readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-xlsr-mn-eng
    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-mn-eng
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the IEMOCAP and Common Voice's MN dataset. Can be used to recognize speech on ENG and MN simultaneously.
  It achieves the following results on the evaluation set:
  - Loss: 0.3087
  - Wer: 0.3402
  
  ## 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: 0.0001
  - train_batch_size: 2
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 8.8609        | 0.08  | 500   | 3.6078          | 1.0    |
  | 3.5494        | 0.15  | 1000  | 3.2044          | 1.0    |
  | 3.1699        | 0.23  | 1500  | 3.1560          | 1.0    |
  | 3.0955        | 0.3   | 2000  | 3.1087          | 1.0    |
  | 2.7918        | 0.38  | 2500  | 2.1146          | 1.0236 |
  | 2.0528        | 0.45  | 3000  | 1.4938          | 0.9648 |
  | 1.6329        | 0.53  | 3500  | 1.2614          | 0.9198 |
  | 1.3932        | 0.6   | 4000  | 1.0504          | 0.8314 |
  | 1.2652        | 0.68  | 4500  | 0.9664          | 0.7809 |
  | 1.1829        | 0.76  | 5000  | 0.8999          | 0.7381 |
  | 1.1674        | 0.83  | 5500  | 0.8200          | 0.6924 |
  | 1.0599        | 0.91  | 6000  | 0.7713          | 0.6729 |
  | 1.027         | 0.98  | 6500  | 0.7714          | 0.6616 |
  | 0.9289        | 1.06  | 7000  | 0.7571          | 0.6433 |
  | 0.9192        | 1.13  | 7500  | 0.6899          | 0.6151 |
  | 0.8996        | 1.21  | 8000  | 0.7012          | 0.6104 |
  | 0.9281        | 1.28  | 8500  | 0.6452          | 0.5914 |
  | 0.8656        | 1.36  | 9000  | 0.6162          | 0.5781 |
  | 0.8635        | 1.44  | 9500  | 0.6249          | 0.5672 |
  | 0.8388        | 1.51  | 10000 | 0.5936          | 0.5558 |
  | 0.8087        | 1.59  | 10500 | 0.5844          | 0.5466 |
  | 0.7755        | 1.66  | 11000 | 0.5838          | 0.5364 |
  | 0.8377        | 1.74  | 11500 | 0.5358          | 0.5202 |
  | 0.8308        | 1.81  | 12000 | 0.5333          | 0.5196 |
  | 0.7775        | 1.89  | 12500 | 0.5129          | 0.5060 |
  | 0.7747        | 1.96  | 13000 | 0.5164          | 0.5096 |
  | 0.7115        | 2.04  | 13500 | 0.5056          | 0.4936 |
  | 0.6974        | 2.12  | 14000 | 0.4925          | 0.4878 |
  | 0.6672        | 2.19  | 14500 | 0.5030          | 0.4908 |
  | 0.6396        | 2.27  | 15000 | 0.4821          | 0.4686 |
  | 0.6943        | 2.34  | 15500 | 0.4693          | 0.4624 |
  | 0.6413        | 2.42  | 16000 | 0.4626          | 0.4636 |
  | 0.6446        | 2.49  | 16500 | 0.4513          | 0.4609 |
  | 0.6338        | 2.57  | 17000 | 0.4386          | 0.4524 |
  | 0.6208        | 2.65  | 17500 | 0.4360          | 0.4445 |
  | 0.6397        | 2.72  | 18000 | 0.4348          | 0.4355 |
  | 0.6127        | 2.8   | 18500 | 0.4367          | 0.4318 |
  | 0.5956        | 2.87  | 19000 | 0.4376          | 0.4322 |
  | 0.6345        | 2.95  | 19500 | 0.4050          | 0.4308 |
  | 0.572         | 3.02  | 20000 | 0.4211          | 0.4219 |
  | 0.5447        | 3.1   | 20500 | 0.4042          | 0.4112 |
  | 0.5323        | 3.17  | 21000 | 0.4101          | 0.4153 |
  | 0.5677        | 3.25  | 21500 | 0.3952          | 0.4188 |
  | 0.5354        | 3.33  | 22000 | 0.3889          | 0.4007 |
  | 0.5297        | 3.4   | 22500 | 0.3793          | 0.3997 |
  | 0.5314        | 3.48  | 23000 | 0.3684          | 0.3956 |
  | 0.5217        | 3.55  | 23500 | 0.3572          | 0.3853 |
  | 0.5224        | 3.63  | 24000 | 0.3535          | 0.3867 |
  | 0.4983        | 3.7   | 24500 | 0.3636          | 0.3804 |
  | 0.5355        | 3.78  | 25000 | 0.3680          | 0.3770 |
  | 0.5115        | 3.85  | 25500 | 0.3472          | 0.3752 |
  | 0.5416        | 3.93  | 26000 | 0.3280          | 0.3689 |
  | 0.5104        | 4.01  | 26500 | 0.3319          | 0.3650 |
  | 0.4524        | 4.08  | 27000 | 0.3453          | 0.3632 |
  | 0.462         | 4.16  | 27500 | 0.3359          | 0.3600 |
  | 0.4823        | 4.23  | 28000 | 0.3268          | 0.3553 |
  | 0.4671        | 4.31  | 28500 | 0.3248          | 0.3535 |
  | 0.4702        | 4.38  | 29000 | 0.3278          | 0.3501 |
  | 0.483         | 4.46  | 29500 | 0.3183          | 0.3492 |
  | 0.4232        | 4.53  | 30000 | 0.3224          | 0.3470 |
  | 0.4227        | 