MOSS-Transcribe-Diarize-0.9B-ONNX-FP16

MOSS-Transcribe-Diarize-0.9B-ONNX-FP16

ONNX 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.

Aggregate inference phases across 80 clips: Processor 0.31 s, Audio encoder 90.74 s, Decoder prefill 12.84 s, Token decode 108.60 s, Other host work 0.01 s; 24.108 ms/generated token.

Paired runtime profile comparison

The same English subset was run in fresh processes for both rows.

Multilingual precision check

Round trips

• 30-second overlapping AMI window: RTF 0.434; text=exact, speaker_sequence=exact, raw=exact. The source predicted 3 speakers.
• 33.12-second two-chunk German clip: RTF 0.228; 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 onnxruntime as ort
from huggingface_hub import snapshot_download

bundle = Path(snapshot_download("soniqo/MOSS-Transcribe-Diarize-0.9B-ONNX-FP16"))
memory_info = ort.OrtMemoryInfo(
    "Cpu",
    ort.OrtAllocatorType.ORT_ARENA_ALLOCATOR,
    0,
    ort.OrtMemType.DEFAULT,
)
arena = ort.OrtArenaCfg(0, 1, -1, -1)  # same-as-requested growth
ort.create_and_register_allocator(memory_info, arena)

audio_options = ort.SessionOptions()
audio_options.add_session_config_entry("session.use_env_allocators", "1")
audio_options.add_session_config_entry("session.disable_prepacking", "1")
decoder_options = ort.SessionOptions()
decoder_options.add_session_config_entry("session.use_env_allocators", "1")
audio = ort.InferenceSession(
    str(bundle / "audio_encoder.onnx"), audio_options
)
decoder = ort.InferenceSession(
    str(bundle / "decoder.onnx"), decoder_options
)
print([value.name for value in decoder.get_inputs()])

The decoder accepts an empty cache for initial prefill and returns only newly generated K/V rows. The host appends those rows, performs greedy decoding, and parses [start][Sxx]text[end] output. Full signatures are in export_config.json.

Command line

hf download soniqo/MOSS-Transcribe-Diarize-0.9B-ONNX-FP16 --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 audio encoder and decoder are separate graphs. The decoder supports empty-cache prefill and returns only new cache rows. Fixed Gather indices materialize GQA K/V heads while preserving the original query-head matrix-multiplication shape. The measured CPU profile shares one same-as-requested arena between both sessions while retaining graph optimization, memory patterns, and decoder prepacking. Audio prepacking is disabled to lower the resident working set. 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. The measured CPU runtime used substantial memory; this is a compatibility export rather than the recommended Mac runtime. The validation here does not establish the upstream 90-minute claim for this deployment format.

Links

• speech-core — C++ runtime
• speech-android — Android SDK
• C++ docs — C++ runtime docs
• Android docs — Android setup docs
• soniqo.audio — website
• blog — blog

License

Apache License 2.0, inherited from the upstream model.