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
Experiment Runners
This directory contains the maintained entrypoints for LatentASR experiments.
All runners default to Qwen/Qwen3-ASR-0.6B and ./latent_qwen_asr_best.pth.
The checkpoint itself is intentionally ignored by Git.
Runners
run_english_clean_and_noise.sh: English clean suite plus optional SNR noise sweeps.run_multilingual_streaming.sh: FLEURS 30 and MLS public7 streaming evaluation.run_threshold_sweep.sh: Value-head halting threshold sweep.run_all_training_modes.sh: Sequential baseline, prompt tuning, LoRA, and LatentASR training.run_legacy_generalization.sh: older SpeechTest generalization/noise runner kept for reproducibility.
Root-level scripts with the old names are thin compatibility wrappers around these maintained files.
Common Environment Variables
LATENT_CKPT: latent adapter checkpoint path.MODEL_ID: HuggingFace model ID.PYTHON_BIN: Python executable override.MAX_SAMPLES_PER_CONFIG: per-config cap;0means full split.OUT_DIR: output directory.RESUME=1: skip JSON outputs that already exist.
Full experimental JSON/log outputs are written under eval_runs/ and are not
committed to Git.