Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from NbAiLab/nb-whisper-large-beta: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/NbAiLab/nb-whisper-large-beta/resolve/main/ggml-model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-large-beta/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/NbAiLab/nb-whisper-large-beta/resolve/main/ggml-model.bin
3.09 GB
- Xet hash:
- faf1e31139f6b683f8bb6b17e4df78bf1575c14e95b524fb512ee9652b9a29db
- Size of remote file:
- 3.09 GB
- SHA256:
- 1ed637b012d3826eafa87daee076021361c7f77a2928e41c04d1e781a87c177f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.