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
| # Hugging Face Release | |
| The public model repo is intended to be: | |
| ```text | |
| voidful/latentASR | |
| ``` | |
| The repository hosts: | |
| - LatentASR code | |
| - adapter checkpoint | |
| - documentation | |
| - model card | |
| - reproducibility outputs | |
| ## Upload | |
| From this project root: | |
| ```bash | |
| python hf_upload/upload_to_hf.py --repo-id voidful/latentASR | |
| ``` | |
| The script creates the model repo if needed and uploads the current folder. | |
| ## Download Checkpoint Programmatically | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| ckpt = hf_hub_download( | |
| repo_id="voidful/latentASR", | |
| filename="checkpoints/latentASR_adapter.pth", | |
| ) | |
| print(ckpt) | |
| ``` | |