readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xlsr-common1000asli-demo-colab-dd
    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-large-xlsr-common1000asli-demo-colab-dd
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0671
  - Wer: 0.5268
  
  ## 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: 128
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 1000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 10.4469       | 10.53  | 400   | 2.9884          | 0.9788 |
  | 1.6358        | 21.05  | 800   | 0.5968          | 0.6578 |
  | 0.3404        | 31.58  | 1200  | 0.5348          | 0.5757 |
  | 0.2218        | 42.11  | 1600  | 0.5945          | 0.5659 |
  | 0.1676        | 52.63  | 2000  | 0.6318          | 0.5753 |
  | 0.1442        | 63.16  | 2400  | 0.6395          | 0.5559 |
  | 0.1237        | 73.68  | 2800  | 0.6981          | 0.5522 |
  | 0.1132        | 84.21  | 3200  | 0.7174          | 0.5428 |
  | 0.1056        | 94.74  | 3600  | 0.7120          | 0.5531 |
  | 0.0942        | 105.26 | 4000  | 0.7865          | 0.5529 |
  | 0.0967        | 115.79 | 4400  | 0.7796          | 0.5546 |
  | 0.0854        | 126.32 | 4800  | 0.7392          | 0.5507 |
  | 0.0816        | 136.84 | 5200  | 0.8173          | 0.5575 |
  | 0.0748        | 147.37 | 5600  | 0.8164          | 0.5550 |
  | 0.0691        | 157.89 | 6000  | 0.8061          | 0.5444 |
  | 0.0654        | 168.42 | 6400  | 0.8098          | 0.5524 |
  | 0.0627        | 178.95 | 6800  | 0.8527          | 0.5655 |
  | 0.0653        | 189.47 | 7200  | 0.8210          | 0.5412 |
  | 0.0639        | 200.0  | 7600  | 0.8619          | 0.5602 |
  | 0.0614        | 210.53 | 8000  | 0.8453          | 0.5606 |
  | 0.0647        | 221.05 | 8400  | 0.8248          | 0.5564 |
  | 0.0611        | 231.58 | 8800  | 0.8323          | 0.5637 |
  | 0.0606        | 242.11 | 9200  | 0.8754          | 0.5654 |
  | 0.0587        | 252.63 | 9600  | 0.8684          | 0.5528 |
  | 0.0524        | 263.16 | 10000 | 0.8798          | 0.5556 |
  | 0.0499        | 273.68 | 10400 | 0.8593          | 0.5553 |
  | 0.0466        | 284.21 | 10800 | 0.9079          | 0.5520 |
  | 0.0505        | 294.74 | 11200 | 0.8680          | 0.5607 |
  | 0.0517        | 305.26 | 11600 | 0.8709          | 0.5557 |
  | 0.0522        | 315.79 | 12000 | 0.8687          | 0.5570 |
  | 0.0453        | 326.32 | 12400 | 0.8585          | 0.5614 |
  | 0.047         | 336.84 | 12800 | 0.9249          | 0.5581 |
  | 0.0431        | 347.37 | 13200 | 0.8934          | 0.5543 |
  | 0.0454        | 357.89 | 13600 | 0.8837          | 0.5583 |
  | 0.0472        | 368.42 | 14000 | 0.9070          | 0.5565 |
  | 0.0431        | 378.95 | 14400 | 0.9202          | 0.5526 |
  | 0.0404        | 389.47 | 14800 | 0.9234          | 0.5543 |
  | 0.0386        | 400.0  | 15200 | 0.9056          | 0.5549 |
  | 0.0372        | 410.53 | 15600 | 0.9901          | 0.5493 |
  | 0.0376        | 421.05 | 16000 | 0.9109          | 0.5460 |
  | 0.0365        | 431.58 | 16400 | 0.9313          | 0.5487 |
  | 0.0347        | 442.11 | 16800 | 0.9027          | 0.5496 |
  | 0.0361        | 452.63 | 17200 | 0.9614          | 0.5457 |
  | 0.0323        | 463.16 | 17600 | 0.9782          | 0.5558 |
  | 0.0325        | 473.68 | 18000 | 0.9549          | 0.5481 |
  | 0.032         | 484.21 | 18400 | 0.9781          | 0.5431 |
  | 0.0289        | 494.74 | 18800 | 0.9840          | 0.5463 |
  | 0.0292        | 505.26 | 19200 | 0.9397          | 0.5357 |
  | 0.0276        | 515.79 | 19600 | 0.9228          | 0.5467 |
  | 0.0283        | 526.32 | 20000 | 0.9683          | 0.5394 |
  | 0.0281        | 536.84 | 20400 | 0.9783          | 0.5479 |
  | 0.026         | 547.37 | 20800 | 0.9663          | 0.5472 |
  | 0.0288        | 557.89 | 21200 | 0.9424          | 0.5426 |
  | 0.0275        | 568.42 | 21600 | 0.9788          | 0.5435 |
  | 0.0264        | 578.95 | 22000 | 0.9703          | 0.5473 |
  | 0.0259        | 589.47 | 22400 | 0.9994          | 0.5446 |
  | 0.0243        | 600.0  | 22800 | 0.9637          | 0.5590 |
  | 0.0251        | 610.53 | 23200 | 0.9577          | 0.5457 |
  | 0.0222   