s3prl/superb
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How to use Jungwoo4021/wav2vec2-base-ks-padpt200 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="Jungwoo4021/wav2vec2-base-ks-padpt200") # Load model directly
from transformers import AutoProcessor, AutoModelForSequenceClassification
processor = AutoProcessor.from_pretrained("Jungwoo4021/wav2vec2-base-ks-padpt200")
model = AutoModelForSequenceClassification.from_pretrained("Jungwoo4021/wav2vec2-base-ks-padpt200", 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 |
|---|---|---|---|---|
| 1.2728 | 1.0 | 50 | 1.6540 | 0.6037 |
| 0.8498 | 2.0 | 100 | 1.2559 | 0.6015 |
| 0.7563 | 3.0 | 150 | 1.4192 | 0.5035 |
| 0.701 | 4.0 | 200 | 1.3318 | 0.5641 |
| 0.6592 | 5.0 | 250 | 1.3236 | 0.5666 |
| 0.6404 | 6.0 | 300 | 1.3653 | 0.5469 |
| 0.6315 | 7.0 | 350 | 1.4052 | 0.5082 |
| 0.6306 | 8.0 | 400 | 1.2818 | 0.5590 |
| 0.6297 | 9.0 | 450 | 1.3096 | 0.5659 |
| 0.6056 | 10.0 | 500 | 1.3595 | 0.5368 |