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: 351 Bytes
6dfd0f2 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"epoch": 1.0,
"eval_loss": 0.5558891892433167,
"eval_runtime": 163.4842,
"eval_samples_per_second": 0.612,
"eval_steps_per_second": 0.104,
"eval_wer": 24.914285714285715,
"train_loss": 3.482638488374606e-09,
"train_runtime": 45.4914,
"train_samples_per_second": 15827.168,
"train_steps_per_second": 1318.931
} |