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
| try: | |
| import whisper | |
| except ImportError: | |
| print("Whisper not found. Try installing with \"pip install git+https://github.com/openai/whisper.git\"") | |
| exit(1) | |
| whisper._MODELS["NbAiLab/whisper-large-sme"] = "https://huggingface.co/NbAiLab/whisper-large-sme/resolve/main/bed43f50f06fd0db81c1009d7d9cbc2c595c5f7f6a6278e137410fea92d15f28/whisper-large-sme.pt" | |
| whisper.tokenizer.LANGUAGES["fi"] = "sami" | |
| whisper.tokenizer.TO_LANGUAGE_CODE["sami"] = "fi" | |
| from whisper.transcribe import cli | |
| if __name__ == "__main__": | |
| cli() | |