aufklarer / Sortformer-Diarization-CoreML

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Model's Last Updated: July 17 2026
audio-classification

Introduction of Sortformer-Diarization-CoreML

Model Details of Sortformer-Diarization-CoreML

Sortformer Diarization (CoreML)

CoreML-converted NVIDIA Sortformer model for end-to-end speaker diarization on Apple Silicon.

Runs on Neural Engine at ~120x real-time.

Model Details
  • Architecture : Sortformer (Sort Loss + FastConformer-based)
  • Task : Speaker diarization (up to 4 speakers)
  • Input : 128-dim log-mel features
  • Output : Per-frame speaker activity probabilities
  • Format : CoreML .mlmodelc (compiled, ready to load)
  • Size : ~240 MB
Streaming Configuration
Parameter Value
Sample rate 16 kHz
Mel bins 128
n_fft 400
Hop length 160
Chunk length 6s
Left context 1 chunk
Right context 7 chunks
Subsampling factor 8
Max speakers 4
Input/Output Shapes
Tensor Shape Description
chunk [1, 112, 128] Mel features for current chunk
chunk_lengths [1] Valid frames in chunk
spkcache [1, 188, 512] Speaker cache state
spkcache_lengths [1] Valid entries in speaker cache
fifo [1, 40, 512] FIFO buffer state
fifo_lengths [1] Valid entries in FIFO
Output
speaker_preds [242, 4] Speaker activity probabilities
chunk_pre_encoder_embs [94, 512] Updated embeddings
out_spkcache_lengths [1] Updated speaker cache length
out_fifo_lengths [1] Updated FIFO length
Usage

Used by speech-swift for speaker diarization:

audio diarize meeting.wav --engine sortformer
let diarizer = try await SortformerDiarizer.fromPretrained()
let result = try diarizer.diarize(audio: samples, sampleRate: 16000)
for segment in result.segments {
    print("Speaker \(segment.speakerId): \(segment.startTime)s - \(segment.endTime)s")
}
Original Model

Converted from NVIDIA NeMo Sortformer via coremltools.

License

CC-BY-4.0

Runs of aufklarer Sortformer-Diarization-CoreML on huggingface.co

647
Total runs
-14
24-hour runs
-6
3-day runs
-57
7-day runs
-896
30-day runs

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