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:
- 4274398afd77e8f9ee0a0005de715e90392c10e4e51bcd3898ee21fa148e4a2a
- Size of remote file:
- 3.64 kB
- SHA256:
- c5e30fc163274f0fc7f258caba48980e9a099c45d586f074c36606a23f42878e
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