Converts raw audio into a time-frequency image on a perceptual (mel) scale - the standard input for speech models.
Raw waveforms are too long and too low-level for transformers, so audio models first compute a spectrogram: short-time Fourier transforms binned onto the mel scale, which spaces frequencies the way human hearing does. The result is effectively an image (time × mel bins) that convolutional or transformer encoders can process like any other 2-D input.
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