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:
- a1a59ecd2e037289efe9d04e0d56c11a2b2659615db0493ff550c7a308f5d512
- Size of remote file:
- 3.64 kB
- SHA256:
- 3abb8bd7649ff6bd33f721c3b00dc3f9026e7e6cc67b1cf516c13afed772e97b
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