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MUSCAT is a new multilingual, scientific conversation benchmark dataset for evaluating ASR systems on challenging multilingual scenarios including code-switching, domain-specific vocabulary, and mixed language input. The dataset consists of bilingual discussions on scientific papers between speakers using different languages, with results showing current state-of-the-art systems struggle with these multilingual challenges.
pyannote 发布 speaker-diarization-community-1 流水线，相比 speaker-diarization-3.1 在说话人分配与计数方面有显著提升，支持离线使用、独家说话人分离以及可选的 pyannoteAI 云端托管。