s3prl/superb
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How to use jialicheng/whisper-tiny-speech_commands with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="jialicheng/whisper-tiny-speech_commands") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("jialicheng/whisper-tiny-speech_commands")
model = AutoModelForAudioClassification.from_pretrained("jialicheng/whisper-tiny-speech_commands")This model is a fine-tuned version of openai/whisper-tiny 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 |
|---|---|---|---|---|
| 0.8486 | 1.0 | 1597 | 0.2380 | 0.9385 |
| 0.0986 | 2.0 | 3194 | 0.1777 | 0.9598 |
| 0.0773 | 3.0 | 4791 | 0.1249 | 0.9738 |
| 0.0532 | 4.0 | 6388 | 0.1078 | 0.9782 |
| 0.0472 | 5.0 | 7985 | 0.1258 | 0.9766 |
| 0.0322 | 6.0 | 9582 | 0.1365 | 0.9772 |
| 0.0258 | 7.0 | 11179 | 0.1338 | 0.9798 |
| 0.0231 | 8.0 | 12776 | 0.1447 | 0.9796 |
| 0.0171 | 9.0 | 14373 | 0.1435 | 0.9797 |
| 0.0142 | 10.0 | 15970 | 0.1434 | 0.9801 |
Base model
openai/whisper-tiny