readme: ---
  language:
  - zh-CN
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - zh
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-zh-CN
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: zh-CN
      metrics:
      - name: Test WER
        type: wer
        value: 80
      - name: Test CER
        type: cer
        value: 40.11
    - 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: 69.1
    - 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: 43.08
  ---
  
  <!-- 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-xls-r-300m-zh-CN
  
  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: 0.8828
  - Wer: 2.0604
  
  ## 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: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 60.2112       | 0.74  | 500   | 64.8189         | 1.0    |
  | 8.1128        | 1.48  | 1000  | 6.8997          | 1.0    |
  | 6.0492        | 2.22  | 1500  | 5.9677          | 1.9495 |
  | 5.9326        | 2.95  | 2000  | 5.8845          | 1.4092 |
  | 5.8763        | 3.69  | 2500  | 5.8460          | 1.6126 |
  | 5.7888        | 4.43  | 3000  | 5.7545          | 2.2034 |
  | 5.735         | 5.17  | 3500  | 5.6777          | 2.3350 |
  | 5.6861        | 5.91  | 4000  | 5.5179          | 2.2232 |
  | 5.381         | 6.65  | 4500  | 5.1420          | 2.1816 |
  | 4.625         | 7.39  | 5000  | 3.9020          | 2.0722 |
  | 4.214         | 8.12  | 5500  | 3.3394          | 2.1430 |
  | 3.8992        | 8.86  | 6000  | 2.9085          | 2.1534 |
  | 3.6481        | 9.6   | 6500  | 2.6208          | 2.3538 |
  | 3.4658        | 10.34 | 7000  | 2.3172          | 2.2271 |
  | 3.257         | 11.08 | 7500  | 2.0916          | 2.1351 |
  | 3.1294        | 11.82 | 8000  | 1.8954          | 2.2133 |
  | 3.0266        | 12.56 | 8500  | 1.7673          | 2.0896 |
  | 2.9451        | 13.29 | 9000  | 1.6659          | 2.1381 |
  | 2.8802        | 14.03 | 9500  | 1.5637          | 2.1969 |
  | 2.78          | 14.77 | 10000 | 1.4921          | 2.2335 |
  | 2.7049        | 15.51 | 10500 | 1.4132          | 2.2217 |
  | 2.6768        | 16.25 | 11000 | 1.3667          | 2.2232 |
  | 2.6358        | 16.99 | 11500 | 1.3111          | 2.1286 |
  | 2.5802        | 17.72 | 12000 | 1.2679          | 2.1430 |
  | 2.5012        | 18.46 | 12500 | 1.2365          | 2.1153 |
  | 2.458         | 19.2  | 13000 | 1.2118          | 2.1573 |
  | 2.4433        | 19.94 | 13500 | 1.1992          | 2.1336 |
  | 2.438         | 20.68 | 14000 | 1.1803          | 2.1509 |
  | 2.418         | 21.42 | 14500 | 1.1601          | 2.1232 |
  | 2.3322        | 22.16 | 15000 | 1.1418          | 2.1930 |
  | 2.3387        | 22.89 | 15500 | 1.1172          | 2.2464 |
  | 2.3349        | 23.63 | 16000 | 1.1144          | 2.1856 |
  | 2.291         | 24.37 | 16500 | 1.1018          | 2.1930 |
  | 2.2766        | 25.11 | 17000 | 1.0883          | 2.1762 |
  | 2.2534        | 25.85 | 17500 | 1.0744          | 2.1875 |
  | 2.2393        | 26.59 | 18000 | 1.0561          | 2.1846 |
  | 2.2085        | 27.33 | 18500 | 1.0466          | 2.1445 |
  | 2.1966        | 28.06 | 19000 | 1.0382          | 2.1089 |
  | 2.1794        | 28.8  | 19500 | 1.0264          | 1.9861 |
  | 2.1423        | 29.54 | 20000 | 1.0246          | 1.9678 |
  | 2.1649        | 30.28 | 20500 | 0.9982          | 2.0005 |
  | 2.143         | 31.02 | 21000 | 0.9985          | 2.0450 |
  | 2.1338        | 31.76 | 21500 | 0.9932          | 2.0025 |
  | 2.1076        | 32.5  |