s3prl/superb
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How to use 0xb1/wav2vec2-base-finetuned-ks with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="0xb1/wav2vec2-base-finetuned-ks") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("0xb1/wav2vec2-base-finetuned-ks")
model = AutoModelForAudioClassification.from_pretrained("0xb1/wav2vec2-base-finetuned-ks", device_map="auto")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 |
|---|---|---|---|---|
| 0.6834 | 1.0 | 399 | 0.6010 | 0.8954 |
| 0.307 | 2.0 | 798 | 0.1814 | 0.9750 |
| 0.1782 | 3.0 | 1197 | 0.1134 | 0.9829 |
| 0.1732 | 4.0 | 1597 | 0.0974 | 0.9819 |
| 0.122 | 5.0 | 1995 | 0.0895 | 0.9832 |