Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use mouseyy/result_data-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mouseyy/result_data-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mouseyy/result_data-5")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mouseyy/result_data-5") model = AutoModelForCTC.from_pretrained("mouseyy/result_data-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9288c1b126cf6dca74ef8640bf1a771102af87e68e7c5fe78a51c39e31a2d2d
- Size of remote file:
- 5.37 kB
- SHA256:
- 67c2c4230ceb4c89b9f5f868d96bac70d824957d8394c867a3feb0bce68b7597
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