Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use alidenewade/unit_5_exercise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alidenewade/unit_5_exercise with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alidenewade/unit_5_exercise")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alidenewade/unit_5_exercise") model = AutoModelForSpeechSeq2Seq.from_pretrained("alidenewade/unit_5_exercise", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb81cfbeb8dae95a80d16d23253fe4884afb53e231a3fc7529cd973daa9e5342
- Size of remote file:
- 151 MB
- SHA256:
- 3fef07f2c7dc180455da21f9af96ed7f8e03d7bf06253d810f1761aea7331053
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