Automatic Speech Recognition
Transformers
qwen3-asr
latent-reasoning
test-time-compute
parameter-efficient
Instructions to use voidful/latentASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/latentASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/latentASR")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voidful/latentASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Results
This release includes JSON and Markdown outputs under results/. The most
important caveat is that the clean benchmark gains are single-run directional
point estimates, not statistically established improvements.
Clean Main Benchmarks
| Model | FLEURS WER | VoxPopuli WER |
|---|---|---|
| Frozen Qwen3-ASR baseline | 4.900 | 9.038 |
| LatentASR, N=4, theta=0 | 4.776 | 8.995 |
Relative WER deltas:
- FLEURS:
-2.54% - VoxPopuli:
-0.47%
Robustness Diagnostics
At SNR 0 dB:
| Dataset | Baseline WER | LatentASR WER | Relative delta |
|---|---|---|---|
| FLEURS | 29.81 | 29.42 | -1.32% |
| VoxPopuli | 19.71 | 19.51 | -1.02% |
Stress broad suite weighted aggregate:
- Baseline WER:
36.843 - LatentASR WER:
36.265 - Delta:
-0.578pp (-1.57%relative)
ASCEND Diagnostic
ASCEND CER:
- Baseline:
57.81 - LatentASR:
48.55 - Relative CER delta:
-16.02%
This is explicitly treated as an overlap-confounded in-domain diagnostic. ASCEND contributes 5/500 activation utterances and speaker-disjointness was not enforced.
Included Result Folders
results/paper500_main_eval_20260518/results/paper500_english_clean_noise_20260518/results/paper500_multilingual_streaming_20260518/results/paper500_threshold_20260518/results/paper500_more_testing_stress_20260525/results/paper500_ascend_20260518/