readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - /workspace/datasets/datasets/MIR_ST500/MIR_ST500.py
  - generated_from_trainer
  datasets:
  - mir_st500
  model-index:
  - name: wav2vec2-large-xlsr-53-MIR_ST500_ASR
    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-large-xlsr-53-MIR_ST500_ASR
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the /WORKSPACE/DATASETS/DATASETS/MIR_ST500/MIR_ST500.PY - ASR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5180
  - Wer: 0.5824
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 8
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 56.764        | 0.13  | 100   | 24.4254         | 0.9990 |
  | 7.5081        | 0.27  | 200   | 2.9111          | 1.0    |
  | 3.4899        | 0.4   | 300   | 2.1361          | 1.0    |
  | 2.4094        | 0.53  | 400   | 1.9088          | 1.0    |
  | 2.6764        | 0.67  | 500   | 1.8543          | 1.0    |
  | 3.3107        | 0.8   | 600   | 1.7979          | 1.0    |
  | 2.2856        | 0.93  | 700   | 1.7571          | 1.0    |
  | 1.856         | 1.07  | 800   | 1.7351          | 0.9648 |
  | 1.8882        | 1.2   | 900   | 1.7181          | 0.9654 |
  | 2.1731        | 1.33  | 1000  | 1.6736          | 0.9637 |
  | 1.8252        | 1.46  | 1100  | 1.3468          | 0.9647 |
  | 1.9092        | 1.6   | 1200  | 1.3302          | 0.9627 |
  | 1.9435        | 1.73  | 1300  | 1.2428          | 0.9634 |
  | 1.3027        | 1.86  | 1400  | 1.2133          | 0.9644 |
  | 1.3438        | 2.0   | 1500  | 1.2002          | 0.9635 |
  | 1.2161        | 2.13  | 1600  | 1.1901          | 0.9636 |
  | 1.203         | 2.26  | 1700  | 1.1620          | 0.9616 |
  | 1.1159        | 2.4   | 1800  | 1.1660          | 0.9598 |
  | 1.1466        | 2.53  | 1900  | 1.2089          | 0.9605 |
  | 1.0563        | 2.66  | 2000  | 1.1732          | 0.9603 |
  | 1.1019        | 2.8   | 2100  | 1.1468          | 0.9612 |
  | 1.029         | 2.93  | 2200  | 1.1188          | 0.9622 |
  | 1.0079        | 3.06  | 2300  | 1.0604          | 0.9617 |
  | 1.0483        | 3.2   | 2400  | 1.0499          | 0.9612 |
  | 0.9892        | 3.33  | 2500  | 1.0292          | 0.9606 |
  | 0.9556        | 3.46  | 2600  | 1.0228          | 0.9604 |
  | 0.9626        | 3.6   | 2700  | 1.0028          | 0.9617 |
  | 1.0537        | 3.73  | 2800  | 1.0051          | 0.9608 |
  | 1.0648        | 3.86  | 2900  | 0.9723          | 0.9604 |
  | 0.8657        | 3.99  | 3000  | 0.9620          | 0.9605 |
  | 0.8964        | 4.13  | 3100  | 1.0432          | 0.9612 |
  | 0.9639        | 4.26  | 3200  | 0.9322          | 0.9589 |
  | 0.8965        | 4.39  | 3300  | 0.9091          | 0.9559 |
  | 0.8257        | 4.53  | 3400  | 0.8999          | 0.9499 |
  | 0.8002        | 4.66  | 3500  | 0.8754          | 0.9554 |
  | 0.7335        | 4.79  | 3600  | 0.8608          | 0.9572 |
  | 0.936         | 4.93  | 3700  | 0.8564          | 0.9510 |
  | 0.8185        | 5.06  | 3800  | 0.8890          | 0.9517 |
  | 0.7422        | 5.19  | 3900  | 0.8262          | 0.9392 |
  | 0.7784        | 5.33  | 4000  | 0.8292          | 0.9259 |
  | 0.8123        | 5.46  | 4100  | 0.8180          | 0.9374 |
  | 0.7256        | 5.59  | 4200  | 0.8158          | 0.9077 |
  | 0.7638        | 5.73  | 4300  | 0.8423          | 0.9170 |
  | 0.6737        | 5.86  | 4400  | 0.7818          | 0.9182 |
  | 0.7644        | 5.99  | 4500  | 0.7692          | 0.8934 |
  | 0.7911        | 6.13  | 4600  | 0.7627          | 0.8978 |
  | 0.6922        | 6.26  | 4700  | 0.7627          | 0.8906 |
  | 0.7369        | 6.39  | 4800  | 0.7570          | 0.8838 |
  | 0.6642        | 6.52  | 4900  | 0.9476          | 0.8953 |
  | 0.7502        | 6.66  | 5000  | 0.7336          | 0.8955 |
  | 0.6243        | 6.79  | 5100  | 0.7583          | 0.8896 |
  | 0.6912        | 6.92  | 5200  | 0.7764          | 0.8761 |
  | 0.7744        | 7.06  | 5300  | 0.7615          | 0.8790 |
  | 0.6195        | 7.19  | 5400  | 0.7114          | 0.8712 |
  | 0.7418        | 7.32  | 5500  | 0.8314          | 0.8864 |
  | 0.7658        | 7.46  | 5600  | 0.8531          | 0.8718 |
  | 0.6821        | 7.59  | 5700  | 0.9068          | 0