s3prl/superb
Viewer • Updated • 304k • 1.51k • 33
How to use antonjaragon/wav2vec2-base-ft-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="antonjaragon/wav2vec2-base-ft-keyword-spotting") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("antonjaragon/wav2vec2-base-ft-keyword-spotting")
model = AutoModelForAudioClassification.from_pretrained("antonjaragon/wav2vec2-base-ft-keyword-spotting")This model is a fine-tuned version of facebook/wav2vec2-base 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 |
|---|---|---|---|---|
| 0.4547 | 1.0 | 399 | 0.3372 | 0.9710 |
| 0.2625 | 2.0 | 798 | 0.1182 | 0.9779 |
| 0.1575 | 3.0 | 1197 | 0.0963 | 0.9787 |
| 0.1314 | 4.0 | 1597 | 0.0827 | 0.9832 |
| 0.1307 | 5.0 | 1995 | 0.0789 | 0.9831 |
Base model
facebook/wav2vec2-base