AudioClassification
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9903
- Accuracy: 0.35
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.6783 | 0.98 | 11 | 1.0771 | 0.21 |
0.6614 | 1.96 | 22 | 0.9514 | 0.25 |
0.6628 | 2.93 | 33 | 0.9843 | 0.28 |
0.6629 | 4.0 | 45 | 1.0408 | 0.27 |
0.6583 | 4.98 | 56 | 1.0061 | 0.29 |
0.6623 | 5.96 | 67 | 1.0227 | 0.31 |
0.6613 | 6.93 | 78 | 1.0398 | 0.30 |
0.6635 | 8.0 | 90 | 1.0085 | 0.29 |
0.6577 | 8.98 | 101 | 0.9842 | 0.34 |
0.6629 | 9.78 | 110 | 0.9903 | 0.35 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for Jurabek/AudioClassification
Base model
SpeechFlow/spoken_language_identification