readme: ---
  license: apache-2.0
  tags:
  - audio-classification
  - generated_from_trainer
  datasets:
  - mir_st500
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-base-mirst500-ac
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-base-mirst500-ac
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the /workspace/datasets/datasets/MIR_ST500/MIR_ST500.py dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7566
  - Accuracy: 0.7570
  
  ## 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: 16
  - eval_batch_size: 1
  - seed: 0
  - distributed_type: multi-GPU
  - num_devices: 2
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - total_eval_batch_size: 2
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.3718        | 1.0   | 1304  | 1.4422          | 0.4255   |
  | 1.1285        | 2.0   | 2608  | 1.1061          | 0.5869   |
  | 1.0275        | 3.0   | 3912  | 0.8825          | 0.6724   |
  | 0.9982        | 4.0   | 5216  | 0.9181          | 0.6713   |
  | 0.9482        | 5.0   | 6520  | 0.8717          | 0.6971   |
  | 0.8687        | 6.0   | 7824  | 0.8041          | 0.7164   |
  | 0.8841        | 7.0   | 9128  | 0.8869          | 0.7034   |
  | 0.8094        | 8.0   | 10432 | 0.8216          | 0.7172   |
  | 0.7733        | 9.0   | 11736 | 0.8018          | 0.7298   |
  | 0.7892        | 10.0  | 13040 | 0.7517          | 0.7426   |
  | 0.8736        | 11.0  | 14344 | 0.7482          | 0.7482   |
  | 0.7035        | 12.0  | 15648 | 0.7730          | 0.7488   |
  | 0.7361        | 13.0  | 16952 | 0.7677          | 0.7510   |
  | 0.7808        | 14.0  | 18256 | 0.7765          | 0.7512   |
  | 0.7359        | 15.0  | 19560 | 0.7566          | 0.7570   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.9.1+cu102
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  