readme: ---
  language:
  - pt
  license: apache-2.0
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - pt
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: sew-tiny-portuguese-cv7
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 28.9
      - name: Test CER
        type: cer
        value: 9.41
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: sv
      metrics:
      - name: Test WER
        type: wer
        value: 47.27
      - name: Test CER
        type: cer
        value: 19.62
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 47.3
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 49.83
  ---
  
  <!-- 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. -->
  
  # sew-tiny-portuguese-cv7
  
  This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4232
  - Wer: 0.2745
  
  ## 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: 0.0001
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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: 1000
  - training_steps: 40000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | No log        | 2.6    | 1000  | 1.0034          | 0.7308 |
  | 4.1307        | 5.19   | 2000  | 0.6274          | 0.4721 |
  | 4.1307        | 7.79   | 3000  | 0.5541          | 0.4130 |
  | 1.3117        | 10.39  | 4000  | 0.5302          | 0.3880 |
  | 1.3117        | 12.99  | 5000  | 0.5082          | 0.3644 |
  | 1.2047        | 15.58  | 6000  | 0.4818          | 0.3539 |
  | 1.2047        | 18.18  | 7000  | 0.4822          | 0.3477 |
  | 1.14          | 20.78  | 8000  | 0.4781          | 0.3428 |
  | 1.14          | 23.38  | 9000  | 0.4840          | 0.3401 |
  | 1.0818        | 25.97  | 10000 | 0.4613          | 0.3251 |
  | 1.0818        | 28.57  | 11000 | 0.4569          | 0.3257 |
  | 1.0451        | 31.17  | 12000 | 0.4494          | 0.3132 |
  | 1.0451        | 33.77  | 13000 | 0.4560          | 0.3201 |
  | 1.011         | 36.36  | 14000 | 0.4687          | 0.3174 |
  | 1.011         | 38.96  | 15000 | 0.4397          | 0.3122 |
  | 0.9785        | 41.56  | 16000 | 0.4605          | 0.3173 |
  | 0.9785        | 44.16  | 17000 | 0.4380          | 0.3064 |
  | 0.9458        | 46.75  | 18000 | 0.4372          | 0.3048 |
  | 0.9458        | 49.35  | 19000 | 0.4426          | 0.3039 |
  | 0.9126        | 51.95  | 20000 | 0.4317          | 0.2962 |
  | 0.9126        | 54.54  | 21000 | 0.4345          | 0.2960 |
  | 0.8926        | 57.14  | 22000 | 0.4365          | 0.2948 |
  | 0.8926        | 59.74  | 23000 | 0.4306          | 0.2940 |
  | 0.8654        | 62.34  | 24000 | 0.4303          | 0.2928 |
  | 0.8654        | 64.93  | 25000 | 0.4351          | 0.2915 |
  | 0.8373        | 67.53  | 26000 | 0.4340          | 0.2909 |
  | 0.8373        | 70.13  | 27000 | 0.4279          | 0.2907 |
  | 0.83          | 72.73  | 28000 | 0.4214          | 0.2867 |
  | 0.83          | 75.32  | 29000 | 0.4256          | 0.2849 |
  | 0.8062        | 77.92  | 30000 | 0.4281          | 0.2826 |
  | 0.8062        | 80.52  | 31000 | 0.4398          | 0.2865 |
  | 0.7846        | 83.12  | 32000 | 0.4218          | 0.2812 |
  | 0.7846        | 85.71  | 33000 | 0.4227          | 0.2791 |
  | 0.7697        | 88.31  | 34000 | 0.4200          | 0.2767 |
  | 0.7697        | 90.91  | 35000 | 0.4285          | 0.2791 |
  | 0.7539        | 93.51  | 36000 | 0.4238          | 0.2777 |
  | 0.7539        | 96.1   | 37000 | 0.4288          | 0.2757 |
  | 0.7413        | 98.7   | 38000 | 0.4205          | 0.2748 |
  | 0.7413    