google/speech_commands
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How to use Ahmed107/whisper-tiny-ar-ft-eos with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="Ahmed107/whisper-tiny-ar-ft-eos") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Ahmed107/whisper-tiny-ar-ft-eos")
model = AutoModelForAudioClassification.from_pretrained("Ahmed107/whisper-tiny-ar-ft-eos")This model is a fine-tuned version of arbml/whisper-tiny-ar on the Speech Commands 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 |
|---|---|---|---|---|
| 0.6826 | 1.0 | 1325 | 0.7084 | 0.4966 |
| 0.7052 | 2.0 | 2650 | 0.6965 | 0.5 |
| 0.7409 | 3.0 | 3975 | 0.6876 | 0.5510 |
| 0.7077 | 4.0 | 5300 | 0.7214 | 0.5170 |
| 0.7988 | 5.0 | 6625 | 0.7523 | 0.4898 |
| 0.5818 | 6.0 | 7950 | 0.8118 | 0.5510 |
| 0.7722 | 7.0 | 9275 | 0.9102 | 0.5306 |
| 1.4165 | 8.0 | 10600 | 1.6832 | 0.5 |
| 0.7113 | 9.0 | 11925 | 1.6268 | 0.5340 |
| 0.2578 | 10.0 | 13250 | 1.8423 | 0.5204 |
Base model
arbml/whisper-tiny-ar