readme: ---
  language:
  - de
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - de
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - German
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: de
      metrics:
      - name: Test WER
        type: wer
        value: 20.16
      - name: Test CER
        type: cer
        value: 5.06
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: de
      metrics:
      - name: Test WER
        type: wer
        value: 39.79
      - name: Test CER
        type: cer
        value: 15.02
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: de
      metrics:
      - name: Test WER
        type: wer
        value: 47.95
  ---
  
  <!-- 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. 
  eval results:
  WER: 0.20161578657865786
  CER: 0.05062357805269733
  -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1768
  - Wer: 0.2016
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 3.4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.7531        | 0.04  | 500   | 5.4564          | 1.0    |
  | 2.9882        | 0.08  | 1000  | 3.0041          | 1.0    |
  | 2.1953        | 0.13  | 1500  | 1.1723          | 0.7121 |
  | 1.2406        | 0.17  | 2000  | 0.3656          | 0.3623 |
  | 1.1294        | 0.21  | 2500  | 0.2843          | 0.2926 |
  | 1.0731        | 0.25  | 3000  | 0.2554          | 0.2664 |
  | 1.051         | 0.3   | 3500  | 0.2387          | 0.2535 |
  | 1.0479        | 0.34  | 4000  | 0.2345          | 0.2512 |
  | 1.0026        | 0.38  | 4500  | 0.2270          | 0.2452 |
  | 0.9921        | 0.42  | 5000  | 0.2212          | 0.2353 |
  | 0.9839        | 0.47  | 5500  | 0.2141          | 0.2330 |
  | 0.9907        | 0.51  | 6000  | 0.2122          | 0.2334 |
  | 0.9788        | 0.55  | 6500  | 0.2114          | 0.2270 |
  | 0.9687        | 0.59  | 7000  | 0.2066          | 0.2323 |
  | 0.9777        | 0.64  | 7500  | 0.2033          | 0.2237 |
  | 0.9476        | 0.68  | 8000  | 0.2020          | 0.2194 |
  | 0.9625        | 0.72  | 8500  | 0.1977          | 0.2191 |
  | 0.9497        | 0.76  | 9000  | 0.1976          | 0.2175 |
  | 0.9781        | 0.81  | 9500  | 0.1956          | 0.2159 |
  | 0.9552        | 0.85  | 10000 | 0.1958          | 0.2191 |
  | 0.9345        | 0.89  | 10500 | 0.1964          | 0.2158 |
  | 0.9528        | 0.93  | 11000 | 0.1926          | 0.2154 |
  | 0.9502        | 0.98  | 11500 | 0.1953          | 0.2149 |
  | 0.9358        | 1.02  | 12000 | 0.1927          | 0.2167 |
  | 0.941         | 1.06  | 12500 | 0.1901          | 0.2115 |
  | 0.9287        | 1.1   | 13000 | 0.1936          | 0.2090 |
  | 0.9491        | 1.15  | 13500 | 0.1900          | 0.2104 |
  | 0.9478        | 1.19  | 14000 | 0.1931          | 0.2120 |
  | 0.946         | 1.23  | 14500 | 0.1914          | 0.2134 |
  | 0.9499        | 1.27  | 15000 | 0.1931          | 0.2173 |
  | 0.9346        | 1.32  | 15500 | 0.1913          | 0.2105 |
  | 0.9509        | 1.36  | 16000 | 0.1902          | 0.2137 |
  | 0.9294        | 1.4   | 16500 | 0.1895          | 0.2086 |
  | 0.9418        | 1.44  | 17000 | 0.1913          | 0.2183 |
  | 0.9302        | 1.49  | 17500 | 0.1884          | 0.2114 |
  | 0.9418        | 1.53  | 18000 | 0.1894          | 0.2108 |
  | 0.9363        | 1.57  | 18500 | 0.1886          | 0.2132 |
  | 0.9338        | 1.61  | 19000 | 0.1856          | 0.2078 |
  | 0.9185        | 1.66  | 19500 | 0.1852          | 0.2056 |
  | 0.9216        | 1.7   | 20000 | 0.1874          | 0.2095 |
  | 0.9176        | 1.74  | 20500 | 0.1873          | 0.2078 |
  |