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license: cc-by-4.0
library_name: coreai
pipeline_tag: voice-activity-detection
base_model: nvidia/diar_streaming_sortformer_4spk-v2
tags: [core-ai, coreaikit, sortformer, speaker-diarization, diarization, streaming, on-device, apple]
---
# Streaming Sortformer 4-spk v2 — Core AI
[`nvidia/diar_streaming_sortformer_4spk-v2`](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2)
(cc-by-4.0, 117M) converted to **Apple Core AI** — streaming **speaker diarization** ("who spoke
when", up to 4 speakers) running fully on-device via the
[zoo](https://github.com/john-rocky/coreai-model-zoo). Only the neural core is a graph; the NeMo
128-mel frontend, the streaming chunk loop, and the AOSC speaker-cache compression run in the Swift
host — a 1:1 port of NeMo `sortformer_modules.py` (inference path).
⚠️ Use the **streaming v2** checkpoint (cc-by-4.0). The offline `diar_sortformer_4spk-v1` is CC-BY-**NC**.
## Files
- `sortformer_float16.aimodel` — the static `forward_for_export` core, fp16 (~237 MB, macOS GPU).
- `sortformer_float16.h18p.aimodelc` — the same graph AOT-compiled for iPhone (h18p, ~450 MB).
- `sortformer_mel_filters_128x257.f32` — librosa-slaney mel filterbank (host log-mel frontend).
- `metadata.json` — streaming params + the fixed-buffer graph contract.
## Fixed-buffer graph contract
```
inputs: chunk_mel [1,1520,128] host zero-pads each mel chunk
spkcache [1,188,512] host-maintained speaker cache
valid [1,378] 1 = real frame / 0 = pad (spkcache block [0:len], chunk block [188:188+pe_len])
outputs: preds [1,378,4] sigmoid speaker activity
chunk_pe [1,190,512] pre-encode embeddings (host appends them to the speaker cache)
```
Host: NeMo 128-mel (preemph 0.97 → STFT n_fft=512/win=400/hop=160 → slaney mel → log, normalize=NA)
→ chunk the mel (188·8 frames, ±1 subsample ctx) → run the graph → slice chunk preds →
`streaming_update` + `compress_spkcache` (AOSC) → threshold 0.5/frame/speaker (frame = 80 ms) → turns.
## Verification
Byte-gated vs NeMo `forward_streaming` at **100.00 % speaker-activity agreement** (@0.5) on a 21.5 s
and a 64.5 s clip (the latter exercises the AOSC cache compression ~4×), in Python, in Swift on
**Mac GPU**, and on **iPhone 17 Pro** (A19 Pro, AOT h18p) — all driving this exported fp16 graph.
## Use
Ships in the **coreai-audio** app (Transcribe tab, "Diarize — who said what"): the diarizer segments
each speaker turn, then the on-device ASR (Whisper / Qwen3-ASR / Parakeet / Nemotron) transcribes it
into a **diarized transcript** — `Speaker 1 [0.3–4.1s]: …`. Speaker diarization already ships
on-device elsewhere (e.g. CoreML/ANE); this is speed parity, offered as a diarized transcript wired to
the zoo's own ASR. Conversion + Swift host loop: see
[`conversion/sortformer_diar`](https://github.com/john-rocky/coreai-model-zoo/tree/main/conversion/sortformer_diar).
Derived from NVIDIA's `diar_streaming_sortformer_4spk-v2` (CC-BY-4.0); this Core AI conversion is
released under the same license.
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