Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-medium-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-medium-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-medium-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-medium-verbatim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7973bcf4bf4129b9ecf91163be0ce441a83cf39d391449df8195ad99dc4a889
- Size of remote file:
- 3.06 GB
- SHA256:
- b49bd3984d891986f8edb94b31c49b679b17b41867a13cf700ae8a24a7c7d6e2
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