Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from NbAiLab/nb-whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 148 MB
-
https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/ggml-model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-base/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/ggml-model.bin
148 MB
- Xet hash:
- 051ea3cb5c2cdfdf99adf0546d44bbd93dcbf5ae891843add4ea425e82408aa0
- Size of remote file:
- 148 MB
- SHA256:
- 05fc04a7f16b634b99e0cbbe5e4e78a00469b834219e83df39ab4f8bca5c4b97
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