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 training_state.bin from NbAiLab/nb-whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 6.32 kB
-
https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/training_state.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-base/training_state.bin
-
curl -L -o training_state.bin https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/training_state.bin
6.32 kB
- Xet hash:
- 1d5af59493516efc3501eda02b1dbbcf2c6865947d22ef2e448d1b56accca76a
- Size of remote file:
- 6.32 kB
- SHA256:
- c5673a9c0eea5d0a137399a1a59e79cf0a7fb754305124b292d0fa1f1b6448aa
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