readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xlsr-53-rm-vallader-with-lm
    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-xlsr-53-rm-vallader-with-lm
  
  This model is a fine-tuned version of [anuragshas/wav2vec2-large-xlsr-53-rm-vallader](https://huggingface.co/anuragshas/wav2vec2-large-xlsr-53-rm-vallader) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4552
  - Wer: 0.3206
  
  ## 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: 7.5e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.112
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.2379        | 3.12  | 100  | 0.4041          | 0.3396 |
  | 0.103         | 6.25  | 200  | 0.4400          | 0.3337 |
  | 0.0664        | 9.38  | 300  | 0.4239          | 0.3315 |
  | 0.0578        | 12.5  | 400  | 0.4303          | 0.3267 |
  | 0.0446        | 15.62 | 500  | 0.4575          | 0.3274 |
  | 0.041         | 18.75 | 600  | 0.4451          | 0.3223 |
  | 0.0402        | 21.88 | 700  | 0.4507          | 0.3206 |
  | 0.0374        | 25.0  | 800  | 0.4649          | 0.3208 |
  | 0.0371        | 28.12 | 900  | 0.4552          | 0.3206 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.1
  - Tokenizers 0.10.3
  