readme: ---
  language:
  - sr
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - xlsr-fine-tuning-week
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  - name: Serbian comodoro Wav2Vec2 XLSR 300M CV8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: sr
      metrics:
      - name: Test WER
        type: wer
        value: 48.5
      - name: Test CER
        type: cer
        value: 18.4
  model-index:
  - name: wav2vec2-xls-r-300m-sr-cv8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8.0
        type: mozilla-foundation/common_voice_8_0
        args: sr
      metrics:
      - name: Test WER
        type: wer
        value: 48.53
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: sr
      metrics:
      - name: Test WER
        type: wer
        value: 97.43
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: sr
      metrics:
      - name: Test WER
        type: wer
        value: 96.69
  ---
  
  # Serbian wav2vec2-xls-r-300m-sr-cv8
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7302
  - Wer: 0.4825
  - Cer: 0.1847
  
  Evaluation on mozilla-foundation/common_voice_8_0 gave the following results:
  
  - WER: 0.48530097993467103
  - CER: 0.18413288165227845
  
  Evaluation on  speech-recognition-community-v2/dev_data gave the following results:
  
  - WER: 0.9718373107518604
  - CER: 0.8302740620263108
  
  The model can be evaluated using the attached `eval.py` script:
  ```
  python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sr-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config sr
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 16
  - 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: 300
  - num_epochs: 800
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
  | 5.6536        | 15.0  | 1200  | 2.9744          | 1.0    | 1.0    |
  | 2.7935        | 30.0  | 2400  | 1.6613          | 0.8998 | 0.4670 |
  | 1.6538        | 45.0  | 3600  | 0.9248          | 0.6918 | 0.2699 |
  | 1.2446        | 60.0  | 4800  | 0.9151          | 0.6452 | 0.2398 |
  | 1.0766        | 75.0  | 6000  | 0.9110          | 0.5995 | 0.2207 |
  | 0.9548        | 90.0  | 7200  | 1.0273          | 0.5921 | 0.2149 |
  | 0.8919        | 105.0 | 8400  | 0.9929          | 0.5646 | 0.2117 |
  | 0.8185        | 120.0 | 9600  | 1.0850          | 0.5483 | 0.2069 |
  | 0.7692        | 135.0 | 10800 | 1.1001          | 0.5394 | 0.2055 |
  | 0.7249        | 150.0 | 12000 | 1.1018          | 0.5380 | 0.1958 |
  | 0.6786        | 165.0 | 13200 | 1.1344          | 0.5114 | 0.1941 |
  | 0.6432        | 180.0 | 14400 | 1.1516          | 0.5054 | 0.1905 |
  | 0.6009        | 195.0 | 15600 | 1.3149          | 0.5324 | 0.1991 |
  | 0.5773        | 210.0 | 16800 | 1.2468          | 0.5124 | 0.1903 |
  | 0.559         | 225.0 | 18000 | 1.2186          | 0.4956 | 0.1922 |
  | 0.5298        | 240.0 | 19200 | 1.4483          | 0.5333 | 0.2085 |
  | 0.5136        | 255.0 | 20400 | 1.2871          | 0.4802 | 0.1846 |
  | 0.4824        | 270.0 | 21600 | 1.2891          | 0.4974 | 0.1885 |
  | 0.4669        | 285.0 | 22800 | 1.3283          | 0.4942 | 0.1878 |
  | 0.4511        | 300.0 | 24000 | 1.4502          | 0.5002 | 0.1994 |
  | 0.4337        | 315.0 | 25200 | 1.4714          | 0.5035 | 0.1911 |
  | 0.4221        | 330.0 | 26400 | 1.4971          | 0.5124 | 0.1962 |
  | 0.3994        | 345.0 | 27600 | 1.4473          | 0.5007 | 0.1920 |
  | 0.3892        | 360.0 | 28800 | 1.3904          | 0.4937 | 0.1887 |
  | 0.373         | 375.0 | 30000 | 1.4971          | 0.4946 | 0.1902 |
  | 0.3657        | 390.0 | 31200 | 1.4208          | 0.4900 | 0.1821 |
  | 0.3559        | 405.0 | 32400 | 1.4648          | 0.4895 | 0.1835 |
  | 0.3476        | 420.0 | 33600 | 1.4848          | 0.4946 | 0.1829 |
  | 0.3276        | 435.0 | 34800 | 1.5597          | 0.4979 | 0.1873 |
  | 0.3193        | 450.0 | 36000 | 1.7329          | 0.5040 | 0.1980 |
  | 0.3078        | 465.0 | 37200 | 1.6379          | 0.4937 | 0.1882 |
  | 0.3058        | 480.0