Automatic Speech Recognition
Transformers
Safetensors
English
qwen3_asr
feature-extraction
speech-recognition
asr
english
distillation
looped-transformer
parameter-sharing
qwen3-asr
custom_code
Eval Results (legacy)
Instructions to use oruk/oruk-loop-153M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oruk/oruk-loop-153M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oruk/oruk-loop-153M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("oruk/oruk-loop-153M", trust_remote_code=True) model = AutoModel.from_pretrained("oruk/oruk-loop-153M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 124 Bytes
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