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: 487 Bytes
58b0065 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"feature_extractor": {
"chunk_length": 30,
"dither": 0.0,
"feature_extractor_type": "Qwen3ASRFeatureExtractor",
"feature_size": 128,
"hop_length": 160,
"min_length": 8000,
"n_fft": 400,
"n_samples": 480000,
"n_window": 50,
"nb_max_frames": 3000,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000
},
"processor_class": "Qwen3ASRProcessor",
"timestamp_segment_time": 80
}
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