readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - multilingual_librispeech
  - generated_from_trainer
  datasets:
  - multilingual_librispeech
  model-index:
  - name: wav2vec2-xlsr-53-300m-mls-german-ft
    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-53-300m-mls-german-ft
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MULTILINGUAL_LIBRISPEECH - GERMAN 10h dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2219
  - Wer: 0.1288
  
  ## 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: 32
  - 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: 200.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 2.9888        | 7.25   | 500   | 2.9192          | 1.0    |
  | 2.9313        | 14.49  | 1000  | 2.8698          | 1.0    |
  | 1.068         | 21.74  | 1500  | 0.2647          | 0.2565 |
  | 0.8151        | 28.99  | 2000  | 0.2067          | 0.1719 |
  | 0.764         | 36.23  | 2500  | 0.1975          | 0.1568 |
  | 0.7332        | 43.48  | 3000  | 0.1812          | 0.1463 |
  | 0.5952        | 50.72  | 3500  | 0.1923          | 0.1428 |
  | 0.6655        | 57.97  | 4000  | 0.1900          | 0.1404 |
  | 0.574         | 65.22  | 4500  | 0.1822          | 0.1370 |
  | 0.6211        | 72.46  | 5000  | 0.1937          | 0.1355 |
  | 0.5883        | 79.71  | 5500  | 0.1872          | 0.1335 |
  | 0.5666        | 86.96  | 6000  | 0.1874          | 0.1324 |
  | 0.5526        | 94.2   | 6500  | 0.1998          | 0.1368 |
  | 0.5671        | 101.45 | 7000  | 0.2054          | 0.1365 |
  | 0.5514        | 108.7  | 7500  | 0.1987          | 0.1340 |
  | 0.5382        | 115.94 | 8000  | 0.2104          | 0.1344 |
  | 0.5819        | 123.19 | 8500  | 0.2125          | 0.1334 |
  | 0.5277        | 130.43 | 9000  | 0.2063          | 0.1330 |
  | 0.4626        | 137.68 | 9500  | 0.2105          | 0.1310 |
  | 0.5842        | 144.93 | 10000 | 0.2087          | 0.1307 |
  | 0.535         | 152.17 | 10500 | 0.2137          | 0.1309 |
  | 0.5081        | 159.42 | 11000 | 0.2215          | 0.1302 |
  | 0.6033        | 166.67 | 11500 | 0.2162          | 0.1302 |
  | 0.5549        | 173.91 | 12000 | 0.2198          | 0.1286 |
  | 0.5389        | 181.16 | 12500 | 0.2241          | 0.1293 |
  | 0.4912        | 188.41 | 13000 | 0.2190          | 0.1290 |
  | 0.4671        | 195.65 | 13500 | 0.2218          | 0.1290 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.10.0
  - Datasets 1.15.2.dev0
  - Tokenizers 0.10.3
  