speechbrain/common_language
Updated • 482 • 44
How to use AescF/hubert-base-ls960-finetuned-common_language with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="AescF/hubert-base-ls960-finetuned-common_language") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("AescF/hubert-base-ls960-finetuned-common_language")
model = AutoModelForAudioClassification.from_pretrained("AescF/hubert-base-ls960-finetuned-common_language")This model is a fine-tuned version of facebook/hubert-base-ls960 on the Common Language dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.9713 | 1.0 | 2774 | 3.0764 | 0.1615 |
| 1.7443 | 2.0 | 5549 | 1.8279 | 0.4734 |
| 1.1304 | 3.0 | 8323 | 1.3202 | 0.6371 |
| 1.2718 | 4.0 | 11098 | 1.1571 | 0.6968 |
| 0.769 | 5.0 | 13872 | 1.2917 | 0.7127 |
| 0.2656 | 6.0 | 16647 | 1.1549 | 0.7479 |
| 0.2939 | 7.0 | 19421 | 1.2372 | 0.7736 |
| 0.1278 | 8.0 | 22196 | 1.2985 | 0.7875 |
| 0.5175 | 9.0 | 24970 | 1.3664 | 0.7986 |
| 0.0547 | 10.0 | 27740 | 1.4164 | 0.8011 |
Base model
facebook/hubert-base-ls960