https://huggingface.co/pyannote/segmentation
공식 코드End-to-end speaker segmentation for overlap-aware resegmentation
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Speaker Diarization is the task of segmenting and co-indexing audio recordings by speaker. The way the task is commonly defined, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, diarization implies finding speaker boundaries and grouping segments that belong to the same speaker, and, as a by-product, determining the number of distinct speakers. In combination with speech recognition, diarization enables speaker-attributed speech-to-text transcription.
Source: Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
End-to-end speaker segmentation for overlap-aware resegmentation
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
End-to-end speaker segmentation for overlap-aware resegmentation
pyannote.audio: neural building blocks for speaker diarization
LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization
Phoneme Boundary Detection using Learnable Segmental Features
Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
Speech Recognition and Multi-Speaker Diarization of Long Conversations
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
Robust speaker recognition using unsupervised adversarial invariance
Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.