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wav2vec2-xls-r-pt-cv7-from-bp400h

Automatic Speech Recognition Transformers PyTorch TensorBoard
Portuguese wav2vec2 generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_7_0 robust-speech-event Eval Results Inference Endpoints
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metadata
language:
  - pt
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - pt
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
license: apache-2.0
model-index:
  - name: wav2vec2-xls-r-pt-cv7-from-bp400h
    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: 12.13
          - name: Test CER
            type: cer
            value: 3.68
      - 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: 28.23
          - name: Test CER
            type: cer
            value: 12.58
      - 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: 26.58
      - 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: 26.86

wav2vec2-xls-r-pt-cv7-from-bp400h

This model is a fine-tuned version of lgris/bp_400h_xlsr2_300M on the common_voice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1535
  • Wer: 0.1254

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: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • training_steps: 5000

Training results

Training Loss Epoch Step Validation Loss Wer
0.4991 0.13 100 0.1774 0.1464
0.4655 0.26 200 0.1884 0.1568
0.4689 0.39 300 0.2282 0.1672
0.4662 0.52 400 0.1997 0.1584
0.4592 0.65 500 0.1989 0.1663
0.4533 0.78 600 0.2004 0.1698
0.4391 0.91 700 0.1888 0.1642
0.4655 1.04 800 0.1921 0.1624
0.4138 1.17 900 0.1950 0.1602
0.374 1.3 1000 0.2077 0.1658
0.4064 1.43 1100 0.1945 0.1596
0.3922 1.56 1200 0.2069 0.1665
0.4226 1.69 1300 0.1962 0.1573
0.3974 1.82 1400 0.1919 0.1553
0.3631 1.95 1500 0.1854 0.1573
0.3797 2.08 1600 0.1902 0.1550
0.3287 2.21 1700 0.1926 0.1598
0.3568 2.34 1800 0.1888 0.1534
0.3415 2.47 1900 0.1834 0.1502
0.3545 2.6 2000 0.1906 0.1560
0.3344 2.73 2100 0.1804 0.1524
0.3308 2.86 2200 0.1741 0.1485
0.344 2.99 2300 0.1787 0.1455
0.309 3.12 2400 0.1773 0.1448
0.312 3.25 2500 0.1738 0.1440
0.3066 3.38 2600 0.1727 0.1417
0.2999 3.51 2700 0.1692 0.1436
0.2985 3.64 2800 0.1732 0.1430
0.3058 3.77 2900 0.1754 0.1402
0.2943 3.9 3000 0.1691 0.1379
0.2813 4.03 3100 0.1754 0.1376
0.2733 4.16 3200 0.1639 0.1363
0.2592 4.29 3300 0.1675 0.1349
0.2697 4.42 3400 0.1618 0.1360
0.2538 4.55 3500 0.1658 0.1348
0.2746 4.67 3600 0.1674 0.1325
0.2655 4.8 3700 0.1655 0.1319
0.2745 4.93 3800 0.1665 0.1316
0.2617 5.06 3900 0.1600 0.1311
0.2674 5.19 4000 0.1623 0.1311
0.237 5.32 4100 0.1591 0.1315
0.2669 5.45 4200 0.1584 0.1295
0.2476 5.58 4300 0.1572 0.1285
0.2445 5.71 4400 0.1580 0.1271
0.2207 5.84 4500 0.1567 0.1269
0.2289 5.97 4600 0.1536 0.1260
0.2438 6.1 4700 0.1530 0.1260
0.227 6.23 4800 0.1544 0.1249
0.2256 6.36 4900 0.1543 0.1254
0.2184 6.49 5000 0.1535 0.1254

Framework versions

  • Transformers 4.15.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.0
  • Tokenizers 0.10.3