Instructions to use Subhadeep/whisper-base-bn-Dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subhadeep/whisper-base-bn-Dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Subhadeep/whisper-base-bn-Dev")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Subhadeep/whisper-base-bn-Dev") model = AutoModelForSpeechSeq2Seq.from_pretrained("Subhadeep/whisper-base-bn-Dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a51179f2ebe87dd89ee4bdedff49de9950da085f1293bc76f67eddb42c8d5d30
- Size of remote file:
- 290 MB
- SHA256:
- a366fcaa2e8ed684b8f3de544eee0b619cc1b4d24fcb633618fcd0aa5639975b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.