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; `0` means 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. | |