mazkooleg/0-9up_google_speech_commands_augmented_raw
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How to use mazkooleg/0-9up-data2vec-audio-base-960h-ft with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="mazkooleg/0-9up-data2vec-audio-base-960h-ft") # Load model directly
from transformers import AutoTokenizer, AutoModelForAudioClassification
tokenizer = AutoTokenizer.from_pretrained("mazkooleg/0-9up-data2vec-audio-base-960h-ft")
model = AutoModelForAudioClassification.from_pretrained("mazkooleg/0-9up-data2vec-audio-base-960h-ft")This model is a fine-tuned version of facebook/data2vec-audio-base-960h 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 |
|---|---|---|---|---|
| 0.1427 | 1.0 | 8558 | 0.9941 | 0.0271 |
| 0.0799 | 2.0 | 17116 | 0.9964 | 0.0154 |
| 0.0889 | 3.0 | 25674 | 0.9967 | 0.0146 |
| 0.0843 | 4.0 | 34232 | 0.9967 | 0.0162 |
| 0.0925 | 5.0 | 42790 | 0.9961 | 0.0151 |