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 tf_model.h5 from NbAiLabBeta/nb-whisper-base-semantic: direct link, hf CLI and curl.
- Browser
- Download file 291 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-base-semantic/resolve/main/tf_model.h5
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-base-semantic/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/NbAiLabBeta/nb-whisper-base-semantic/resolve/main/tf_model.h5
291 MB
- Xet hash:
- 42aec7ab46c0e35c74720d1399101e456cad1307c3c7120aec3e3db8d8625ea8
- Size of remote file:
- 291 MB
- SHA256:
- 308738ca94ee73cef46de1fd1760179756e8971dc35300dbcf0012f02d720090
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