แนวคิดหลัก
Zipformer introduces efficiency and performance improvements to ASR encoders.
บทคัดย่อ
Zipformer is introduced as a faster, more memory-efficient, and better-performing Transformer model for ASR.
The model features a U-Net-like encoder structure with downsampling at various frame rates, reorganized block structure, BiasNorm for length information retention, and new activation functions SwooshR and SwooshL.
The ScaledAdam optimizer is proposed for faster convergence and better performance.
Extensive experiments on LibriSpeech, Aishell-1, and WenetSpeech datasets demonstrate Zipformer's effectiveness.
Ablation studies show the impact of different components on model performance.
สถิติ
Zipformer는 ASR을 위한 더 빠르고 효율적인 Transformer 모델로 소개됩니다.
모델은 U-Net과 유사한 인코더 구조, 재구성된 블록 구조, BiasNorm, 새로운 활성화 함수 SwooshR 및 SwooshL을 특징으로 합니다.
ScaledAdam 옵티마이저는 더 빠른 수렴과 더 나은 성능을 위해 제안됩니다.
LibriSpeech, Aishell-1 및 WenetSpeech 데이터셋에서의 실험 결과가 Zipformer의 효과를 입증합니다.
คำพูด
"Zipformer achieves state-of-the-art results on all three datasets."
"The proposed modeling and optimization-related innovations demonstrate the effectiveness of Zipformer."