Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use iqbalasrif/whisper-tiny-hyperparameter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iqbalasrif/whisper-tiny-hyperparameter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="iqbalasrif/whisper-tiny-hyperparameter")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("iqbalasrif/whisper-tiny-hyperparameter") model = AutoModelForSpeechSeq2Seq.from_pretrained("iqbalasrif/whisper-tiny-hyperparameter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 552b2e7da34cd0cd5242e4255cf36584b3d10dc55d72d45d6096e2a0315b31e3
- Size of remote file:
- 5.37 kB
- SHA256:
- 32aa2deb8106dc2b84ec85669778f76eab69a5fb35be741e7625b0d96cfd6257
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.