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Whisper-MLA: Reducing GPU Memory Consumption of ASR Models based on MHA2MLA Conversion
arXiv – CS AI|Sen Zhang, Jianguo Wei, Wenhuan Lu, Xianghu Yue, Wei Li, Qiang Li, Pengcheng Zhao, Ming Cai, Luo Si||7 views
🤖AI Summary
Researchers introduce Whisper-MLA, a modified version of OpenAI's Whisper speech recognition model that uses Multi-Head Latent Attention to reduce GPU memory consumption by up to 87.5% while maintaining accuracy. The innovation addresses a key scalability issue with transformer-based ASR models when processing long-form audio.
Key Takeaways
- →Whisper-MLA reduces KV cache size by up to 87.5% compared to standard Whisper models while maintaining competitive accuracy
- →The approach applies Multi-Head Latent Attention specifically to decoder self-attention for optimal performance-memory balance
- →Existing pretrained Whisper models can be converted to Whisper-MLA with minimal fine-tuning required
- →The solution addresses significant GPU memory consumption issues that limit Whisper's use with long-form audio
- →Extensive testing on LibriSpeech benchmark validates the effectiveness of the MHA to MLA conversion approach
#whisper#speech-recognition#gpu-optimization#transformer#memory-efficiency#asr#multi-head-attention#ai-models#machine-learning#performance-optimization
Read Original →via arXiv – CS AI
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