danavery/urbansound8K
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How to use rishabhsabnavis/noise-pollution-model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="rishabhsabnavis/noise-pollution-model") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("rishabhsabnavis/noise-pollution-model")
model = AutoModelForAudioClassification.from_pretrained("rishabhsabnavis/noise-pollution-model")This model is a fine-tuned version of facebook/wav2vec2-base on the URBAN-SOUND8K 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 | F1 |
|---|---|---|---|---|---|
| 0.6296 | 1.0 | 1747 | 0.9168 | 0.7098 | 0.6543 |
| 0.3658 | 2.0 | 3494 | 0.4589 | 0.8798 | 0.8788 |
| 0.108 | 3.0 | 5241 | 0.4362 | 0.9107 | 0.9102 |
| 0.3019 | 4.0 | 6988 | 0.4455 | 0.9216 | 0.9215 |
| 0.0019 | 5.0 | 8735 | 0.3645 | 0.9433 | 0.9433 |
| 0.0014 | 6.0 | 10482 | 0.3780 | 0.9416 | 0.9417 |
| 0.1803 | 7.0 | 12229 | 0.3196 | 0.9519 | 0.9519 |
| 0.0004 | 8.0 | 13976 | 0.2672 | 0.9651 | 0.9651 |
Base model
facebook/wav2vec2-base