MOSS-Transcribe-Diarize-0.9B-CoreML-INT8

MOSS-Transcribe-Diarize-0.9B-CoreML-INT8

Core ML conversion of OpenMOSS-Team/MOSS-Transcribe-Diarize, pinned to revision e6d68cdfcddbdad1a7e8454f0cb859cad76e2502. The model produces timestamped, speaker-attributed text in the form [start][Sxx]text[end].

Model

Files

Performance

Measured with greedy decoding on an Apple M5 Pro with 48 GB unified memory. Word error rate (WER), character error rate (CER), and real-time factor (RTF) are lower when better. Throughput is 1 / RTF, is higher when better, and reports how many seconds of audio are processed per wall-clock second. RTF excludes model loading. RSS is process memory sampled at clip boundaries; OS high-water RSS also includes transient peaks when the operating system reports it.

Multilingual precision check

Round trips

• 30-second overlapping AMI window: RTF 0.113; text=exact, speaker_sequence=exact, raw=drift. The source predicted 3 speakers where RTTM contains 4.
• 33.12-second two-chunk German clip: RTF 0.054; text=exact, speaker_sequence=exact, raw=exact.

The multilingual check covers only ten German, ten French, and ten Modern Standard Arabic clips. It is a conversion check, not proof of the upstream model's full 50-language quality. The meeting round trip checks transcript and speaker-sequence parity but is not a diarization error-rate benchmark.

Usage

Python

from pathlib import Path

import coremltools as ct
from huggingface_hub import snapshot_download

bundle = Path(snapshot_download("aufklarer/MOSS-Transcribe-Diarize-0.9B-CoreML-INT8"))
audio = ct.models.CompiledMLModel(str(bundle / "audio_encoder.mlmodelc"))
embedding = ct.models.CompiledMLModel(
    str(bundle / "decoder/decoder.mlmodelc"), function_name="embedding"
)
decoder = ct.models.CompiledMLModel(
    str(bundle / "decoder/decoder.mlmodelc"), function_name="decoder"
)
state = decoder.make_state()

The host computes Whisper features, replaces audio-placeholder embeddings, runs prompt prefill, then performs greedy token decoding while preserving state. The exact tensor names and shapes are recorded in config.json and decoder/config.json.

Command line

hf download aufklarer/MOSS-Transcribe-Diarize-0.9B-CoreML-INT8 --local-dir ./moss-transcribe-diarize

This downloads a complete low-level model bundle. SDK integration is tracked in speech-swift issue #388; until that integration lands, applications must implement the documented host contract around the exported graphs or weights.

Runtime contract

The multifunction decoder exposes embedding and decoder and uses a persistent FP16 MLState K/V cache. It was loaded with ComputeUnit.ALL and FastPrediction. Prompt construction, audio chunking, generation, cache management, and transcript parsing remain host responsibilities. Machine-readable details and measured results are in config.json, export_config.json, and validation.json.

Source

• Upstream model
• Upstream implementation
• Technical report

No training was performed for this conversion. The source weights contain 908,513,280 parameters and are licensed under Apache 2.0.

Limitations

Speaker IDs are anonymous within each inference. The source model can emit malformed or overlapping timestamps and can miss speakers; conversion parity does not correct those behaviors. ANE-only loading failed with Core ML error -14, so the measured runtime allows Core ML accelerator fallback. The validation here does not establish the upstream 90-minute claim for this deployment format.

Links

• speech-swift — Apple SDK
• Docs — Apple setup and CLI docs
• soniqo.audio — website
• blog — blog

License

Apache License 2.0, inherited from the upstream model.