2 | Implement multi-file audio processing | 3 hours | Task 1 | Handle multiple audio chunks |
| 3 | Develop speaker diarization functionality | 4 hours | Task 2 | Use Pyannote's diarization pipeline |
| 4 | Implement speaker embedding extraction | 3 hours | Task 3 | Use Pyannote's embedding model |
| 5 | Develop speaker identification mechanism | 4 hours | Task 4 | Implement cosine similarity matching |
| 6 | Integrate speech recognition | 3 hours | Task 3 | Use Whisper model for transcription |
| 7 | Implement SRT file generation | 2 hours | Tasks 5, 6 | Create timestamped transcriptions |
| 8 | Develop speaker profile updating mechanism | 3 hours | Task 5 | Improve speaker memorization |
Testing & Validation
The POC will be tested and validated through the following methods:
-
Basic Tests:
- Verify that audio is segmented correctly by speaker across multiple files.
- Check that embeddings are accurately extracted and compared.
- Ensure transcriptions are generated correctly with appropriate speaker labels.
-
Validation Criteria:
- Consistent identification of speakers across multiple audio chunks.
- Accuracy of speaker diarization and identification.
- Quality and consistency of generated transcriptions.
- Efficiency of processing multiple audio files.
Risks & Mitigations
| Risk | Impact | Likelihood | Mitigation Strategy |
|---|---|---|---|
| Inconsistent audio quality | High | Medium | Implement audio preprocessing and normalization techniques. |
| Misidentification of speakers | Medium | High | Use robust embedding comparison methods and implement a threshold for similarity matching. |
| Processing time for large audio files | Medium | High | Optimize code and consider implementing multi-threading for faster processing. |
| Inaccurate transcriptions | Medium | Medium | Use the latest version of Whisper model and consider post-processing of transcriptions. |
Conclusion
This POC demonstrates a reliable method for consistent speaker identification and transcription across multiple audio chunks. The implementation showcases improved accuracy in speaker identification, efficient processing of multiple audio files, and the generation of speaker-labeled transcriptions.