Instructions to use Subhadeep/whisper-tiny-bn-Dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subhadeep/whisper-tiny-bn-Dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Subhadeep/whisper-tiny-bn-Dev")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Subhadeep/whisper-tiny-bn-Dev") model = AutoModelForSpeechSeq2Seq.from_pretrained("Subhadeep/whisper-tiny-bn-Dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa92cf836dd1c81e8168d499609adba198509f2ed074a146b2b30bef003700af
- Size of remote file:
- 151 MB
- SHA256:
- ee78a24535b3ac790fec640588e5745be13c8b10189f75914b0cbdef2a9a45e6
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