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
File size: 530 Bytes
29c5a08 440f4ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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()
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