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:
- 1368ca9f69f2129c1032f48ee31d0672d217aa38a2daccc796c1dab44ee22d46
- Size of remote file:
- 151 MB
- SHA256:
- eb1a9006d0a38fb57f3facf21e30ca6cbef5b9d1a7f2f5bc78757fa693f1b2af
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