Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-tiny-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-tiny-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-tiny-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-tiny-beta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16338fbdd4afc7cfd302e255fdf950f5f2df8c1853b7b1b31d275047cc91bec9
- Size of remote file:
- 22.3 kB
- SHA256:
- 38c8ffeda15361056fc6e9fce517217df95571080794d300785718be612fb5eb
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