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Speaker Diarization
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BUTSpeechFIT/TS-ASR-Whisper
공식 코드DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition
https://github.com/BenoitWang/Speech_Emotion_Diarization
공식 코드https://github.com/DonkeyShot21/uis-rnn-sml
공식 코드Supervised online diarization with sample mean loss for multi-domain data
https://github.com/GeorgeEfstathiadis/LLM-Diarize-ASR-Agnostic
공식 코드LLM-based speaker diarization correction: A generalizable approach
https://github.com/HuangZiliAndy/RPNSD
공식 코드https://github.com/NVIDIA/NeMo
공식 코드https://github.com/PaddlePaddle/PaddleSpeech
공식 코드https://github.com/UltraSuite/ultrasuite-kaldi
공식 코드Ultrasound tongue imaging for diarization and alignment of child speech therapy sessions
https://github.com/alibaba-damo-academy/3D-Speaker
공식 코드3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization
https://github.com/alibaba-damo-academy/FunASR
공식 코드https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr
공식 코드Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information
https://github.com/alumae/torch-xvectors-wav
공식 코드https://github.com/audio-westlakeu/audiossl
공식 코드Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
https://github.com/audio-westlakeu/fs-eend
공식 코드https://github.com/avishaielmakies/unsupervised_speech_segmentation_using_slm
공식 코드Unsupervised Speech Segmentation: A General Approach Using Speech Language Models
https://github.com/benoitwang/speechbrain
공식 코드https://github.com/bsxfan/probabilistic_embeddings
공식 코드https://github.com/butspeechfit/diacorrect
공식 코드DiaCorrect: Error Correction Back-end For Speaker Diarization
https://github.com/butspeechfit/diaper
공식 코드DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors
https://github.com/butspeechfit/diarizen
공식 코드https://github.com/butspeechfit/eend
공식 코드https://github.com/calclavia/tal-asrd
공식 코드Speech Recognition and Multi-Speaker Diarization of Long Conversations
https://github.com/desh2608/dover-lap
공식 코드Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem
https://github.com/dihardchallenge/dihard3_baseline
공식 코드https://github.com/doerlbh/MiniVox
공식 코드Speaker Diarization as a Fully Online Learning Problem in MiniVox
https://github.com/dr-pato/ssgd
공식 코드Low-Latency Speech Separation Guided Diarization for Telephone Conversations
https://github.com/edunlp/nemo-multistage-classroom-diarization
공식 코드https://github.com/egruttadauria98/sspavaldo
공식 코드Online speaker diarization of meetings guided by speech separation
https://github.com/espnet/espnet
공식 코드https://github.com/fgnt/graph_pit
공식 코드Utterance-by-utterance overlap-aware neural diarization with Graph-PIT
https://github.com/firdavsn/SurgicalFeedbackAI
공식 코드Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment
https://github.com/frenchkrab/is2023-powerset-diarization
공식 코드Powerset multi-class cross entropy loss for neural speaker diarization
https://github.com/frenchkrab/is2024-powerset-calibration
공식 코드https://github.com/google/speaker-id
공식 코드DiarizationLM: Speaker Diarization Post-Processing with Large Language Models
https://github.com/google/speaker-id/tree/master/DiarizationLM
공식 코드DiarizationLM: Speaker Diarization Post-Processing with Large Language Models
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