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sew-tiny-portuguese-cv7

Automatic Speech Recognition Transformers PyTorch TensorBoard
Portuguese sew generated_from_trainer hf-asr-leaderboard robust-speech-event Eval Results Inference Endpoints
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metadata
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

sew-tiny-portuguese-cv7

This model is a fine-tuned version of 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 101.3 39000 0.4241 0.2761
0.7348 103.89 40000 0.4232 0.2745

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0