Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-large-verbatim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9da75a7163447ee2dfa25bb110011bf4bc2eeff4ef74daca46f22b911771cebf
- Size of remote file:
- 3.09 GB
- SHA256:
- 8434db3e74f6c1ed84bf0bc1728aa4ee3c8567a512ad94817ca50f3b19ec31a5
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