s3prl/superb
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How to use fkov/wav2vec2-base-ft-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="fkov/wav2vec2-base-ft-keyword-spotting") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("fkov/wav2vec2-base-ft-keyword-spotting")
model = AutoModelForAudioClassification.from_pretrained("fkov/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:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6324 | 1.0 | 399 | 0.4707 | 0.9626 |
| 0.2367 | 2.0 | 798 | 0.1377 | 0.9741 |
| 0.1685 | 3.0 | 1197 | 0.1009 | 0.9788 |
| 0.1091 | 4.0 | 1597 | 0.0922 | 0.9791 |
| 0.1297 | 5.0 | 1995 | 0.0835 | 0.9804 |