readme: ---
  license: apache-2.0
  tags:
  - speech-recognition
  - librispeech_asr
  - generated_from_trainer
  model-index:
  - name: wav2vec2-librispeech-clean-100h-demo-dist
    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-librispeech-clean-100h-demo-dist
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the LIBRISPEECH_ASR - CLEAN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0572
  - Wer: 0.0417
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 32
  - 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: 500
  - num_epochs: 3.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.399         | 0.11  | 100  | 3.6153          | 1.0    |
  | 2.8892        | 0.22  | 200  | 2.8963          | 1.0    |
  | 2.8284        | 0.34  | 300  | 2.8574          | 1.0    |
  | 0.7347        | 0.45  | 400  | 0.6158          | 0.4850 |
  | 0.1138        | 0.56  | 500  | 0.2038          | 0.1560 |
  | 0.248         | 0.67  | 600  | 0.1274          | 0.1024 |
  | 0.2586        | 0.78  | 700  | 0.1108          | 0.0876 |
  | 0.0733        | 0.9   | 800  | 0.0936          | 0.0762 |
  | 0.044         | 1.01  | 900  | 0.0834          | 0.0662 |
  | 0.0393        | 1.12  | 1000 | 0.0792          | 0.0622 |
  | 0.0941        | 1.23  | 1100 | 0.0769          | 0.0627 |
  | 0.036         | 1.35  | 1200 | 0.0731          | 0.0603 |
  | 0.0768        | 1.46  | 1300 | 0.0713          | 0.0559 |
  | 0.0518        | 1.57  | 1400 | 0.0686          | 0.0537 |
  | 0.0815        | 1.68  | 1500 | 0.0639          | 0.0515 |
  | 0.0603        | 1.79  | 1600 | 0.0636          | 0.0500 |
  | 0.056         | 1.91  | 1700 | 0.0609          | 0.0480 |
  | 0.0265        | 2.02  | 1800 | 0.0621          | 0.0465 |
  | 0.0496        | 2.13  | 1900 | 0.0607          | 0.0449 |
  | 0.0436        | 2.24  | 2000 | 0.0591          | 0.0446 |
  | 0.0421        | 2.35  | 2100 | 0.0590          | 0.0428 |
  | 0.0641        | 2.47  | 2200 | 0.0603          | 0.0443 |
  | 0.0466        | 2.58  | 2300 | 0.0580          | 0.0429 |
  | 0.0132        | 2.69  | 2400 | 0.0574          | 0.0423 |
  | 0.0073        | 2.8   | 2500 | 0.0586          | 0.0417 |
  | 0.0021        | 2.91  | 2600 | 0.0574          | 0.0412 |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  