Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5450c5f5a164199b44bc00862063c7d5a2cff2e3c5e0085abd6d6f206bb86ec
- Size of remote file:
- 6.17 GB
- SHA256:
- aae7d24b39d99f56709027a3b5d524cfdd81748844cfeae52ab069f93507c2d7
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