Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f23ab26926f2278d963d0f7fa86f3932e3473224c499a8e19ccdfbd5b305f406
- Size of remote file:
- 1.84 GB
- SHA256:
- 75859dbad91eaa8eb4c072a64eac451cb1a62f0ef20021a16742d4b8b34b3ba4
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