Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base-semantic")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-base-semantic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from NbAiLabBeta/nb-whisper-base-semantic: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-base-semantic/resolve/main/model.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-base-semantic/model.bin
-
curl -L -o model.bin https://huggingface.co/NbAiLabBeta/nb-whisper-base-semantic/resolve/main/model.bin
290 MB
- Xet hash:
- 928571fdd6886baf9ab6a90aa9b3f64098d87b1c22b5f69eca2f7c9a3d676328
- Size of remote file:
- 290 MB
- SHA256:
- b0003ee7a522ae0872cffb97f7a74499ab878498751cf10c14da2067fd7b7066
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