polyvoice

Speaker diarization for Python — who spoke when.

Rust-powered, ONNX-based speaker diarization that runs on CPU, fits in 30 MB, and requires zero Python runtime overhead. Pipeline v2 with VBx clustering and overlap detection.

Install

pip install polyvoice

Requires Python 3.9+.

Quick start

import polyvoice

# Models auto-download on first run (~30 MB)
pipeline = polyvoice.Pipeline.balanced()

result = pipeline.run(samples, sample_rate=16000)

print(f"Speakers: {result['num_speakers']}")
for turn in result["turns"]:
    print(f"Speaker {turn['speaker']}: {turn['start']:.1f}s - {turn['end']:.1f}s")

API

polyvoice.Pipeline.balanced(models_cache=None, clusterer=None, vbx_plda_dir=None) — balanced accuracy / speed.
polyvoice.Pipeline.mobile(models_cache=None, clusterer=None, vbx_plda_dir=None) — smaller, faster model. clusterer is "vbx" (default, matching the CLI) or "ahc". VBx resolves its PLDA params via vbx_plda_dir, then the POLYVOICE_VBX_PLDA_DIR env var, then a registry download.
pipeline.run(samples, sample_rate) → dict with num_speakers and turns.
pipeline.run_result(samples, sample_rate) → typed DiarizationResult with .to_json() / .to_rttm() / .to_srt() / .to_vtt() / .to_txt() projections.
polyvoice.DiarizationResult.from_json(json) — re-hydrate a saved result.

Performance

Pipeline	VoxConverse-test DER (collar 0, overlap-scored)	Model size
default (v2+VBx)	15.4%	~30 MB

Full protocol, collar/averaging disclosure, and competitor numbers: docs/BENCHMARKS.md.

See the full repository for Rust / C / CLI APIs, benchmarks, and development docs.

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