mazkooleg/0-9up_google_speech_commands_augmented_raw
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How to use mazkooleg/0-9up-wavlm-base-plus-ft with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="mazkooleg/0-9up-wavlm-base-plus-ft") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("mazkooleg/0-9up-wavlm-base-plus-ft")
model = AutoModelForAudioClassification.from_pretrained("mazkooleg/0-9up-wavlm-base-plus-ft", device_map="auto")This model is a fine-tuned version of microsoft/wavlm-base-plus on the None 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.087 | 1.0 | 8558 | 0.9947 | 0.0216 |
| 0.0439 | 2.0 | 17117 | 0.9964 | 0.0117 |
| 0.0626 | 3.0 | 25675 | 0.9973 | 0.0093 |
| 0.0396 | 4.0 | 34232 | 0.9964 | 0.0097 |
| 0.0417 | 5.0 | 42790 | 0.9964 | 0.0123 |