s3prl/superb
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How to use jialicheng/speech-commands_wav2vec2-large with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="jialicheng/speech-commands_wav2vec2-large") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("jialicheng/speech-commands_wav2vec2-large")
model = AutoModelForAudioClassification.from_pretrained("jialicheng/speech-commands_wav2vec2-large")This model is a fine-tuned version of facebook/wav2vec2-large 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 |
|---|---|---|---|---|
| 1.0646 | 1.0 | 1597 | 0.1839 | 0.9625 |
| 0.3751 | 2.0 | 3194 | 0.1954 | 0.9647 |
| 0.3156 | 3.0 | 4791 | 0.1335 | 0.9744 |
| 0.257 | 4.0 | 6388 | 0.1062 | 0.9796 |
| 0.2386 | 5.0 | 7985 | 0.1029 | 0.9801 |
| 0.2085 | 6.0 | 9582 | 0.1002 | 0.9815 |
| 0.1715 | 7.0 | 11179 | 0.1031 | 0.9818 |
| 0.1575 | 8.0 | 12776 | 0.0938 | 0.9819 |
| 0.1332 | 9.0 | 14373 | 0.0896 | 0.9831 |
| 0.1288 | 10.0 | 15970 | 0.0856 | 0.9840 |
Base model
facebook/wav2vec2-large