Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-medium-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-medium-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-medium-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-medium-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-medium-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf5585820684abcc2d4cb99781590de551544741a5d8095f8c544bae12e291df
- Size of remote file:
- 1.53 GB
- SHA256:
- 6fbf7f8e951af12d5cc9b17ed6f289608480f99a97c45517433940ed1855abc6
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