Instructions to use bhattasp/w_f1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhattasp/w_f1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bhattasp/w_f1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bhattasp/w_f1") model = AutoModelForSpeechSeq2Seq.from_pretrained("bhattasp/w_f1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c4f0c4da530d875bc0fa52732811a797fdb8b92a0e2dc0ab7c5b23f76269f16
- Size of remote file:
- 5.3 kB
- SHA256:
- e11b4f21fc41001d7c8979a753995989acc150463bbc2ba69354b028f207b1d9
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