Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Bengali
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Rakib/whisper-tiny-bn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rakib/whisper-tiny-bn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rakib/whisper-tiny-bn")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Rakib/whisper-tiny-bn") model = AutoModelForSpeechSeq2Seq.from_pretrained("Rakib/whisper-tiny-bn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 387d6000269b5b5fa440ea5141b8f0fbeb31bed1e4f2898a2e1f8c7e28adb133
- Size of remote file:
- 151 MB
- SHA256:
- 725527cf806b2b810b7dabce008c98d1741d7f139943f9233f0ba9f51c1a1dfc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.