This repository offers custom nodes for ComfyUI that facilitate audio processing through speaker diarization and the integration of speaker data into segments transcribed by the Whisper model. Utilizing the PyAnnote library for speaker identification and pandas for data management, these nodes enhance the capabilities of audio analysis in ComfyUI.
- Provides a Speaker Diarization Node to identify and segment speech from different speakers in audio files.
- Integrates a Whisper Segments to Speaker Node that aligns transcription segments with speaker information based on time overlaps.
- Ensures efficient data handling and manipulation through the use of pandas and other necessary libraries.
Context
This tool consists of specialized nodes designed for ComfyUI, aimed at improving audio processing workflows. Its primary purpose is to enable users to perform speaker diarization and enhance transcriptions from audio files by integrating speaker identification.
Key Features & Benefits
The Speaker Diarization Node allows users to identify when different speakers are talking within an audio file, providing detailed segments that include timestamps and speaker identifiers. The Whisper Segments to Speaker Node enriches Whisper transcriptions by labeling segments with corresponding speaker information, making it easier to follow conversations or discussions in audio recordings.
Advanced Functionalities
The nodes leverage advanced libraries such as PyAnnote for speaker diarization, which is a sophisticated method for distinguishing between multiple speakers in audio data. This capability is essential for applications where understanding who is speaking at any given time is critical, such as in interviews or multi-participant discussions.
Practical Benefits
By incorporating these nodes into ComfyUI, users can significantly enhance their audio processing capabilities, leading to improved workflow efficiency and greater control over audio data analysis. The integration of speaker labels with transcriptions not only boosts the quality of the output but also makes it more user-friendly and accessible for further analysis.
Credits/Acknowledgments
The project is open-source and acknowledges the contributions of the original authors and the community. For details on licensing, please refer to the LICENSE file included in the repository.