s3prl/superb
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How to use cloudwalkerw/wav2vec2-base-ft-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="cloudwalkerw/wav2vec2-base-ft-keyword-spotting") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("cloudwalkerw/wav2vec2-base-ft-keyword-spotting")
model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wav2vec2-base-ft-keyword-spotting")This model is a fine-tuned version of microsoft/wavlm-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 |
|---|---|---|---|---|
| 1.3203 | 1.0 | 199 | 1.2906 | 0.6328 |
| 0.9587 | 2.0 | 399 | 0.7793 | 0.7355 |
| 0.6218 | 3.0 | 599 | 0.3858 | 0.9289 |
| 0.4379 | 4.0 | 799 | 0.2581 | 0.9688 |
| 0.3779 | 4.98 | 995 | 0.2270 | 0.9694 |
Base model
microsoft/wavlm-base