readme: ---
  language:
  - zh-CN
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - sv
  datasets:
  - common_voice
  model-index:
  - name: ''
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: zh-CN
      metrics:
      - name: Test CER
        type: cer
        value: 66.22
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: zh-CN
      metrics:
      - name: Test CER
        type: cer
        value: 37.51
  ---
  
  <!-- 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. -->
  
  # 
  
  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 - ZH-CN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8122
  - Wer: 0.8392
  - Cer: 0.2059
  
  ## 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: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
  | 69.215        | 0.74  | 500   | 74.9751         | 1.0    | 1.0    |
  | 8.2109        | 1.48  | 1000  | 7.0617          | 1.0    | 1.0    |
  | 6.4277        | 2.22  | 1500  | 6.3811          | 1.0    | 1.0    |
  | 6.3513        | 2.95  | 2000  | 6.3061          | 1.0    | 1.0    |
  | 6.2522        | 3.69  | 2500  | 6.2147          | 1.0    | 1.0    |
  | 5.9757        | 4.43  | 3000  | 5.7906          | 1.1004 | 0.9924 |
  | 5.0642        | 5.17  | 3500  | 4.2984          | 1.7729 | 0.8214 |
  | 4.6346        | 5.91  | 4000  | 3.7129          | 1.8946 | 0.7728 |
  | 4.267         | 6.65  | 4500  | 3.2177          | 1.7526 | 0.6922 |
  | 3.9964        | 7.39  | 5000  | 2.8337          | 1.8055 | 0.6546 |
  | 3.8035        | 8.12  | 5500  | 2.5726          | 2.1851 | 0.6992 |
  | 3.6273        | 8.86  | 6000  | 2.3391          | 2.1029 | 0.6511 |
  | 3.5248        | 9.6   | 6500  | 2.1944          | 2.3617 | 0.6859 |
  | 3.3683        | 10.34 | 7000  | 1.9827          | 2.1014 | 0.6063 |
  | 3.2411        | 11.08 | 7500  | 1.8610          | 1.6160 | 0.5135 |
  | 3.1299        | 11.82 | 8000  | 1.7446          | 1.5948 | 0.4946 |
  | 3.0574        | 12.56 | 8500  | 1.6454          | 1.1291 | 0.4051 |
  | 2.985         | 13.29 | 9000  | 1.5919          | 1.0673 | 0.3893 |
  | 2.9573        | 14.03 | 9500  | 1.4903          | 1.0604 | 0.3766 |
  | 2.8897        | 14.77 | 10000 | 1.4614          | 1.0059 | 0.3653 |
  | 2.8169        | 15.51 | 10500 | 1.3997          | 1.0030 | 0.3550 |
  | 2.8155        | 16.25 | 11000 | 1.3444          | 0.9980 | 0.3441 |
  | 2.7595        | 16.99 | 11500 | 1.2911          | 0.9703 | 0.3325 |
  | 2.7107        | 17.72 | 12000 | 1.2462          | 0.9565 | 0.3227 |
  | 2.6358        | 18.46 | 12500 | 1.2466          | 0.9955 | 0.3333 |
  | 2.5801        | 19.2  | 13000 | 1.2059          | 1.0010 | 0.3226 |
  | 2.5554        | 19.94 | 13500 | 1.1919          | 1.0094 | 0.3223 |
  | 2.5314        | 20.68 | 14000 | 1.1703          | 0.9847 | 0.3156 |
  | 2.509         | 21.42 | 14500 | 1.1733          | 0.9896 | 0.3177 |
  | 2.4391        | 22.16 | 15000 | 1.1811          | 0.9723 | 0.3164 |
  | 2.4631        | 22.89 | 15500 | 1.1382          | 0.9698 | 0.3059 |
  | 2.4414        | 23.63 | 16000 | 1.0893          | 0.9644 | 0.2972 |
  | 2.3771        | 24.37 | 16500 | 1.0930          | 0.9505 | 0.2954 |
  | 2.3658        | 25.11 | 17000 | 1.0756          | 0.9609 | 0.2926 |
  | 2.3215        | 25.85 | 17500 | 1.0512          | 0.9614 | 0.2890 |
  | 2.3327        | 26.59 | 18000 | 1.0627          | 1.1984 | 0.3282 |
  | 2.3055        | 27.33 | 18500 | 1.0582          | 0.9520 | 0.2841 |
  | 2.299         | 28.06 | 19000 | 1.0356          | 0.9480 | 0.2817 |
  | 2.2673        | 28.8  | 19500 | 1.0305          | 0.9367 | 0.2771 |
  | 2.2166        | 29.54 | 20000 | 1.0139          | 0.9223 | 0.2702 |
  | 2.2378        | 30.28 | 20500 | 1.0095          | 0.9268 | 0.2722 |
  | 2.2168        | 31.02 | 21000 | 1.0001          | 0.9085 | 0.2691 |
  | 2.1766        | 31.76 | 21500 | 0.9884  