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MarkChen1214/Breeze-ASR-25-MLX-5bit
Breeze-ASR-25-MLX-5bit is a automatic speech recognition model from MarkChen1214. Use it when you need speech turned into text. It is set up for mlx. The card lists the license as apache-2.0.
Uniform affine 5-bit MLX conversion of MediaTek-Research/Breeze-ASR-25, an Apache-2.0 Whisper-large-v2 fine-tune for Taiwan Mandarin and Mandarin-English code switching.
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From the Hugging Face model README
Uniform affine 5-bit MLX conversion of
MediaTek-Research/Breeze-ASR-25,
an Apache-2.0 Whisper-large-v2 fine-tune for Taiwan Mandarin and
Mandarin-English code switching.
This is an independent community conversion, not an official MediaTek release. Every quantizable Linear and Embedding module uses 5 bits with group size 64. The weight file is 1,069,126,236 bytes, 65.32% smaller (2.88x) than the verified 3,082,904,296-byte BF16 MLX base.
model.safetensors: 1,069,126,236 bytes9d04f350111b6a0234117e4e3d3b50adff1c64b9c6e8a8811028db855b4d0e5bquant_bitmap.jsonThe included mlx_whisper_mixed.py provides the verified short-form runtime.
It uses fused attention, text-only greedy decoding, automatic language
detection, a 64-token ceiling, and conservative exact-loop termination.
from mlx_whisper_mixed import load_model, transcribe_short_with_model
model = load_model("MarkChen1214/Breeze-ASR-25-MLX-5bit")
result = transcribe_short_with_model(
model,
"audio.wav",
context_seconds=10,
)
print(result["text"])
Audio longer than the selected context is rejected rather than silently
truncated. Use context_buckets=(10, 30) for variable short-form input, or
chunk long-form audio explicitly.
Tested with Python 3.12, MLX 0.32.0, and mlx-whisper 0.4.3.
The primary evaluation uses all 4,976 examples (5.155 hours) from the
Common Voice 16.1 zh-TW test split. It specifically reads the mirror's
original column containing raw Mozilla audio, not its MP-SENet-denoised
audio column. Nothing is sampled, dropped, or truncated: 4,974 clips use a
10-second context and the two longer clips use 30 seconds.
Both MLX models use the same M4 Pro, audio cache, utterance order, automatic
language detection, text-only greedy decoding, fused attention, and 64-token
ceiling. CER applies OpenCC s2tw to both sides and removes whitespace.
| Artifact | Weights | Errors / chars | CER | Stable-power RTFx* |
|---|---|---|---|---|
| MLX BF16 control | 3,082.9 MB | 3,861 / 39,477 | 9.78% | 14.7x |
| MLX Q5 candidate | 1,069.1 MB | 4,065 / 39,477 | 10.30% | 17.7x |
Q5 adds 0.5168 CER points versus the paired BF16 control. A 100,000-draw paired utterance bootstrap gives a 95% interval of +0.2695 to +0.7824 points. Both models produced 4,976/4,976 non-empty transcripts.
MediaTek reports 7.97 MER for Breeze-ASR-25 (Twister) on
CommonVoice16-zh-TW in the
official paper. That value is useful
author-reported context, but it is not used as the quantization baseline:
the paper does not disclose its exact decoder or text-normalization contract.
The paired MLX BF16 result above is the reproducible measure of Q5's quality
cost.
*Quality and timing use the same complete 4,976 row identities and references.
Quality uses raw original audio; timing uses the mirror's MP-SENet-denoised
audio column because those full runs maintained a valid stable power state.
The stable normal-power timing measured 14.7x BF16 and 17.7x Q5,
making Q5 1.20x faster in the matched lane.
The raw-audio quality runs occurred during a low-battery incident, so their observed 5.3x BF16 and 13.9x Q5 figures are deliberately rejected and are not reported in the table. This distinction prevents a charger transition or near-empty-battery throttling from becoming a speed claim.
Warm-up and one-time model loading are excluded from RTFx. WAV loading, mel
preprocessing, and decoding are included. Hardware is a MacBook Pro Mac16,7
with Apple M4 Pro and 48 GB memory.
On a deterministic 250-row ML2021 Mandarin-English code-switch set, the same automatic-language runtime measured 10.44% CER / 10.3x RTFx for Q5 and 10.05% CER / 8.6x RTFx for BF16. This remains secondary evidence; Common Voice zh-TW is almost entirely Mandarin and cannot establish broad English code-switch quality by itself.
MediaTek-Research/Breeze-ASR-25 at
cffe7ccb404d025296a00758d0a33468bec3a9d0c5d952b3bc03ea277209aff0ef5b5c4c055d74449ff794c02d8f4e315fdef6b6eoleedi/Breeze-ASR-25-mlx at
172151e3fd93ab6dc841d639f23dd48b24bc83012.98e-089d04f350111b6a0234117e4e3d3b50adff1c64b9c6e8a8811028db855b4d0e5boriginal raw audio for CER, denoised audio for RTFx).
Rebenchmark raw audio on the deployment machine if that preprocessing
distinction matters to capacity planning.Apache-2.0, following the source checkpoint. Please cite the original Breeze ASR 25 authors and model when using this conversion.