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metadata
language:
  - ur
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_9_0
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_9_0
metrics:
  - wer
model-index:
  - name: XLS-R-300M - Urdu
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_9_0
          name: Common Voice 9
          args: ur
        metrics:
          - type: wer
            value: 23.75
            name: Test WER
          - name: Test CER
            type: cer
            value: 8.31

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - UR dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4147
  • Wer: 0.3172
  • Cer: 0.1050

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: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 5108
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.2894 7.83 400 3.1501 1.0 1.0
1.8586 15.68 800 0.8871 0.6721 0.2402
1.3431 23.52 1200 0.5813 0.5502 0.1939
1.2052 31.37 1600 0.4956 0.4788 0.1665
1.1097 39.21 2000 0.4447 0.4143 0.1397
1.0528 47.06 2400 0.4439 0.3961 0.1333
0.9939 54.89 2800 0.4348 0.4014 0.1379
0.9441 62.74 3200 0.4236 0.3653 0.1223
0.913 70.58 3600 0.4309 0.3475 0.1157
0.8678 78.43 4000 0.4270 0.3337 0.1110
0.8414 86.27 4400 0.4158 0.3220 0.1070
0.817 94.12 4800 0.4185 0.3231 0.1072

Framework versions

  • Transformers 4.19.0.dev0
  • Pytorch 1.11.0+cu102
  • Datasets 2.1.1.dev0
  • Tokenizers 0.12.1