Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-medium-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-medium-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-medium-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-medium-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-medium-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7235cb28f8ef3b0a30cc8a0c4c25f291a72fa1fccd6011a50ccca99723ccd72c
- Size of remote file:
- 2.04 GB
- SHA256:
- 60618092e71641ff2ef2ea57355670ed79fc970dab885cea244e0c1acaf2b3fd
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