Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Dutch
whisper
whisper-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use GeoffVdr/whisper-medium-nlcv11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GeoffVdr/whisper-medium-nlcv11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GeoffVdr/whisper-medium-nlcv11")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("GeoffVdr/whisper-medium-nlcv11") model = AutoModelForSpeechSeq2Seq.from_pretrained("GeoffVdr/whisper-medium-nlcv11") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65e9cee6a61e27b3ec459d11df189895a1d625879625a767a5f5b67ca2504d42
- Size of remote file:
- 3.06 GB
- SHA256:
- ce1be6a9a54a34551bbd1c7519159844400b2c9d36b856cd327f088a024bf518
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.