Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e71fed01e4e9ab8593888454edb33de1383dba9c0d39834835e64953bfb06dc
- Size of remote file:
- 6.17 GB
- SHA256:
- c73edab0421ec6ed9f1642427dd4a9916e093629b2be41f9934429f7b4132a54
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