Real-time answering-machine detection for 8 kHz US-English telephony (VICIdial / Asterisk outbound campaigns). A pure-PyTorch YAMNet backbone (fine-tuned, top-2 blocks) feeds a bidirectional GRU head; output is p_machine ∈ [0,1]. ~16 ms inference per 4 s of audio on a single CPU thread — fast enough to decide inside the first ring seconds.

Two checkpoints:

checkpoint	training pool	how it was measured
`v5b_full` (root)	50,876 clips — the full max-data pool (incl. real carrier voicemail, IVR, network intercepts, robocalls, call-center + conversational telephone speech)	validation (4,949 held-out clips)
`benchmarked_v4a/`	31,947 clips (frozen-eval-disjoint)	two frozen external test sets

Results

v5b_full (validation, threshold 0.5): 97.7% accuracy, machine recall 97.0% under a ≤3% false-machine-on-humans cap, fmh 1.56%. On held-out clips of the hardest class — real carrier voicemail greetings — error is 13%, versus 91% for a naive-composition baseline.

benchmarked_v4a (frozen external sets, never trained on, one pass): 79.8% accuracy / balanced-acc .845 / AUC .935 on a 796-clip external set; 92.5% / .928 / .979 on a 1,429-clip balanced set; machine recall at the FTC-style ≤3% abandonment cap: 0.56–0.91.

Usage

bash

pip install torch numpy soundfile scipy
python code/predict_example.py your_call.wav

python

import sys; sys.path.insert(0, "code")
from predict_example import load, predict
yamnet, head = load()
p_machine = predict(yamnet, head, "call.wav")   # 0..1

Audio: any wav (resampled to 16 kHz mono internally; trained domain is 8 kHz G.711 telephony, first ≤10 s of the call).

Intended use & limitations

Research / evaluation only (CC BY-NC 4.0). Trained on a mixed-license research pool; not cleared for commercial deployment.
US-English telephony. Non-English and non-telephone audio are out of domain.
Decision thresholds must be calibrated on your own audio — false-machine rates measured on any fixed test set do not transfer exactly to live traffic.
v5b_full has no fully-external benchmark (its pool absorbed the project's test sources — by design, for maximum coverage); benchmarked_v4a is the checkpoint with honest external numbers. Known weak spots: robocall recall (v5 val slice: 26% miss), heavily accented/studio-recorded humans.

Architecture

Backbone: YAMNet (AudioSet), pure-PyTorch port, custom torch.stft mel frontend — no TensorFlow; top-2 blocks fine-tuned (BN frozen), bf16.
Head: bidirectional GRU over 1024-d patch embeddings (0.96 s window / 0.48 s hop), ~350k params, label smoothing 0.1.
Files: *.yamnet.pth (backbone, 15 MB) + *.head.pt (head + config, 1 MB)
- *.json (export manifest with round-trip verification).

ozonetg/amd-telephony-yamnet-gru

AMD — Answering Machine Detection for telephony (YAMNet + GRU)

Results

Usage

Intended use & limitations

Architecture

ozonetg/
amd-telephony-yamnet-gru