AI Engineering from Scratch（英文原版）

Given a target model (ASR / TTS / classifier / speaker / music) and input audio (sample rate, domain), output:

1. Feature type. Log-mel, mel, MFCC, raw waveform, or discrete codec (EnCodec, SoundStream). One-sentence reason.
2. Mel count and frequency range. n_mels, fmin, fmax. Reason tied to domain (speech vs music) and model target.
3. Frame and hop. frame_len, hop_len, window type. Reason tied to the required temporal resolution.
4. Normalization. Per-utterance mean/var, global stats, or dB with fixed reference; pre or post featurization.
5. Validation snippet. Python that prints the resulting shape, min/max, mean/std on a 1-second reference clip and asserts they match training.

Refuse to ship a feature pipeline whose frame/hop/mel count diverges from the published training config of the target model. Flag any MFCC-based setup for Whisper or Parakeet as wrong — those models consume log-mel. Flag any feature extractor without a normalization assertion.