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-base-MIR_ST500_ASR_109
    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-MIR_ST500_ASR_109
  
  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 - ASR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6452
  - Wer: 0.3732
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 16
  - 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: 30.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 12.5751       | 0.27  | 100   | 6.0291          | 1.0    |
  | 4.343         | 0.53  | 200   | 2.8709          | 1.0    |
  | 4.1911        | 0.8   | 300   | 2.5472          | 1.0    |
  | 2.4535        | 1.06  | 400   | 2.4323          | 1.0    |
  | 2.6157        | 1.33  | 500   | 2.2799          | 1.0    |
  | 2.4839        | 1.6   | 600   | 2.2722          | 1.0    |
  | 2.2787        | 1.86  | 700   | 2.2269          | 1.0    |
  | 2.1981        | 2.13  | 800   | 2.2221          | 1.0    |
  | 2.159         | 2.39  | 900   | 2.1657          | 1.0    |
  | 2.1421        | 2.66  | 1000  | 2.1769          | 1.0    |
  | 2.0841        | 2.93  | 1100  | 2.1688          | 1.0    |
  | 2.0599        | 3.19  | 1200  | 2.1141          | 1.0    |
  | 2.0257        | 3.46  | 1300  | 2.0445          | 1.0    |
  | 1.979         | 3.72  | 1400  | 2.0180          | 1.0    |
  | 1.9366        | 3.99  | 1500  | 1.9419          | 1.0    |
  | 1.8547        | 4.26  | 1600  | 1.8765          | 1.0    |
  | 1.3988        | 4.52  | 1700  | 1.4151          | 0.7999 |
  | 1.1881        | 4.79  | 1800  | 1.1158          | 0.7347 |
  | 0.9557        | 5.05  | 1900  | 1.0095          | 0.6485 |
  | 0.9087        | 5.32  | 2000  | 0.9644          | 0.6848 |
  | 0.8086        | 5.59  | 2100  | 0.8960          | 0.6119 |
  | 0.9106        | 5.85  | 2200  | 0.8892          | 0.5941 |
  | 0.8252        | 6.12  | 2300  | 0.8333          | 0.5756 |
  | 0.8299        | 6.38  | 2400  | 0.8559          | 0.5838 |
  | 0.8021        | 6.65  | 2500  | 0.8201          | 0.5883 |
  | 0.7979        | 6.91  | 2600  | 0.8349          | 0.575  |
  | 0.7223        | 7.18  | 2700  | 0.7883          | 0.5563 |
  | 0.6754        | 7.45  | 2800  | 0.7590          | 0.5393 |
  | 0.6454        | 7.71  | 2900  | 0.7411          | 0.5291 |
  | 0.6228        | 7.98  | 3000  | 0.7464          | 0.5300 |
  | 0.6475        | 8.24  | 3100  | 0.7478          | 0.5295 |
  | 0.6452        | 8.51  | 3200  | 0.7555          | 0.5360 |
  | 0.5636        | 8.78  | 3300  | 0.7369          | 0.5232 |
  | 0.564         | 9.04  | 3400  | 0.7331          | 0.5076 |
  | 0.6173        | 9.31  | 3500  | 0.7199          | 0.5034 |
  | 0.625         | 9.57  | 3600  | 0.7243          | 0.5193 |
  | 0.8122        | 9.84  | 3700  | 0.7436          | 0.5242 |
  | 0.5455        | 10.11 | 3800  | 0.7111          | 0.4920 |
  | 0.7928        | 10.37 | 3900  | 0.7137          | 0.4858 |
  | 0.5446        | 10.64 | 4000  | 0.6874          | 0.4828 |
  | 0.4772        | 10.9  | 4100  | 0.6760          | 0.4801 |
  | 0.6447        | 11.17 | 4200  | 0.6893          | 0.4886 |
  | 0.5818        | 11.44 | 4300  | 0.6789          | 0.4740 |
  | 0.4952        | 11.7  | 4400  | 0.7043          | 0.4811 |
  | 0.5722        | 11.97 | 4500  | 0.6794          | 0.4766 |
  | 0.58          | 12.23 | 4600  | 0.6629          | 0.4580 |
  | 0.5432        | 12.5  | 4700  | 0.6907          | 0.4906 |
  | 0.4786        | 12.77 | 4800  | 0.6925          | 0.4854 |
  | 0.5177        | 13.03 | 4900  | 0.6666          | 0.4532 |
  | 0.5448        | 13.3  | 5000  | 0.6744          | 0.4542 |
  | 0.5732        | 13.56 | 5100  | 0.6930          | 0.4986 |
  | 0.5065        | 13.83 | 5200  | 0.6647          | 0.4351 |
  | 0.4005        | 14.1  | 5300  | 0.6659          | 0.4508 |
  | 0.4256        | 14.36 | 5400  | 0.6682          | 0.4533 |
  | 0.4459        | 14.63 | 5500  | 0.6594          | 0.4326 |
  | 0.4645        | 14.89 | 5600  | 0.6615          | 0.4287 |
  | 0.4275        | 15.16 | 5700  | 0.6423          | 0.4299 |
  | 0.4026        | 1