readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - kensho/spgispeech
  widget:
  - example_title: Finance Speech
    src: https://drive.google.com/uc?id=151bzDnN_f0Dfjjrg36nI97tXM39t5Ka8
  model-index:
  - name: wav2vec2-base-finetuned-spgispeech-dev
    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-finetuned-spgispeech-dev
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [kensho/spgispeech](https://huggingface.co/datasets/kensho/spgispeech) dev dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2897
  - Wer: 0.1508
  
  ## 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: 16
  - 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: 1000
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.8285        | 2.22  | 1500  | 0.3361          | 0.2754 |
  | 0.2582        | 4.44  | 3000  | 0.2643          | 0.2205 |
  | 0.1697        | 6.66  | 4500  | 0.2467          | 0.2006 |
  | 0.1314        | 8.88  | 6000  | 0.2711          | 0.1927 |
  | 0.1084        | 11.09 | 7500  | 0.2521          | 0.1872 |
  | 0.0922        | 13.31 | 9000  | 0.2588          | 0.1827 |
  | 0.0818        | 15.53 | 10500 | 0.2572          | 0.1783 |
  | 0.0712        | 17.75 | 12000 | 0.2720          | 0.1766 |
  | 0.067         | 19.97 | 13500 | 0.2873          | 0.1751 |
  | 0.0594        | 22.19 | 15000 | 0.2753          | 0.1704 |
  | 0.0546        | 24.41 | 16500 | 0.2794          | 0.1694 |
  | 0.0505        | 26.63 | 18000 | 0.2811          | 0.1665 |
  | 0.0467        | 28.85 | 19500 | 0.2906          | 0.1657 |
  | 0.0417        | 31.07 | 21000 | 0.3043          | 0.1661 |
  | 0.0395        | 33.28 | 22500 | 0.3068          | 0.1627 |
  | 0.0368        | 35.5  | 24000 | 0.3096          | 0.1617 |
  | 0.0334        | 37.72 | 25500 | 0.3036          | 0.1581 |
  | 0.0322        | 39.94 | 27000 | 0.2819          | 0.1564 |
  | 0.0286        | 42.16 | 28500 | 0.2936          | 0.1544 |
  | 0.0279        | 44.38 | 30000 | 0.2914          | 0.1534 |
  | 0.0264        | 46.6  | 31500 | 0.2957          | 0.1519 |
  | 0.0241        | 48.82 | 33000 | 0.2897          | 0.1508 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  