Part of the soniqo.audio speech toolkit. See the speaker-diarization guide, inspect the native Swift Community-1 runtime, or browse the Core ML Speech Models collection.

This is a pipeline bundle, not one end-to-end model. The host must run powerset decoding, speaker counting, overlap-aware mask selection, VBx clustering, and timeline reconstruction exactly as described by config.json.

Model

Component	Parameters	Precision	Format	Size	Context / sample rate
PyanNet segmentation	1.49M	FP32	compiled Core ML	5.7 MiB	10 s / 16 kHz mono
Masked WeSpeaker	6.86M	FP32	compiled Core ML	26.2 MiB	10 s + three 589-frame masks
PLDA transforms	—	FP32	safetensors	0.19 MiB	256 to 128 dimensions

The WeSpeaker graph includes the exact Kaldi filterbank and weighted statistics pooling used by Community-1. Its vectors are intentionally not normalized before PLDA. Both Core ML graphs use fixed batch size one and require iOS 17 or macOS 14 or later.

Files

File	Size	Description
`segmentation.mlmodelc/`	5.7 MiB	Precompiled PyanNet segmentation graph
`embedding.mlmodelc/`	26.2 MiB	Precompiled filterbank and masked WeSpeaker graph
`plda.safetensors`	0.19 MiB	Precomputed x-vector and PLDA transforms for VBx
`config.json`	—	Tensor interfaces, pipeline constants, hashes, and conversion checks
`benchmark.json`	—	Sanitized aggregate and per-recording benchmark results
`LICENSE`	—	CC BY 4.0 notice and upstream attribution

Performance

Lower diarization error rate (DER) and Jaccard error rate (JER) are better. Throughput above 1x means faster than realtime.

Evaluation	DER	JER	Throughput	Interpretation
VoxConverse dev, five-file subset, 0.25 s collar, overlap included	4.65%	21.42%	25.0x	Below 10% DER and exactly matches upstream Community-1
speech-swift native runtime, same five files and scorer	4.66%	21.43%	25.2x	0.02 DER points from the published reference
Same five files, strict 0 s collar	6.99%	23.07%	25.0x	Boundary errors are fully counted
AMI ES2004a single-meeting diagnostic, known 4 speakers	23.00%	23.62%	24.0x	Exact upstream match; weak absolute accuracy on this meeting

Tested on Apple M5 Pro with cpu-and-neural-engine. Peak process memory was 863 MiB. The five-file speaker-count estimate was exact for 3 of 5 recordings, so callers should allow a known or bounded count when available.

The neural stages themselves processed one 10-second window in a median 7.44 ms for segmentation and 33.24 ms for all three masked embeddings. The benchmark used the official Community-1 host processing and revision 3533c8cf8e369892e6b79ff1bf80f7b0286a54ee. Scores and speaker counts matched the upstream PyTorch/MPS run for every evaluated recording.

The VoxConverse result is a small five-recording release check, not a claim over the full dataset. The AMI row is one meeting and is shown as a limitation, not a representative AMI score.

Swift runtime integration

The matching native runtime is available in soniqo/speech-swift on feat/community1-coreml at commit a6ed5a5. It runs both Core ML graphs, powerset decoding, speaker counting, PLDA, VBx, constrained assignment, and timeline reconstruction without Python.

bash

speech diarize meeting.wav --engine community1
speech diarize meeting.wav --engine community1 --num-speakers 2
speech diarize meeting.wav --engine community1 --min-speakers 2 --max-speakers 6

swift

let diarizer = try await Community1DiarizationPipeline.fromPretrained()
try diarizer.prewarm()

let result = try diarizer.diarize(
    audio: samples,
    sampleRate: 16_000,
    speakerBounds: Community1SpeakerBounds(minimum: 2, maximum: 6)
)

The runtime returns diarized segments plus one 256-dimensional centroid for each detected speaker. Speaker IDs are local to one result; use the centroids for recording-local or persistent identity matching.

Download

hf download aufklarer/Pyannote-Community-1-CoreML --local-dir Pyannote-Community-1-CoreML

Python example

The following runs the segmentation stage. Complete diarization also needs the host steps and PLDA data described in config.json.

python

import json
from pathlib import Path

import coremltools as ct
import numpy as np

root = Path("Pyannote-Community-1-CoreML")
config = json.loads((root / "config.json").read_text())
model = ct.models.CompiledMLModel(
    str(root / config["segmentation"]["model"]),
    compute_units=ct.ComputeUnit.CPU_AND_NE,
)

# One 10-second, 16 kHz mono window in [-1, 1].
waveform = np.zeros((1, 1, 160_000), dtype=np.float32)
log_probabilities = model.predict({"waveform": waveform})["log_probabilities"]
print(log_probabilities.shape)  # (1, 589, 7)

On Apple platforms, load the .mlmodelc directories directly. Do not compile an .mlpackage at runtime; compiled artifacts are provided to keep behavior stable across macOS, iOS, and simulator runtimes.

Source and license

Derived from pyannote/speaker-diarization-community-1 at revision 3533c8cf8e369892e6b79ff1bf80f7b0286a54ee. Community-1 combines Pyannote segmentation, WeSpeaker embeddings, and VBx clustering and is distributed under CC BY 4.0. Preserve this attribution when redistributing the bundle.

aufklarer/Pyannote-Community-1-CoreML

Pyannote Community-1 Core ML