Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cf7b38a9b9471f5283c59ac9806dd0cb54546a26afd17d6faf843d0a0c7d825
- Size of remote file:
- 3.47 GB
- SHA256:
- aceec3b8f724f615b5795e2c4b0167cfe8eb31ac63ed9aec1f2ed2046121c523
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.