The 0.349 GB safetensor uses group-64 affine quantization for 265 eligible
matrices: 100 attention matrices at 3-bit and 165 encoder, feed-forward, and
pointwise-convolution matrices at 4-bit. The eight predictor and joint matrices
remain BF16, as do normalization, depthwise-convolution, bias, and other
ineligible weights.
It is 25.8% smaller than the 0.470 GB external encoder-INT4 model while
matching its macro WER on a secondary 387-row domain panel. On the full
6,206-utterance English gate, it is the throughput-focused option: 51.3% faster
and 1.04% smaller than the separately published Unified mixed-precision model,
with 0.06 point higher macro WER.
Full three-collection evaluation
Measured locally on Apple Silicon with MLX and public parakeet-mlx inference.
The gate uses the complete LibriSpeech test-clean (2,620), complete
LibriSpeech test-other (2,939), and complete FLEURS en_us test (647) splits.
Every artifact scored all 6,206 utterances, totaling 12.51 audio hours,
with zero skipped references. Macro WER gives each collection equal weight.
Model
Weights (GB)
LS clean WER
LS other WER
FLEURS en_us WER
Macro WER
RTFx
Unified FP32 MLX
2.474
1.31%
2.81%
3.85%
2.66%
56.4x
Unified mixed 3/4/6-bit
0.352
1.36%
2.90%
4.07%
2.78%
59.8x
TDT v2 FP32 MLX
2.472
1.31%
2.82%
4.18%
2.77%
85.7x
This TDT v2 mixed 3/4-bit/BF16 model
0.349
1.34%
2.89%
4.28%
2.84%
90.5x
Against FP32, this model trades 0.07 macro-WER point for an 85.90% reduction
in weight storage. There is no universal winner among the compressed models:
Unified has the cautious quality edge, while this TDT release is the
throughput choice.
Secondary meeting and financial-speech panel
An earlier seed-2026 comparison sampled up to 100 examples from LibriSpeech
clean, LibriSpeech other, AMI, and Earnings-22. AMI contributed 87 valid rows,
for 387 matched utterances. It remains useful domain evidence, but is not the
headline quality gate.
Model
Weights (GB)
LS clean
LS other
AMI
Earnings-22
Macro WER
RTFx
TDT v2 FP32 MLX
2.472
1.26%
2.61%
9.24%
10.70%
5.95%
76.9x
External TDT v2 encoder-INT4
0.470
1.48%
2.39%
9.75%
10.81%
6.11%
85.1x
This TDT v2 mixed 3/4-bit/BF16 model
0.349
1.57%
2.67%
9.54%
10.61%
6.10%
90.3x
Repo Parakeet Unified mixed 3/4/6-bit
0.352
1.35%
2.28%
11.06%
10.33%
6.26%
55.3x
The TDT candidate and external INT4 model are a practical quality tie on this
panel. Their 0.01-point difference is not an accuracy claim; the useful result
is retaining that quality class with 0.121 GB less weight storage.
Allocation and rejected candidates
The calibration set contains 12 FLEURS en_us validation clips selected with
seed 31415; it is disjoint from the final test rows. Four-layer attention,
feed-forward, and pointwise-convolution regions plus the predictor and joint
were independently fake-quantized against a BF16 teacher.
The accepted allocation is:
encoder attention in layers 0–15 and 20–23: affine Q3;
encoder attention in layers 16–19: affine Q4;
all encoder feed-forward matrices: affine Q4;
encoder pre-projection and all pointwise convolutions: affine Q4;
predictor embedding, four LSTM matrices, and three joint matrices: BF16.
The broader screen rejected several attractive-looking size points. Uniform Q3
reached 0.272 GB but 11.88% macro WER on the seed-42 screen. Uniform Q4 reached
0.349 GB but 8.17%. A 0.323 GB Q3-attention/Q3-FFN composition reached 8.35%,
showing that low isolated calibration drift did not always compose safely.
The exact allocation is included as quantization_recipe.json. The compatibility
loader is included as parakeet_unified_mlx.py; conversion code, sensitivity
results, rejected profiles, and matched benchmark evidence are maintained in
yuting1214/model-lab.
Usage
Install the public runtime and loader dependencies:
