# Architecture

## Chosen pipeline

```text
Audio
  |
  v
16 kHz Mono Preprocessing
  |
  v
Whisper Tiny
Frozen Encoder
  |
  v
Mean Pooling (over encoder time axis -> 384-dim vector)
  |
  v
Logistic Regression
  |
  v
P(END)
  |
  v
Threshold / Debounce / Hysteresis
  |
  v
END / CONTINUE
```

## Why each stage exists

- **16kHz mono preprocessing**: matches both the dataset's native format
  (confirmed: `pipecat-ai/smart-turn-data-v3.2-train` is 16kHz mono for
  every row inspected — `docs/PHASE3_REAL_AUDIO_VALIDATION.md`) and
  Whisper's expected input format. No resampling needed for in-distribution
  data; `src/turn_detector/audio_io.py` resamples defensively if a caller
  provides audio at a different rate.
- **Whisper Tiny frozen encoder**: chosen over the acoustic-only baseline
  based on measured evidence, not the assessment brief's suggestion alone
  — see `docs/RESULTS.md` for the actual numbers (F1 0.693 vs. 0.575,
  directional improvement on independent samples from the same dataset)
  and `docs/ERROR_ANALYSIS.md` for why: it specifically reduced the
  filler-associated false-END errors the acoustic model struggled with.
  Frozen (not fine-tuned) because fine-tuning was explicitly out of scope
  for this submission's timeline — see "Future Work" in the README.
- **Mean pooling**: the simplest, cheapest way to collapse Whisper's
  variable-length encoder output into a fixed-size vector for a linear
  classifier. Untested alternatives (attention pooling, last-token
  pooling, temporal-aware pooling) are noted as future work, not assumed
  inferior — mean pooling is what was actually measured.
- **Logistic Regression**: smallest classifier that worked well in
  testing (92 parameters, ~2.9KB) — chosen over an MLP per the "prefer the
  smallest head that works" instruction, and because it was never shown to
  underfit in the experiments actually run.
- **Threshold / debounce / hysteresis**: exists because a turn detector
  that commits to END the instant `P(END)` crosses 0.5 on a single noisy
  call will end turns prematurely on one uncertain pause. This layer
  (`src/turn_detector/inference.py`: `TurnDecisionConfig`,
  `TurnDecisionState`) lets a caller require sustained evidence — a
  minimum confidence margin, and/or several consecutive END calls in a
  streaming context — before committing to END, without training a
  second model. Defaults are simple (threshold=0.5, no debounce) so the
  basic case stays basic; both knobs are opt-in.

## What this diagram does NOT claim

- **Not a continuous per-frame streaming architecture.** Like the dataset
  it was trained on, and like the reference Smart Turn project it takes
  inspiration from, this is an event-triggered classifier over a buffered
  clip, not a frame-by-frame streaming model. See
  `docs/INITIAL_ANALYSIS.md` §5 for the full discussion of this design
  choice and its tradeoffs.
- **Not fine-tuned.** The Whisper encoder's weights are frozen exactly as
  measured in EXP-004 — this diagram reflects what was actually built and
  evaluated, not a target architecture that was never tested.