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
| # Checkpoints | |
| This directory contains the released LatentASR adapter checkpoint: | |
| - `latentASR_adapter.pth` | |
| The checkpoint stores only the lightweight LatentASR modules: | |
| - `init_proj` | |
| - `delta_proj` | |
| - `step_proj` | |
| - `step_embed` | |
| - `log_scale` | |
| - `value_head` | |
| - `injection_gate` | |
| It does not include the frozen `Qwen/Qwen3-ASR-0.6B` backbone. Evaluation and | |
| inference load the base model from Hugging Face and then attach this adapter. | |
| Checkpoint metadata: | |
| - Source run: `activation_500_epoch10.pth` | |
| - Base model: `Qwen/Qwen3-ASR-0.6B` | |
| - Latent budget: `N=4` | |
| - Training set size: 500 utterances | |
| - Adapter parameters: 5,251,077 | |
| - SHA256: `f0ce39fa5e6952fced6992508f3e2b32ea8467442b545678781a0f04e64f2430` | |