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
File size: 707 Bytes
262fa3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # 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`
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