readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: hubert-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. -->
  
  # hubert-large-xlsr-common1000asli-demo-colab-dd
  
  This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0754
  - Wer: 0.5189
  
  ## 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    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 8.5628        | 10.53  | 400   | 1.4949          | 0.9944 |
  | 0.7496        | 21.05  | 800   | 0.6398          | 0.6917 |
  | 0.3298        | 31.58  | 1200  | 0.6116          | 0.6148 |
  | 0.228         | 42.11  | 1600  | 0.6544          | 0.5835 |
  | 0.17          | 52.63  | 2000  | 0.7028          | 0.5955 |
  | 0.1466        | 63.16  | 2400  | 0.6935          | 0.5992 |
  | 0.1261        | 73.68  | 2800  | 0.7101          | 0.5735 |
  | 0.1109        | 84.21  | 3200  | 0.7360          | 0.5610 |
  | 0.1001        | 94.74  | 3600  | 0.7924          | 0.5604 |
  | 0.0856        | 105.26 | 4000  | 0.7975          | 0.5653 |
  | 0.0821        | 115.79 | 4400  | 0.8027          | 0.5611 |
  | 0.0783        | 126.32 | 4800  | 0.8238          | 0.5566 |
  | 0.0691        | 136.84 | 5200  | 0.8109          | 0.5519 |
  | 0.0627        | 147.37 | 5600  | 0.8231          | 0.5544 |
  | 0.0589        | 157.89 | 6000  | 0.8747          | 0.5506 |
  | 0.0548        | 168.42 | 6400  | 0.8440          | 0.5478 |
  | 0.052         | 178.95 | 6800  | 0.8289          | 0.5393 |
  | 0.0471        | 189.47 | 7200  | 0.8689          | 0.5492 |
  | 0.0486        | 200.0  | 7600  | 0.8437          | 0.5372 |
  | 0.0433        | 210.53 | 8000  | 0.8360          | 0.5453 |
  | 0.0419        | 221.05 | 8400  | 0.8645          | 0.5391 |
  | 0.0393        | 231.58 | 8800  | 0.8821          | 0.5506 |
  | 0.0404        | 242.11 | 9200  | 0.9073          | 0.5419 |
  | 1.318         | 252.63 | 9600  | 0.8408          | 0.5813 |
  | 0.0489        | 263.16 | 10000 | 0.8206          | 0.5449 |
  | 0.0406        | 273.68 | 10400 | 0.8592          | 0.5466 |
  | 0.0359        | 284.21 | 10800 | 0.8597          | 0.5476 |
  | 0.0344        | 294.74 | 11200 | 0.8349          | 0.5369 |
  | 0.032         | 305.26 | 11600 | 0.8352          | 0.5379 |
  | 0.0299        | 315.79 | 12000 | 0.8409          | 0.5420 |
  | 0.0287        | 326.32 | 12400 | 0.8562          | 0.5441 |
  | 0.0292        | 336.84 | 12800 | 0.9100          | 0.5519 |
  | 0.0258        | 347.37 | 13200 | 0.9213          | 0.5447 |
  | 0.0229        | 357.89 | 13600 | 0.9020          | 0.5343 |
  | 0.0257        | 368.42 | 14000 | 0.9219          | 0.5531 |
  | 0.0236        | 378.95 | 14400 | 0.9301          | 0.5516 |
  | 0.0241        | 389.47 | 14800 | 0.9058          | 0.5359 |
  | 0.022         | 400.0  | 15200 | 0.9067          | 0.5408 |
  | 3.4199        | 410.53 | 15600 | 0.9661          | 0.6957 |
  | 0.0554        | 421.05 | 16000 | 0.8984          | 0.5661 |
  | 0.0289        | 431.58 | 16400 | 0.8843          | 0.5504 |
  | 0.0234        | 442.11 | 16800 | 0.8943          | 0.5407 |
  | 0.0219        | 452.63 | 17200 | 0.9325          | 0.5391 |
  | 0.0194        | 463.16 | 17600 | 0.9588          | 0.5442 |
  | 0.0195        | 473.68 | 18000 | 0.9660          | 0.5478 |
  | 0.0184        | 484.21 | 18400 | 0.9325          | 0.5394 |
  | 0.0178        | 494.74 | 18800 | 0.9526          | 0.5435 |
  | 0.0171        | 505.26 | 19200 | 0.9533          | 0.5412 |
  | 0.0174        | 515.79 | 19600 | 0.8962          | 0.5410 |
  | 0.0165        | 526.32 | 20000 | 0.9699          | 0.5422 |
  | 0.0153        | 536.84 | 20400 | 0.9252          | 0.5301 |
  | 0.0141        | 547.37 | 20800 | 0.9364          | 0.5401 |
  | 0.0148        | 557.89 | 21200 | 0.9479          | 0.5387 |
  | 0.0141        | 568.42 | 21600 | 0.9692          | 0.5365 |
  | 0.0136        | 578.95 | 22000 | 0.9779          | 0.5343 |
  | 0.0127        | 589.47 | 22400 | 0.9684          | 0.5303 |
  | 0.0122        | 600.0  | 22800 | 0.9930          | 0.5346 |
  | 0.0122        | 610.53 | 23200 | 0.9733          | 0.5348 |
  | 0.0112        | 621.0