Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large-des23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large-des23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large-des23")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large-des23") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-large-des23") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d75c9f35fd0678c9b18495c25324dc817ddfe42aadbc1f5e62b3e80239d511a
- Size of remote file:
- 3.09 GB
- SHA256:
- 9329821cdec54be279ed1ae2565f273d8f7dbd5be6db9535c186a751dd3b1b34
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