Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use Zion16/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zion16/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Zion16/my_awesome_mind_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Zion16/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("Zion16/my_awesome_mind_model") - Notebooks
- Google Colab
- Kaggle
my_awesome_mind_model
This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 2.6657
- Accuracy: 0.0088
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 2.6370 | 0.0973 |
| No log | 2.0 | 4 | 2.6400 | 0.0796 |
| No log | 3.0 | 6 | 2.6482 | 0.0619 |
| No log | 4.0 | 8 | 2.6541 | 0.0619 |
| 2.635 | 5.0 | 10 | 2.6597 | 0.0265 |
| 2.635 | 6.0 | 12 | 2.6623 | 0.0088 |
| 2.635 | 7.0 | 14 | 2.6633 | 0.0088 |
| 2.635 | 8.0 | 16 | 2.6648 | 0.0088 |
| 2.635 | 9.0 | 18 | 2.6653 | 0.0088 |
| 2.6268 | 10.0 | 20 | 2.6657 | 0.0088 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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Model tree for Zion16/my_awesome_mind_model
Base model
facebook/wav2vec2-baseEvaluation results
- Accuracy on minds14self-reported0.009