Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Northern Sami
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use NbAiLab/whisper-large-sme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/whisper-large-sme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/whisper-large-sme")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/whisper-large-sme") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/whisper-large-sme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| #!/usr/bin/env python | |
| # !pip install git+https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets.git | |
| from multiple_datasets.hub_default_utils import convert_hf_whisper | |
| model_name_or_path = './' | |
| whisper_checkpoint_path = './whisper-large-sme.pt' | |
| convert_hf_whisper(model_name_or_path, whisper_checkpoint_path) | |