google/speech_commands
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How to use Teapack1/whisper-tiny-finetuned-no-go-kws with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="Teapack1/whisper-tiny-finetuned-no-go-kws") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Teapack1/whisper-tiny-finetuned-no-go-kws")
model = AutoModelForAudioClassification.from_pretrained("Teapack1/whisper-tiny-finetuned-no-go-kws", device_map="auto")This model is a fine-tuned version of openai/whisper-tiny on the Speech Commands[no, go] 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 |
|---|---|---|---|---|
| 0.33 | 1.0 | 780 | 0.0272 | 0.9938 |
| 0.0002 | 2.0 | 1560 | 0.0420 | 0.9876 |
| 0.0001 | 3.0 | 2340 | 0.0487 | 0.9913 |
| 0.0011 | 4.0 | 3120 | 0.0789 | 0.9802 |
| 0.0001 | 5.0 | 3900 | 0.0915 | 0.9851 |
| 0.0014 | 6.0 | 4680 | 0.1017 | 0.9839 |
| 0.0 | 7.0 | 5460 | 0.0993 | 0.9888 |
| 0.0 | 8.0 | 6240 | 0.0694 | 0.9913 |
| 0.0 | 9.0 | 7020 | 0.0760 | 0.9926 |
| 0.0 | 10.0 | 7800 | 0.0842 | 0.9901 |
Base model
openai/whisper-tiny