Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_state.bin from NbAiLab/nb-whisper-large: direct link, hf CLI and curl.
- Browser
- Download file 6.33 kB
-
https://huggingface.co/NbAiLab/nb-whisper-large/resolve/main/training_state.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-large/training_state.bin
-
curl -L -o training_state.bin https://huggingface.co/NbAiLab/nb-whisper-large/resolve/main/training_state.bin
6.33 kB
- Xet hash:
- 34c40052514ce725afebce32ffd7b1cdccbe32a972f8f175c40ccd122d7263bf
- Size of remote file:
- 6.33 kB
- SHA256:
- bfa44952157b2df6ef723e428150c301ea84424aa51a9d90036130470e3b02c8
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