readme: ---
  language:
  - pt
  license: apache-2.0
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - pt
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: sew-tiny-portuguese-cv8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 33.71
      - name: Test CER
        type: cer
        value: 10.69
    - 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: 52.79
      - name: Test CER
        type: cer
        value: 20.98
    - 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: 53.18
    - 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: 55.23
  ---
  
  <!-- 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-cv8
  
  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: 0.4082
  - Wer: 0.3053
  
  ## 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: 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: 1000
  - training_steps: 40000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | No log        | 1.93  | 1000  | 2.9134          | 0.9767 |
  | 2.9224        | 3.86  | 2000  | 2.8405          | 0.9789 |
  | 2.9224        | 5.79  | 3000  | 2.8094          | 0.9800 |
  | 2.8531        | 7.72  | 4000  | 2.7439          | 0.9891 |
  | 2.8531        | 9.65  | 5000  | 2.7057          | 1.0159 |
  | 2.7721        | 11.58 | 6000  | 2.7235          | 1.0709 |
  | 2.7721        | 13.51 | 7000  | 2.5931          | 1.1035 |
  | 2.6566        | 15.44 | 8000  | 2.2171          | 0.9884 |
  | 2.6566        | 17.37 | 9000  | 1.2399          | 0.8081 |
  | 1.9558        | 19.31 | 10000 | 0.9045          | 0.6353 |
  | 1.9558        | 21.24 | 11000 | 0.7705          | 0.5533 |
  | 1.4987        | 23.17 | 12000 | 0.7068          | 0.5165 |
  | 1.4987        | 25.1  | 13000 | 0.6641          | 0.4718 |
  | 1.3811        | 27.03 | 14000 | 0.6043          | 0.4470 |
  | 1.3811        | 28.96 | 15000 | 0.5532          | 0.4268 |
  | 1.2897        | 30.89 | 16000 | 0.5371          | 0.4101 |
  | 1.2897        | 32.82 | 17000 | 0.5924          | 0.4150 |
  | 1.225         | 34.75 | 18000 | 0.4949          | 0.3894 |
  | 1.225         | 36.68 | 19000 | 0.5591          | 0.4045 |
  | 1.193         | 38.61 | 20000 | 0.4927          | 0.3731 |
  | 1.193         | 40.54 | 21000 | 0.4922          | 0.3712 |
  | 1.1482        | 42.47 | 22000 | 0.4799          | 0.3662 |
  | 1.1482        | 44.4  | 23000 | 0.4846          | 0.3648 |
  | 1.1201        | 46.33 | 24000 | 0.4770          | 0.3623 |
  | 1.1201        | 48.26 | 25000 | 0.4530          | 0.3426 |
  | 1.0892        | 50.19 | 26000 | 0.4523          | 0.3527 |
  | 1.0892        | 52.12 | 27000 | 0.4573          | 0.3443 |
  | 1.0583        | 54.05 | 28000 | 0.4488          | 0.3353 |
  | 1.0583        | 55.98 | 29000 | 0.4295          | 0.3285 |
  | 1.0319        | 57.92 | 30000 | 0.4321          | 0.3220 |
  | 1.0319        | 59.85 | 31000 | 0.4244          | 0.3236 |
  | 1.0076        | 61.78 | 32000 | 0.4197          | 0.3201 |
  | 1.0076        | 63.71 | 33000 | 0.4230          | 0.3208 |
  | 0.9851        | 65.64 | 34000 | 0.4090          | 0.3127 |
  | 0.9851        | 67.57 | 35000 | 0.4088          | 0.3133 |
  | 0.9695        | 69.5  | 36000 | 0.4123          | 0.3088 |
  | 0.9695        | 71.43 | 37000 | 0.4017          | 0.3090 |
  | 0.9514        | 73.36 | 38000 | 0.4184          | 0.3086 |
  | 0.9514        | 75.29 | 39000 | 0.4075          