Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-medium-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-medium-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-medium-semantic")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-medium-semantic") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4ed2836c69730557c8a5f94579050e562a5cf07477fb3dc87b2f4dac80f36ae
- Size of remote file:
- 1.53 GB
- SHA256:
- c837ce74a9ba4efbff2b723229b56aa276f35b374c8cc8a985d7ad6900e42f1c
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