"As explained in this video, flow-matching-based generative methods are a class of models that learn a "continuous vector field" in order to manage and transform what are relatively simple "noise distributions" into more complex data distributions. They do this by following ordinary differential equations. Instead of learning "discrete denoising steps" (that's what diffusion models do), they train the flow to match probability paths directly between data and noise."
"Put even more simply than that.... it is like drawing a smooth path from noise to data. By rethinking how speech models are trained, Drax claims to capture the nuance of real-world audio without the delay that slows traditional systems. The company says that Drax delivers accuracy on par with, or better than, leading models like OpenAI's Whisper while achieving five times lower latency than other major speech systems, such as Alibaba's Qwen2."
Flow-matching generative methods learn a continuous vector field and follow ordinary differential equations to convert simple noise distributions into complex data distributions. These methods match probability paths directly between data and noise rather than using discrete denoising steps like diffusion models. Drawing a smooth path from noise to data allows models to process multiple tokens simultaneously and better capture real-world audio nuance. Modern speech recognition systems struggle to balance speed and accuracy; token-by-token systems can be slow for long-form audio while some diffusion approaches gain speed but lose accuracy when trained on clean, idealised data.
Read at Techzine Global
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