s3prl/superb
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How to use Jungwoo4021/wav2vec2-base-ks-linear_lrX100 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="Jungwoo4021/wav2vec2-base-ks-linear_lrX100") # Load model directly
from transformers import AutoProcessor, AutoModelForSequenceClassification
processor = AutoProcessor.from_pretrained("Jungwoo4021/wav2vec2-base-ks-linear_lrX100")
model = AutoModelForSequenceClassification.from_pretrained("Jungwoo4021/wav2vec2-base-ks-linear_lrX100")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 |
|---|---|---|---|---|
| 1.1789 | 1.0 | 50 | 1.3621 | 0.6225 |
| 0.636 | 2.0 | 100 | 0.9176 | 0.6912 |
| 0.5575 | 3.0 | 150 | 0.8543 | 0.7376 |
| 0.5289 | 4.0 | 200 | 0.6970 | 0.8001 |
| 0.4926 | 5.0 | 250 | 0.8232 | 0.7548 |
| 0.4831 | 6.0 | 300 | 0.7442 | 0.7755 |
| 0.4539 | 7.0 | 350 | 0.7484 | 0.7785 |
| 0.4816 | 8.0 | 400 | 0.7038 | 0.7982 |
| 0.4666 | 9.0 | 450 | 0.7277 | 0.7764 |
| 0.4417 | 10.0 | 500 | 0.7289 | 0.7870 |