Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use mouseyy/result_data-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mouseyy/result_data-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mouseyy/result_data-1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mouseyy/result_data-1") model = AutoModelForCTC.from_pretrained("mouseyy/result_data-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81eba1db313b817d1264a7ca8ba3b7141432bf5aa406f7a5771da823ab94dc4d
- Size of remote file:
- 5.37 kB
- SHA256:
- f6f3d72c535a1754f0c4fa9fbb95f1ad813e5e3b1bf18f80a201a33cac7f55e0
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