#Speaker Diarization

Discover a diverse array of speaker diarization resources including top-tier papers, detailed libraries, vast datasets, and advanced software solutions. This project efficiently organizes essential diarization assets, offering valuable tools for researchers and developers. It includes frameworks, clustering, and speaker change detection to push diarization technology forward. Regular community contributions provide ongoing updates and improvements.

Explore the capabilities of whisperX for advanced speech recognition with remarkable accuracy and velocity. Featuring 70x real-time transcription, detailed word-level timing, and speaker identification via pyannote-audio, whisperX delivers precise results in complex auditory situations. Utilizing forced phoneme alignment and voice-activity recognition, it minimizes errors and enhances transcription quality. With straightforward GPU setup, whisperX supports multilingual transcriptions across a variety of languages using robust models like wav2vec2. Recognized for its excellence at the Ego4d transcription challenge and INTERSPEECH 2023, whisperX stands out in rapid, multilingual ASR.

This third-party project leverages OpenAI's Whisper ASR alongside Voice Activity Detection and Speaker Embedding to improve the accuracy of speaker diarization. By utilizing tools like MarbleNet and TitaNet for audio segmentation and speaker identification, the system effectively manages transcription and timestamp alignment. With compatibility for Python 3.10 and dependencies on FFMPEG and Cython, the project provides options for parallel processing and is designed to efficiently handle large audio files, while continuously addressing issues like overlapping speakers.

Explore a diverse collection of speech datasets in multiple languages, including Chinese, English, and Japanese, designed for speech recognition, synthesis, and speaker diarization. This collection supports various applications, such as speech commands and ASR system evaluation, facilitating advancements in speech technology. Notable datasets like Common Voice and LibriSpeech play a crucial role in enhancing machine learning models. This resource is invaluable for researchers seeking comprehensive audio data for developing speech-related solutions across different linguistic contexts.