s3prl/superb
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How to use Jungwoo4021/wav2vec2-base-ks-padpt1600 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="Jungwoo4021/wav2vec2-base-ks-padpt1600") # Load model directly
from transformers import AutoProcessor, AutoModelForSequenceClassification
processor = AutoProcessor.from_pretrained("Jungwoo4021/wav2vec2-base-ks-padpt1600")
model = AutoModelForSequenceClassification.from_pretrained("Jungwoo4021/wav2vec2-base-ks-padpt1600", 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 |
|---|---|---|---|---|
| 1.3499 | 1.0 | 50 | 1.6019 | 0.6111 |
| 0.9698 | 2.0 | 100 | 1.4349 | 0.5613 |
| 0.866 | 3.0 | 150 | 1.4232 | 0.5547 |
| 0.8162 | 4.0 | 200 | 1.5573 | 0.4675 |
| 0.7632 | 5.0 | 250 | 1.4991 | 0.4950 |
| 0.7461 | 6.0 | 300 | 1.4251 | 0.5321 |
| 0.7374 | 7.0 | 350 | 1.6291 | 0.4247 |
| 0.7237 | 8.0 | 400 | 1.5307 | 0.4797 |
| 0.7273 | 9.0 | 450 | 1.5635 | 0.4520 |
| 0.7007 | 10.0 | 500 | 1.5841 | 0.4497 |