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")# pip install -U transformers accelerate # 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 flax_model.msgpack from NbAiLab/nb-whisper-large-beta: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/NbAiLab/nb-whisper-large-beta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-whisper-large-beta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-whisper-large-beta/resolve/main/flax_model.msgpack
6.17 GB
- Xet hash:
- b6c8613a805b0f8d9e0a44a0e2b06e36870e83aa4c7cd30d7580ffbf48f929e6
- Size of remote file:
- 6.17 GB
- SHA256:
- 3a0b889ba811936f2944c4b79f3b17f31e8e6f2a3561114d154d61f9095cfdb9
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