Wren is the fast, lightweight model in Crisp's filler-detection family (named
after birds — keen ears, clean song; the larger, higher-accuracy sibling is
Kestrel). A wren is the tiniest bird yet punches far above its size — fitting
for a ~75k-param CNN that hits 0.94 precision at ~600× real-time.
It detects filler words ("uh", "um") in speech for the
Crisp macOS app — a fast, on-device alternative to running
full speech-to-text just to find fillers.
Input: one log-mel chunk, [1, 1, 64, 25] (250 ms of 16 kHz mono audio,
standardized with fixed constants MEL_MEAN=-18.5658, MEL_STD=17.9252).
Output:filler_prob — P(this chunk is a filler), 0…1.
Format: a single-file Core ML .mlmodel (download directly, no unzip).
Runs on-device — Core ML schedules it across the Neural Engine, GPU, or CPU.
Feature extraction (mel + chunking + the slide/merge into time ranges) lives in
the host app; the model itself is a pure chunk → probability function.
Training
Data:PodcastFillers — ~35k human-labeled
"uh"/"um" events, 350+ speakers. Trained on the train split.
Task: binary filler-vs-speech on 250 ms chunks (silence/pauses are handled
separately by the app via ffmpeg silencedetect).
Evaluation (held-out, episode-disjoint test split)
Threshold
Precision
Recall
F1
0.5
0.914
0.933
0.924
0.7
0.944
0.894
0.918
0.8
0.959
0.863
0.908
Speed: ~600× real-time on CPU. Recommended operating threshold 0.7 (Crisp
favors precision — a false positive cuts real speech). Main error mode: occasional
confusion of short real words; breaths/laughter/music are rarely misfired.
Limitations
English only. Trained on podcast audio; very different domains (heavy accents,
noisy rooms) may need a higher threshold or a fine-tune. Not a transcriber — it
only flags fillers.
Files & versions (open weights)
Everything is downloadable — pick whichever you need:
File
What
Wren.mlmodel
Core ML model (Apple Neural Engine). A single file the app downloads directly.
Wren.pt
Raw PyTorch weights (state_dict) — for your own inference/fine-tuning.
Versioning: the version is the repo's commit count, v0.0.N (mirrors
Crisp's 0.<commits> scheme); each release is one commit + one tag. Pin an exact
version in the URL:
