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xls-r-300m-te

Automatic Speech Recognition
Transformers
PyTorch
Telugu
wav2vec2
openslr_SLR66
generated_from_trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
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metadata
language:
  - te
license: apache-2.0
tags:
  - automatic-speech-recognition
  - openslr_SLR66
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - openslr
  - SLR66
metrics:
  - wer
model-index:
  - name: xls-r-300m-te
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: openslr
          name: Open SLR
          args: SLR66
        metrics:
          - type: wer
            value: 24.695121951219512
            name: Test WER
          - type: cer
            value: 4.861934182322532
            name: Test CER

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2680
  • Wer: 0.3467

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: 7.5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.0304 4.81 500 1.5676 1.0554
1.5263 9.61 1000 0.4693 0.8023
1.5299 14.42 1500 0.4368 0.7311
1.5063 19.23 2000 0.4360 0.7302
1.455 24.04 2500 0.4213 0.6692
1.4755 28.84 3000 0.4329 0.5943
1.352 33.65 3500 0.4074 0.5765
1.3122 38.46 4000 0.3866 0.5630
1.2799 43.27 4500 0.3860 0.5480
1.212 48.08 5000 0.3590 0.5317
1.1645 52.88 5500 0.3283 0.4757
1.0854 57.69 6000 0.3162 0.4687
1.0292 62.5 6500 0.3126 0.4416
0.9607 67.31 7000 0.2990 0.4066
0.9156 72.12 7500 0.2870 0.4009
0.8329 76.92 8000 0.2791 0.3909
0.7979 81.73 8500 0.2770 0.3670
0.7144 86.54 9000 0.2841 0.3661
0.6997 91.35 9500 0.2721 0.3485
0.6568 96.15 10000 0.2681 0.3437

Framework versions

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