Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use kazeric/whisper-small-dv-streaming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kazeric/whisper-small-dv-streaming with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kazeric/whisper-small-dv-streaming")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kazeric/whisper-small-dv-streaming") model = AutoModelForSpeechSeq2Seq.from_pretrained("kazeric/whisper-small-dv-streaming", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7fac1bbb6d78c0560b70b61e5ced71267a4430a51f5b10516ec424a945a9ad0
- Size of remote file:
- 5.5 kB
- SHA256:
- cfe7337c6603d03db6c6032fa9470c01f093a4b8b4a09344adce58911b4a6ee7
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