whisper-diarization and whisper-v3-diarization
These are **competitors** — both implement speaker diarization on top of Whisper for transcription with speaker identification, but A uses basic diarization while B adds WhisperX for improved accuracy and timing precision, making B a more feature-complete alternative.
About whisper-diarization
MahmoudAshraf97/whisper-diarization
Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
Combines Whisper with NVIDIA NeMo's voice activity detection and speaker embedding models (MarbleNet/TitaNet) to attribute transcribed text to individual speakers. Uses source separation (Demucs) for vocal extraction, CTC-forced alignment for precise timestamp correction, and punctuation-based realignment to compensate for temporal drift across segments. Outputs speaker-labeled transcriptions with segment-level timestamps, supporting configurable Whisper models and parallel inference modes for systems with sufficient VRAM.
About whisper-v3-diarization
TharanaBope/whisper-v3-diarization
Production-ready audio transcription & speaker diarization CLI & GUI using OpenAI Whisper and WhisperX
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work