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 requirements.txt from voidful/latentASR: direct link, hf CLI and curl.
- Browser
- Download file 167 Bytes
-
https://huggingface.co/voidful/latentASR/resolve/main/requirements.txt
- Command line
-
hf download hf://voidful/latentASR/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/voidful/latentASR/resolve/main/requirements.txt
167 Bytes
| torch>=2.6 | |
| torchaudio>=2.6 | |
| transformers>=4.57 | |
| datasets>=3.6 | |
| huggingface-hub>=0.34 | |
| qwen-asr>=0.0.6 | |
| qwen-omni-utils>=0.0.8 | |
| jiwer>=4.0 | |
| peft>=0.18 | |
| numpy>=1.24 | |
| tqdm>=4.66 | |