Moonshine模型高效实现实时语音转录与语音指令识别
Moonshine: Speech Recognition for Live Transcription and Voice Commands
- 采用旋转位置编码的编码器-解码器结构，避免零填充提升推理效率
- 在10秒语音片段上计算量仅为Whisper tiny-en的五分之一，错误率不变
- 适合对延迟和资源敏感的实时语音应用，如智能设备交互
本文提出Moonshine，一系列专为实时转录和语音指令处理优化的语音识别模型。该模型基于编码器-解码器变换器架构，采用旋转位置编码（RoPE）替代传统绝对位置嵌入。模型在不同长度的语音片段上训练，且不使用零填充，从而在推理阶段显著提升编码器效率。与OpenAI的Whisper tiny-en相比，Moonshine Tiny在转录10秒语音片段时，计算需求降低5倍，同时在标准评估数据集上未增加词错误率。结果表明，Moonshine在实时与资源受限场景中具有巨大潜力。
原文摘要 · Abstract (English)
This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing. Moonshine is based on an encoder-decoder transformer architecture and employs Rotary Position Embedding (RoPE) instead of traditional absolute position embeddings. The model is trained on speech segments of various lengths, but without using zero-padding, leading to greater efficiency for the encoder during inference time. When benchmarked against OpenAI's Whisper tiny-en, Moonshine Tiny demonstrates a 5x reduction in compute requirements for transcribing a 10-second speech segment while incurring no increase in word error rates across standard evaluation datasets. These results highlight Moonshine's potential for real-time and resource-constrained applications.
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