readme: ---
  tags:
  - automatic-speech-recognition
  - librispeech_asr
  - generated_from_trainer
  - asr_seq2esq
  model-index:
  - name: wav2vec2-2-bart-base
    results: []
  widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
  - example_title: Common Voice sample
    src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
  ---
  
  To rerun this experiment, please clone this directory and run:
  
  ```bash
  python create_model.py
  ```
  followed by
  
  ```bash
  ./run_librispeech.sh
  ```
  
  <!-- 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. -->
  
  # wav2vec2-2-bart-base
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [bart-base](https://huggingface.co/facebook/bart-base) on the librispeech_asr - clean dataset.
    
  It achieves the following results on the evaluation set:
  - Loss: 0.405
  - Wer: 0.0728
  
  ## 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: 0.0003
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 64
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 400
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  See Training Metrics Tab.
  
  
  ### Framework versions
  
  - Transformers 4.15.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.16.2.dev0
  - Tokenizers 0.10.3
  