readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec-base-Millad_TIMIT
    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. -->
  
  # wav2vec-base-Millad_TIMIT
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3772
  - Wer: 0.6859
  - Cer: 0.3217
  
  ## 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: 0.0001
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 5000
  - num_epochs: 60
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
  | No log        | 2.36  | 2000  | 2.6233          | 1.0130 | 0.6241 |
  | No log        | 4.73  | 4000  | 2.2206          | 0.9535 | 0.5032 |
  | No log        | 7.09  | 6000  | 2.3036          | 0.9368 | 0.5063 |
  | 1.235         | 9.46  | 8000  | 1.9932          | 0.9275 | 0.5032 |
  | 1.235         | 11.82 | 10000 | 2.0207          | 0.8922 | 0.4498 |
  | 1.235         | 14.18 | 12000 | 1.6171          | 0.7993 | 0.3976 |
  | 1.235         | 16.55 | 14000 | 1.6729          | 0.8309 | 0.4209 |
  | 0.2779        | 18.91 | 16000 | 1.7043          | 0.8141 | 0.4340 |
  | 0.2779        | 21.28 | 18000 | 1.7426          | 0.7658 | 0.3960 |
  | 0.2779        | 23.64 | 20000 | 1.5230          | 0.7361 | 0.3830 |
  | 0.2779        | 26.0  | 22000 | 1.4286          | 0.7658 | 0.3794 |
  | 0.1929        | 28.37 | 24000 | 1.4450          | 0.7379 | 0.3644 |
  | 0.1929        | 30.73 | 26000 | 1.5922          | 0.7491 | 0.3826 |
  | 0.1929        | 33.1  | 28000 | 1.4443          | 0.7454 | 0.3617 |
  | 0.1929        | 35.46 | 30000 | 1.5450          | 0.7268 | 0.3621 |
  | 0.1394        | 37.83 | 32000 | 1.9268          | 0.7491 | 0.3763 |
  | 0.1394        | 40.19 | 34000 | 1.7094          | 0.7342 | 0.3783 |
  | 0.1394        | 42.55 | 36000 | 1.4024          | 0.7082 | 0.3494 |
  | 0.1394        | 44.92 | 38000 | 1.4467          | 0.6840 | 0.3395 |
  | 0.104         | 47.28 | 40000 | 1.4145          | 0.6933 | 0.3407 |
  | 0.104         | 49.65 | 42000 | 1.3901          | 0.6970 | 0.3403 |
  | 0.104         | 52.01 | 44000 | 1.3589          | 0.6636 | 0.3348 |
  | 0.104         | 54.37 | 46000 | 1.3716          | 0.6952 | 0.3340 |
  | 0.0781        | 56.74 | 48000 | 1.4025          | 0.6896 | 0.3312 |
  | 0.0781        | 59.1  | 50000 | 1.3772          | 0.6859 | 0.3217 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  