Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large-semantic")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-large-semantic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabBeta/nb-whisper-large-semantic: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-large-semantic/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-large-semantic/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabBeta/nb-whisper-large-semantic/resolve/main/flax_model.msgpack
3.09 GB
- Xet hash:
- 04144941d651aa5959fa52edf3aea20b915072c67645119891e7be79ea24e6a1
- Size of remote file:
- 3.09 GB
- SHA256:
- 07c62f0dee5ecd21ba23ff59a912594a3eabd4edc49e977413bf1b801d25955e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.