readme: ---
  language:
  - hsb
  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:
  - common_voice
  model-index:
  - name: Upper Sorbian 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: hsb
      metrics:
      - name: Test WER
        type: wer
        value: 56.3
      - name: Test CER
        type: cer
        value: 14.3
  ---
  
  # Upper Sorbian wav2vec2-xls-r-300m-hsb-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: 0.9643
  - Wer: 0.5037
  - Cer: 0.1278
  
  ## Evaluation
  
  The model can be evaluated using the attached `eval.py` script:
  ```
  python eval.py --model_id comodoro/wav2vec2-xls-r-300m-hsb-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config hsb
  ```
  
  ### 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: 200
  - num_epochs: 500
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    | Cer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
  | 4.3121        | 19.35  | 1200  | 3.2059          | 1.0    | 1.0    |
  | 2.6525        | 38.71  | 2400  | 1.1324          | 0.9387 | 0.3204 |
  | 1.3644        | 58.06  | 3600  | 0.8767          | 0.8099 | 0.2271 |
  | 1.093         | 77.42  | 4800  | 0.8739          | 0.7603 | 0.2090 |
  | 0.9546        | 96.77  | 6000  | 0.8454          | 0.6983 | 0.1882 |
  | 0.8554        | 116.13 | 7200  | 0.8197          | 0.6484 | 0.1708 |
  | 0.775         | 135.48 | 8400  | 0.8452          | 0.6345 | 0.1681 |
  | 0.7167        | 154.84 | 9600  | 0.8551          | 0.6241 | 0.1631 |
  | 0.6609        | 174.19 | 10800 | 0.8442          | 0.5821 | 0.1531 |
  | 0.616         | 193.55 | 12000 | 0.8892          | 0.5864 | 0.1527 |
  | 0.5815        | 212.9  | 13200 | 0.8839          | 0.5772 | 0.1503 |
  | 0.55          | 232.26 | 14400 | 0.8905          | 0.5665 | 0.1436 |
  | 0.5173        | 251.61 | 15600 | 0.8995          | 0.5471 | 0.1417 |
  | 0.4969        | 270.97 | 16800 | 0.8633          | 0.5325 | 0.1334 |
  | 0.4803        | 290.32 | 18000 | 0.9074          | 0.5253 | 0.1352 |
  | 0.4596        | 309.68 | 19200 | 0.9159          | 0.5146 | 0.1294 |
  | 0.4415        | 329.03 | 20400 | 0.9055          | 0.5189 | 0.1314 |
  | 0.434         | 348.39 | 21600 | 0.9435          | 0.5208 | 0.1314 |
  | 0.4199        | 367.74 | 22800 | 0.9199          | 0.5136 | 0.1290 |
  | 0.4008        | 387.1  | 24000 | 0.9342          | 0.5174 | 0.1303 |
  | 0.4051        | 406.45 | 25200 | 0.9436          | 0.5132 | 0.1292 |
  | 0.3861        | 425.81 | 26400 | 0.9417          | 0.5084 | 0.1283 |
  | 0.3738        | 445.16 | 27600 | 0.9573          | 0.5079 | 0.1299 |
  | 0.3768        | 464.52 | 28800 | 0.9682          | 0.5062 | 0.1289 |
  | 0.3647        | 483.87 | 30000 | 0.9643          | 0.5037 | 0.1278 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  