Nemotron-3.5-ASR-Streaming-Multilingual-0.6b-CoreML

Nemotron 3.5 ASR Streaming Multilingual 0.6B — CoreML

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CoreML / Apple Neural Engine ships of nemotron-3.5-asr-streaming-0.6b (Conformer encoder + RNN-T decoder), optimized for on-device streaming ASR on Apple Silicon. Benchmarked on Apple M5 Pro / macOS 26.5.

Built on the 2026-05-29 base-checkpoint update.

Two models × 4 latency tiers = 8 bundles.

• latin/ — one Latin-script-pruned vocab (2828 tokens) shared by en / es / fr / it / pt / de (smaller, faster joint).
• multilingual/ — the full 13087-token vocab covering every language, including zh / ja (and 100+ more via prompt_id).

Each at four chunk sizes — 0.56 s / 1 s / 2 s / 4 s — trading latency for throughput. Pick the folder by script; pass the exact language at inference (--language de-DE). FluidAudio's downloader auto-routes the language to the right folder. Per-language results are in the table below and in manifest.json.

Ship matrix (per-file RTFx, single-stream batch=1)

RTFx = real-time factor (audio-seconds processed per wall-second; higher is faster). WER for Latin-script languages, CER for zh/ja (no word boundaries). All numbers are FLEURS test, full splits (see methodology). The Folder column is which bundle serves that language — the en/es/fr/it/pt/de rows are all the same latin/ model measured per language; zh/ja and Multilingual are the same multilingual/ model.

‡ 560 ms is the lowest-latency tier but off the trained attention tiling — lower throughput and a small quality cost vs 1120 ms. Use 1120 ms+ unless sub-second latency is required.

Full-vocab models (zh / ja / multilingual) are tier-sensitive. The 13087-vocab joint matmul only fits the ANE working-set efficiently at the 2 s tier. At 560 ms the per-chunk joint overhead dominates and throughput collapses to ≈ 21–23 RTFx; use the 2 s tier for zh/ja/multilingual (zh/ja ≈ 84–90, multilingual-en ≈ 80). Throughput at 1 s depends on output density — sparse Latin text (multilingual-en ≈ 71 RTFx) fares far better than dense CJK (zh/ja ≈ 26), since CJK hits the big joint on more decode steps. The Latin-script ships (small joint) are fast at every tier.

Which tier to use

• 2 s (2240 ms) is the recommended default for every model. Latin-script ships run ≈ 130–150 RTFx; zh/ja/multilingual peak here at ≈ 84–90 RTFx. WER/CER is at or near its best, at 2.5 s latency.
• 1 s (1120 ms) for lower latency (1.25 s) on the Latin-script ships at near-full quality (≈ 103–109 RTFx). Avoid for zh/ja/multilingual (≈ 26 RTFx).
• 0.56 s (560 ms) only when sub-second latency is mandatory; off the trained tiling, so throughput and quality both dip. Not recommended for zh/ja/multilingual (≈ 21–22 RTFx).
• 4 s (4480 ms) for offline/long-form. Within noise of 2 s for the Latin-script ships, so 2 s usually dominates.

Recipe

All ships share: LAYERPOS [42,13] mixed-precision encoder (first/last 3 Conformer layers INT8, middle 18 layers 6-bit palettized — ~55% encoder size cut vs FP16, WER-neutral) + B1 decoder⊕joint fusion + triple-stage pipelining.

Vocab handling differs by script:

• Latin-script languages (en/es/fr/it/pt/de) share one Latin-script-pruned joint — the keep-set is derived from the writing system (all Latin + shared punctuation/digit tokens kept; CJK/Hangul/Cyrillic/Arabic/etc. dropped), not from any test corpus. 2828 tokens, ~5× smaller joint, no test-set overfit and no in-script OOV. One model file serves all six languages.
• Chinese / Japanese / multilingual keep the full 13087-vocab joint — no pruning, no OOV, full character coverage.

The encoder is shared across all languages (a multilingual encoder that selects language via prompt_id) and is byte-identical across the Latin-script and full-vocab ships at each tier — only the decode stack differs.

Usage (FluidAudio)

Each <model>/<tier>ms/ directory is a self-contained bundle. Pick the folder by script (latin for en/es/fr/it/pt/de, multilingual for everything else) and pass the exact language:

fluidaudiocli nemotron-multilingual-transcribe \
    --input audio.wav \
    --model-dir latin/2240ms \
    --language de-DE

The FluidAudio auto-downloader routes --language to the correct folder automatically. Models are shipped as compiled .mlmodelc (immediate load on Apple Silicon).

Folder layout

<model>/<tier>ms/
  preprocessor.mlmodelc
  encoder.mlmodelc          # LAYERPOS [42,13], byte-identical across both models per tier
  decoder.mlmodelc
  joint.mlmodelc
  decoder_joint.mlmodelc    # B1 fusion (default decode path)
  metadata.json
  tokenizer.json

<model> ∈ {latin, multilingual}; <tier> ∈ {560, 1120, 2240, 4480}. latin serves en/es/fr/it/pt/de (shared Latin-script vocab); multilingual serves zh/ja and 100+ languages via prompt_id (full vocab). A top-level manifest.json indexes both models, all tiers, and per-language benchmark numbers.

iOS 17

All latin/ and multilingual/ bundles target iOS 17+ / macOS 14+. The encoder uses an iOS 17-compatible quantization (INT8 cuff via affine_dequantize

• 6-bit palettized middle); the 6-bit middle is unchanged from the prior iOS 18 build. Speed and WER are identical to that iOS 18 build — verified on LibriSpeech test-clean (M5 Pro, 2 s tier): 120 RTFx, 3.67% WER for both targets.

Notes

• Latin-script ships are domain-general. The vocab keep-set is defined by the Latin writing system, not derived from any evaluation corpus, so there is no test-set overfit and no out-of-vocabulary loss for any Latin-script text.
• zh/ja use the full-vocab model (no pruned keep-set), so they have no OOV limitation and cover the full character inventory — at the cost of throughput below the 2 s tier (use 2 s).
• The multilingual full-vocab model (13087) supports 100+ languages via prompt_id — use it when broad coverage matters more than per-language speed.

Benchmark methodology

Apple M5 Pro, macOS 26.5, coremltools 9.0, CoreML iOS 17 target, .cpuAndNeuralEngine routing. Single-stream, batch=1, per-file sum-aggregate RTFx (matches the Open ASR Leaderboard convention). All languages evaluated on FLEURS test, full splits. WER for Latin-script languages, CER for zh/ja, via HuggingFace normalization. No inverse text normalization is applied, so FLEURS' digit-bearing utterances inflate WER by ~1–2 pp relative to number-normalized references; FLEURS is also multi-domain, so these numbers run higher than LibriSpeech/MLS would for the same model.

License & attribution

Derived from the base model nemotron-3.5-asr-streaming-0.6b, released by NVIDIA under the OpenMDW-1.1 License and marked ready for commercial use. This CoreML port is a derivative of those weights and is distributed under the same OpenMDW-1.1 license; commercial use and redistribution are permitted under its terms. Retain this attribution to NVIDIA and the OpenMDW-1.1 notice when redistributing.

Weights are quantized/pruned post-training only — no retraining, no fine-tuning, no calibration-data fitting.