Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-small-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-small-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-small-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-small-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-small-beta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59683b1e57aa85e6e3ab41cba92253b49c94e6c9107263d80e4200160ec7ac9c
- Size of remote file:
- 967 MB
- SHA256:
- 6ac36fbe25a92e0ba4aaf7fe29f7c3feef112830cb8080425bed847266f01aef
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