Guitar to MIDI conversion
There isn’t enough information in 10 mS of sampled audio to reliably tell the difference in frequency between a low E and a low F or D# notes.

10 mS is barely more than 1 full pitch cycle at those low frequencies, and there’s tons of non-pitched noise from the attack (pluck or strum) interfering with any measurement of the rate of phase change of the higher harmonics, assuming a pitch has even stabilized.  (Just look at a plot of the beginning of a notes waveform to see this!)  And you have to analyze the harmonics, as the higher overtones might be stronger (e.g. have the largest magnitudes in an FFT result) than the fundamental pitch frequency of the lowest string notes (search "missing fundamental" for details).  And the odd harmonics are not even the same musical note as the fundamental pitch.

For an FFT to show semitone different (5.9%) note fundamentals in completely separate FFT result bins, you would need at least 17 full pitch cycles, up to 2X more if you window before the FFT.

The loop filter of a stable PLL might have a similar delay.

Once the pitch has stabilized (entered sustained ringing after the attack transient phase), up-sampled or interpolated autocorrelation (or AMDF or ASDF) might provide a decent pitch estimate faster than an FFT.

A better bet would be to try to measure the reflection time of the traveling wave down the string from the pluck to the fret and back before the pitch stabilizes to a constant frequency standing wave.  From the reflection time, you can make a guess about which fret is pressed.  And then guess which note corresponds to that fret.

Or feed a complete set of note attack transients to train a DNN via machine learning, since that might be closer to how humans recognize notes quickly.