Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use razhan/whisper-base-sdh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razhan/whisper-base-sdh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="razhan/whisper-base-sdh")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("razhan/whisper-base-sdh") model = AutoModelForSpeechSeq2Seq.from_pretrained("razhan/whisper-base-sdh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a6106a20e89d02cb43c00760c03807ee1ac0d8a42bb41a6a3e9467f650fe14f
- Size of remote file:
- 5.5 kB
- SHA256:
- 0510a33fe84a13f0478b767b932097b830afd6754df0d15881908767384b113f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.