s3prl/superb
Viewer • Updated • 304k • 1.96k • 33
How to use jialicheng/speech-commands_hubert-xlarge with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="jialicheng/speech-commands_hubert-xlarge") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("jialicheng/speech-commands_hubert-xlarge")
model = AutoModelForAudioClassification.from_pretrained("jialicheng/speech-commands_hubert-xlarge")This model is a fine-tuned version of facebook/hubert-xlarge-ll60k on the superb dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5639 | 1.0 | 1597 | 0.1012 | 0.9794 |
| 0.205 | 2.0 | 3194 | 0.0720 | 0.9841 |
| 0.1774 | 3.0 | 4791 | 0.0664 | 0.9856 |
| 0.1549 | 4.0 | 6388 | 0.0621 | 0.9856 |
| 0.1416 | 5.0 | 7985 | 0.0620 | 0.9859 |
| 0.1328 | 6.0 | 9582 | 0.0614 | 0.9865 |
| 0.1159 | 7.0 | 11179 | 0.0615 | 0.9865 |
| 0.1211 | 8.0 | 12776 | 0.0579 | 0.9872 |
| 0.108 | 9.0 | 14373 | 0.0566 | 0.9863 |
| 0.1088 | 10.0 | 15970 | 0.0572 | 0.9869 |
Base model
facebook/hubert-xlarge-ll60k