s3prl/superb
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How to use anton-l/wav2vec2-base-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="anton-l/wav2vec2-base-keyword-spotting") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("anton-l/wav2vec2-base-keyword-spotting")
model = AutoModelForAudioClassification.from_pretrained("anton-l/wav2vec2-base-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 |
|---|---|---|---|---|
| 0.8279 | 1.0 | 399 | 0.6792 | 0.8558 |
| 0.2961 | 2.0 | 798 | 0.1383 | 0.9798 |
| 0.2069 | 3.0 | 1197 | 0.0972 | 0.9809 |
| 0.1757 | 4.0 | 1596 | 0.0843 | 0.9825 |
| 0.1607 | 5.0 | 1995 | 0.0746 | 0.9843 |