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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voidful/latentASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download hf_upload/README.md from voidful/latentASR: direct link, hf CLI and curl.
- Browser
- Download file 269 Bytes
-
https://huggingface.co/voidful/latentASR/resolve/main/hf_upload/README.md
- Command line
-
hf download hf://voidful/latentASR/hf_upload/README.md
-
curl -L -o README.md https://huggingface.co/voidful/latentASR/resolve/main/hf_upload/README.md
269 Bytes
| # HF Upload Helper | |
| Run from the repository root: | |
| ```bash | |
| python hf_upload/upload_to_hf.py --repo-id voidful/latentASR | |
| ``` | |
| The script uploads the entire project folder, including the released adapter | |
| checkpoint and documentation, to a Hugging Face model repository. | |