Same recording, four methods: who said what?

Play the audio and follow the subtitles. Each word is colored by the speaker that method assigned; a red underline means it disagrees with ground truth. Source: Harper Valley Bank (CC BY 4.0) — no patient audio.
What each method knows: AWS and W+ECAPA self-enrolled are fully blind — profiles come from the recording's own clusters, with no outside reference audio. W+ECAPA enrolled and FT v2 were given about 20 s of reference audio from each person's clean channel — available for care partners in the archive, rarely for patients. Compare the first two for a fair test, the last two to see what enrollment adds.
Scores

Subtitles

Extraction quality — pretrained vs our fine-tunes

A different question, and the one extraction is actually built for: on a natural call, how cleanly does the model pull out one person? Three held-out Harper Valley calls, agent enrolled. Scores are SI-SNR in dB while the target speaks alone (higher is better), and the other speaker's residue (lower is better). The pretrained model was the best of three off-the-shelf options; the fine-tunes are ours.

FT v2 — extracted audio

The other methods only label; they never split the audio. FT v2 actually extracts it — this is the per-person output.

Speed

Measured end to end on the 107 s two-men call, one laptop CPU, no GPU. Model loading (about 8 s total) excluded.
stagetimevs real time
Whisper large-v3-turbo (words)27.5 s3.9x
pyannote (clustering)96.3 s1.1x
ECAPA (423 sliding windows)9.3 s11.5x
SpeakerBeam FT v2 (one speaker)25.5 s4.2x
Pipeline totals for 107 s of audio: W+ECAPA enrolled 36.8 s (2.9x) · W+ECAPA self-enrolled 133.1 s (0.8x) · plus FT v2 on both speakers 184.1 s (0.6x).
pyannote is the bottleneck — it costs more than everything else combined. A CP voice profile removes it from the pipeline, which is why enrollment is worth about 3.6x in speed on top of what it does for accuracy.