Add speakrs head-to-head harness and measured DER results - #89
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Wire speakrs into the cross-engine benchmark (single der.py scorer), add a thin speakrs-rttm CLI helper, refresh polyvoice runner variants for the v2+VBx default, and record the locked H2H protocol plus an interim 4-file matched-set DER summary.
10-file VoxConverse-test under der.py: speakrs-coreml 13.74% vs polyvoice 17.72% no-collar micro; gap is confusion-dominated. polyvoice remains faster on cold CLI (~53× vs ~23× CoreML on M1 Pro).
Process a directory of WAVs with a single loaded pipeline and --hyp-dir output, skipping cached RTTMs. Enables full-split CoreML runs in tens of minutes instead of cold-starting per file.
Warm batch on VoxConverse-test (M1 Pro, 144× RTFx) reproduces speakrs published accuracy. Gap vs polyvoice remains ~4 pp, confusion-dominated.
Matched der.py scoring on full VoxConverse-test. Gap is 4.14 pp no-collar micro and almost entirely confusion (8.04 vs 3.63).
Record speakrs CoreML 11.08% vs polyvoice 15.22% under the same der.py scorer (full 232, collar 0). Gap is confusion-dominated. Update COMPETITORS with speakrs as the primary Rust peer threat.
Same der.py scorer on Mix-Headset. Gap is 5.97 pp no-collar; speakrs gets speaker count roughly right (11/16 exact) while polyvoice collapses (2/16). Documents both corpus H2H tables in BENCHMARKS.
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Summary
speakrs_runner, warm-batchspeakrs-rttmhelper,score_h2h.py).benchmarks/der.pyon Apple M1 Pro:docs/BENCHMARKS.mdanddocs/COMPETITORS.md(speakrs as primary Rust peer).benchmarks/results/speakrs-h2h-2026-08-03/(PROTOCOL / VERDICT / JSON scores).Why
speakrs is the closest peer (Rust + ONNX/CoreML, community-1 style). Their published ~11% Vox DER is reproduced on our scorer. The residual is speaker confusion/count, not RTF.
Test plan
benchmarks/tools/speakrs-rttm(--features coremlon macOS)Notes
speakrscheckout by default (../../../../speakrsfrom the tool crate); seebenchmarks/tools/speakrs-rttm/README.md.