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
Utility Scripts
summarize_lr_showcase.py: summarize one experiment output directory intoshowcase_report.md.summarize_threshold_sweep.py: summarize threshold-sweep JSON/log outputs.write_full_asr_experiment_report.py: build the paper-facing multilingual/English report from existing result folders.analysis/per_sample_difficulty.py: per-sample difficulty analysis used for the paper discussion.hparam/search_alpha.sh: legacy alpha/tolerance grid search helper.