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DrishtiSharma
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wav2vec2-large-xls-r-300m-br-d2

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Breton
wav2vec2
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Metrics Community
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metadata
language:
  - br
license: apache-2.0
tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-br-d2
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice 8
          args: br
        metrics:
          - type: wer
            value: 0.49770598355954887
            name: Test WER
          - name: Test CER
            type: cer
            value: 0.18090500890299605
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: br
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-br-d2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1257
  • Wer: 0.4631

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs

  1. To evaluate on speech-recognition-community-v2/dev_data

Breton language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.00034
  • 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: 750
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
14.0379 0.68 100 5.6808 1.0
3.9145 1.35 200 3.1970 1.0
3.0293 2.03 300 2.9513 1.0
2.0927 2.7 400 1.4545 0.8887
1.1556 3.38 500 1.0966 0.7564
0.9628 4.05 600 0.9808 0.7364
0.7869 4.73 700 1.0488 0.7355
0.703 5.41 800 0.9500 0.6881
0.6657 6.08 900 0.9309 0.6259
0.5663 6.76 1000 0.9133 0.6357
0.496 7.43 1100 0.9890 0.6028
0.4748 8.11 1200 0.9469 0.5894
0.4135 8.78 1300 0.9270 0.6045
0.3579 9.46 1400 0.8818 0.5708
0.353 10.14 1500 0.9244 0.5781
0.334 10.81 1600 0.9009 0.5638
0.2917 11.49 1700 1.0132 0.5828
0.29 12.16 1800 0.9696 0.5668
0.2691 12.84 1900 0.9811 0.5455
0.25 13.51 2000 0.9951 0.5624
0.2467 14.19 2100 0.9653 0.5573
0.2242 14.86 2200 0.9714 0.5378
0.2066 15.54 2300 0.9829 0.5394
0.2075 16.22 2400 1.0547 0.5520
0.1923 16.89 2500 1.0014 0.5397
0.1919 17.57 2600 0.9978 0.5477
0.1908 18.24 2700 1.1064 0.5397
0.157 18.92 2800 1.0629 0.5238
0.159 19.59 2900 1.0642 0.5321
0.1652 20.27 3000 1.0207 0.5328
0.141 20.95 3100 0.9948 0.5312
0.1417 21.62 3200 1.0338 0.5328
0.1514 22.3 3300 1.0513 0.5313
0.1365 22.97 3400 1.0357 0.5291
0.1319 23.65 3500 1.0587 0.5167
0.1298 24.32 3600 1.0636 0.5236
0.1245 25.0 3700 1.1367 0.5280
0.1114 25.68 3800 1.0633 0.5200
0.1088 26.35 3900 1.0495 0.5210
0.1175 27.03 4000 1.0897 0.5095
0.1043 27.7 4100 1.0580 0.5309
0.0951 28.38 4200 1.0448 0.5067
0.1011 29.05 4300 1.0665 0.5137
0.0889 29.73 4400 1.0579 0.5026
0.0833 30.41 4500 1.0740 0.5037
0.0889 31.08 4600 1.0933 0.5083
0.0784 31.76 4700 1.0715 0.5089
0.0767 32.43 4800 1.0658 0.5049
0.0769 33.11 4900 1.1118 0.4979
0.0722 33.78 5000 1.1413 0.4986
0.0709 34.46 5100 1.0706 0.4885
0.0664 35.14 5200 1.1217 0.4884
0.0648 35.81 5300 1.1298 0.4941
0.0657 36.49 5400 1.1330 0.4920
0.0582 37.16 5500 1.0598 0.4835
0.0602 37.84 5600 1.1097 0.4943
0.0598 38.51 5700 1.0976 0.4876
0.0547 39.19 5800 1.0734 0.4825
0.0561 39.86 5900 1.0926 0.4850
0.0516 40.54 6000 1.1579 0.4751
0.0478 41.22 6100 1.1384 0.4706
0.0396 41.89 6200 1.1462 0.4739
0.0472 42.57 6300 1.1277 0.4732
0.0447 43.24 6400 1.1517 0.4752
0.0423 43.92 6500 1.1219 0.4784
0.0426 44.59 6600 1.1311 0.4724
0.0391 45.27 6700 1.1135 0.4692
0.0362 45.95 6800 1.0878 0.4645
0.0329 46.62 6900 1.1137 0.4668
0.0356 47.3 7000 1.1233 0.4687
0.0328 47.97 7100 1.1238 0.4653
0.0323 48.65 7200 1.1307 0.4646
0.0325 49.32 7300 1.1242 0.4645
0.03 50.0 7400 1.1257 0.4631

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0