altic-dev / nemotron-3-diarization-coreml

Nemotron 3 Diarization — Core ML

Core ML export of the final nvidia/Nemotron-3-Diarization checkpoint (revision 98fcee8e866bb534eee85d1eb3817170700ae717 ), for on-device speaker diarization on macOS 14+ / iOS 17+.

The license terms of the source checkpoint apply to this derivative.

Files

Precision: fp16 storage, fp32 compute

Weights are stored as fp16 and widened to fp32 at load time ( constexpr_cast ), so every op computes in fp32. The package stays at fp16 size (~199 MB).

• The checkpoint is bf16, which fp16 represents almost exactly (0.005% of values differ, by less than 3e-8).
• A plain fp16-compute export drifts on long audio: small per-chunk rounding feeds back through the speaker cache and compounds. On a 36-minute meeting it matched the reference on only 98.4–98.9% of frames and added a speaker. This happened on GPU, CPU and Neural Engine alike.

Validation

Frame-level agreement with NVIDIA's NeMo reference, same streaming loop and post-processing, on real meeting recordings:

Speed: ~21 ms per 27.2 s chunk on Apple silicon (about 2 s for a 36-minute track).

Interface

One call processes one chunk; the caller owns the speaker cache and FIFO update between calls (NeMo streaming_update ).

Required runtime settings

These must match training, or the model splits one voice across several speaker slots:

• spkcache_sil_frames_per_spk = 1
• Fill silence slots in the speaker cache with learnable_sil_emb.f32 , not a running mean of silent frames.
• Offline profile: chunk_len 340 , chunk_right_context 40 , fifo_len 40 , spkcache_len 264 , spkcache_update_period 300 .