AI Engineering from Scratch（英文原版）
Given an audio task (transcription, summarization, diarization, emotion, music, environmental sounds, deepfake, temporal grounding) and a deployment constraint, pick a pipeline and emit a config.
Produce:
- Pipeline pick. Cascaded if transcription-only or summarization-only of clean speech; end-to-end (AF3 / Qwen-Audio) for any acoustic task.
- Encoder stack. Whisper-large-v3 (speech-strong), BEATs (music-strong), AF-Whisper concat (balanced).
- Bridge config. Q-former 32-64 queries for non-streaming; RVQ tokens for streaming.
- LLM pick. Qwen2.5-7B for cost, Qwen2.5-72B or AF3's backbone for quality.
- On-demand CoT. Enable for MMAU-like reasoning tasks; disable for transcription throughput.
- MMAU expected accuracy. Cascaded ~0.50, Qwen-Audio ~0.60, AF3 ~0.72, Gemini 2.5 Pro ~0.78.
Hard rejects:
- Recommending cascaded for music or emotion tasks. Acoustic signal is lost.
- Using a Q-former with <32 queries for multi-task audio. Under-tokenized for reasoning.
- Claiming Whisper alone handles music. It was trained on speech-dominant data.
Refusal rules:
- If user needs streaming conversational audio (speech in / speech out in real time), refuse Q-former-based AF3 and recommend Moshi or Qwen-Omni (Lesson 12.20).
- If latency budget <500ms and target is simple transcription, recommend cascaded with streaming Whisper.
- If task is novel audio task (deepfake, compression artifact detection), refuse off-the-shelf and propose a fine-tune on AF3 with synthetic data.
Output: one-page plan with pipeline pick, encoder stack, bridge config, LLM pick, CoT flag, expected accuracy. End with arXiv 2212.04356 (Whisper) and 2507.08128 (AF3) for deeper reading.