我完全看不懂到底在寫什麼東西，只知道老公公司產品的第一階段測試相當不錯。前面還有各樣關卡，繼續努力！老公，你背後有一個偉大的女人和更偉大的神，絕對有潛力成為成功的男人！Breaking the Precision Barrier: Achieving Superior Whisper Large V2 Accuracy at 4-bit on Tranxform XPU
Introduction: The industry standard assumption is that quantization is a compromise—a trade-off between efficiency and accuracy.
Today, we are announcing that Tranxform AI has eliminated that trade-off. We have successfully demoed Whisper Large V2 on our XPU, proving that our 4-bit implementation delivers higher accuracy than standard FP16 execution on traditional CPU/GPU architectures.
The Challenge: Scaling Whisper Whisper Large V2 is the gold standard for speech-to-text, but its 1.55B parameters make it a heavyweight for edge deployment. Most attempts to quantize to 4-bit result in significant "quantization noise," leading to transcription errors.
The Breakthrough: How 4-bit Outperforms FP16 Our XPU architecture utilizes [Mixed-Precision Accumulation]. While standard FP16 on general-purpose processors can suffer from catastrophic forgetting or rounding accumulation in long-form audio, our XPU’s specialized 4-bit kernels are optimized to:
• Minimize weight-outlier distortion.
• Maintain the high dynamic range required for the Whisper attention mechanism.
• Reduce Word Error Rate to 2.5% compared to FP16 baselines 2.7%.
Performance Metrics
• Accuracy: Lower WER on the LibriSpeech Clean/Other datasets compared to FP16.
• Efficiency: 4x reduction in memory footprint and 10X improvement in power efficiency.
Conclusion For enterprises, this means the highest-tier AI is no longer confined to the data center. It can live on the device, with better results and lower costs.
