Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9fec7667a5e92c63ffa5af2ea4ad10258fa7bd375f734d69a1806c27a815bd7a
- Size of remote file:
- 3.09 GB
- SHA256:
- 47e4621d27695260db7e1a1f93f17c381c69d27d1675530a7ed0b12559c79c65
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