mazkooleg/0-9up_google_speech_commands_augmented_raw
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How to use mazkooleg/0-9up-wav2vec2-base-ft with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="mazkooleg/0-9up-wav2vec2-base-ft") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("mazkooleg/0-9up-wav2vec2-base-ft")
model = AutoModelForAudioClassification.from_pretrained("mazkooleg/0-9up-wav2vec2-base-ft")This model is a fine-tuned version of facebook/wav2vec2-base 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 |
|---|---|---|---|---|
| 0.0905 | 1.0 | 8558 | 0.9952 | 0.0197 |
| 0.0572 | 2.0 | 17117 | 0.9964 | 0.0156 |
| 0.0597 | 3.0 | 25674 | 0.9955 | 0.0181 |
| 0.0396 | 4.0 | 34232 | 0.9950 | 0.0207 |
| 0.0399 | 5.0 | 42790 | 0.9938 | 0.0249 |