[!Note]
Feather is Microsoft's newest family of mini speech-recognition models, designed
for fast on-device transcription with a tiny CPU footprint. This checkpoint —
feather-0.2b-onnx-int8 — is the German-specialised member of the family: a
streaming Conformer-Transducer (RNN-T) of about 200 M parameters, quantised to
INT8 weight-only and packaged for onnxruntime-genai.
The encoder is a "LiteConformer" — a slimmed-down Conformer variant that
diverges from the standard NVIDIA Nemotron Conformer in three ways, specifically
chosen to reduce the per-layer op count on CPU:
Block
Standard Nemotron Conformer
Feather LiteConformer encoder
FFN
Macaron: two FFN blocks per layer (feed_forward1 + feed_forward2, each with fc_factor = 0.5)
Single FFN per layer (feed_forward2 only, fc_factor = 1.0) — drops one LayerNorm + two Linear ops per layer
nn.RMSNorm (weight-only, no bias, no mean subtraction) — fewer ops + no centring pass
Activation
Swish / SiLU (x * sigmoid(x)) in FFN and convolution
GELU (matches faster ORT fused BiasGelu kernel) in FFN and convolution
Across the 20-layer encoder this removes roughly 40 LayerNorm sub-ops (centring
bias add) and 40 Linear projections worth of FFN compute compared to the
Macaron baseline, while RMSNorm and GELU map directly to fused
SkipSimplifiedLayerNormalization / BiasGelu kernels in ONNX Runtime CPU EP.
After export, the encoder is graph-fused with OrtTransformersOptimization
(model_type=conformer, MHA fusion disabled for accuracy) and the encoder weights
are quantised with MatMulNBitsQuantizer (bits=8, block_size=32, symmetric,
k-quant). The decoder and joint networks are kept FP32 — they are tiny and
benefit little from quantisation. A bundled Silero VAD is included for
optional voice-activity-driven streaming.
All numbers are streaming, greedy-RNNT, CPU, no VAD. Reference normaliser is
BasicMultilingualTextNormalizer from
huggingface/open_asr_leaderboard.
Dataset
Split
Utts
Hours
WER
RTF (CPU)
FLEURS de_de
test
862
3.15
10.22 %
14.9×
Common Voice 17 de
test
16,206
27.96
10.28 %
12.5×
MLS german
test
3,394
14.29
9.28 %
14.1×
VoxPopuli de
test
1,968
4.92
13.75 %
13.1×
RTF measured single-threaded on a Linux x86-64 CPU host with onnxruntime-genai's
streaming Generator. No batching.
Comparison vs NVIDIA Nemotron Multilingual (German)
For reference, the official
nvidia/nemotron-asr-streaming-multilingual-0.6b
model — same architecture family, ~3× the parameters, covers 40+ languages —
evaluated on the same German test splits scores:
Dataset
Nemotron Multilingual (0.6B) ONNX INT4
Feather 0.2B (this model)
Δ (Feather − Nemotron)
FLEURS de_de
12.25 %
10.22 %
−2.03
Common Voice 17 de
12.80 %
10.28 %
−2.52
MLS german
9.91 %
9.28 %
−0.63
VoxPopuli de
16.90 %
13.75 %
−3.15
Feather is German-specialised and a third of the size in terms of number of parameters, so it both transcribes
German more accurately and runs noticeably faster on CPU — in our streaming
benchmarks Feather achieves ~2× the RTF of Nemotron Multilingual 0.6B ONNX INT4 on the
same CPU under identical decoding settings.
Feather 0.2B German was trained on a nearly 10,000-hour German speech mixture
drawn from several complementary sources:
Source
Role in the mixture
Multilingual LibriSpeech (MLS)
Read audiobook-style German speech
Common Voice
Crowd-sourced German speech with diverse speakers and recording conditions
VoxPopuli
Parliamentary / broadcast-style German speech
CML-TTS
Synthetic German speech
YODAS / Granary
Large-scale German speech data
This mixture was selected to improve robustness across domains while keeping the
model specialised for German-only streaming ASR.
Responsible AI / demographic evaluation
The model was evaluated on Common Voice 17 German using available demographic
metadata across gender, age, and accent/dialect. The overall WER on the full
Common Voice German test split is 10.28%.
Gender
Performance remains relatively consistent across gender groups. Male speakers
achieve 12.75% WER, female speakers achieve 13.89% WER, and samples with
unknown gender metadata achieve 10.06% WER.
While the male and female subsets show moderately higher WER than the overall
average, these subsets represent a substantially smaller portion of the
evaluation set compared to samples with unknown metadata. This limits the
statistical strength of subgroup comparisons. No severe gender-specific
degradation is observed.
Age
Age-group WER ranges from 10.64% to 15.04%. The fifties group performs
best at 10.64% WER, while the forties group shows the highest WER at
15.04% WER. Most age groups cluster between roughly 11% and 14% WER,
indicating generally stable performance across age demographics despite uneven
subgroup sizes.
Accent / dialect
The model demonstrates robust performance across major German-speaking regional
variants. Standard German (Deutschland Deutsch) achieves 9.09% WER,
Austrian German achieves 10.22% WER, and Swiss German achieves
10.99% WER.
The spread between the evaluated accent groups remains below 2 percentage
points, indicating good generalisation across the evaluated German dialectal
varieties.
Important caveat
The majority of evaluation samples contain unknown demographic metadata, which is
inherent to the Common Voice dataset and limits the statistical power of this
subgroup analysis. Some demographic subsets, particularly female speakers and
certain age or accent categories, contain relatively small sample counts and
should therefore be interpreted cautiously.
Summary
The model demonstrates stable performance across the evaluated demographic
dimensions and German-speaking regional accents. No subgroup with meaningful
representation exhibits catastrophic degradation, and the results indicate
reasonable robustness for a low-latency streaming German ASR system.
Limitations
German only. Inference on other languages is unsupported and will produce
garbled output.
Streaming chunk = 560 ms. The encoder caches are sized for this latency
setting and cannot be changed at inference time.
CPU-optimised. The encoder is INT8 weight-only quantised and graph-fused
for ONNX Runtime CPU EP; GPU EP will run but will not benefit from the
optimisations.
Inconsistent capitalization and punctuation. The model might not always transcribe words with correct capitalization or output appropriate punctuation.
License
MIT.
Acknowledgements
Base architecture: NVIDIA Nemotron Conformer-Transducer family.
Optimisation stack: Microsoft Olive + ONNX Runtime + onnxruntime-genai.
