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
Download training_state.bin from NbAiLab/nb-whisper-large-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 2.02 kB
-
https://huggingface.co/NbAiLab/nb-whisper-large-verbatim/resolve/main/training_state.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-large-verbatim/training_state.bin
-
curl -L -o training_state.bin https://huggingface.co/NbAiLab/nb-whisper-large-verbatim/resolve/main/training_state.bin
2.02 kB
- Xet hash:
- f233ff2a62117cda5220d926004f1e7f70e8b2b101aa0eefaea752742c5128d1
- Size of remote file:
- 2.02 kB
- SHA256:
- 8b4e200cf930afba4fd11f9ec4526ffba82a0327e3226d124d06213bbeb2f309
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