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metadata
language:
  - ur
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
model-index:
  - name: wav2vec2-large-xls-r-300m-Urdu
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice ur
          args: ur
        metrics:
          - type: wer
            value: 61.1
            name: Test WER
          - type: cer
            value: 25.17
            name: Test CER

wav2vec2-large-xls-r-300m-Urdu

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

  • Loss: 1.5867
  • Wer: 0.6240
  • Cer: 0.2579

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • 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: 200
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
11.1231 8.31 100 3.5275 1.0 1.0
3.4028 16.63 200 3.1164 1.0 1.0
3.1973 24.94 300 2.5590 1.0 1.0
1.3774 33.31 400 1.4729 0.7646 0.3325
0.5427 41.63 500 1.4140 0.6939 0.3001
0.3541 49.94 600 1.4532 0.6580 0.2815
0.26 58.31 700 1.5309 0.6403 0.2726
0.2025 66.63 800 1.5230 0.6310 0.2655
0.171 74.94 900 1.5578 0.6336 0.2632
0.1511 83.31 1000 1.5733 0.6321 0.2635
0.1352 91.63 1100 1.6022 0.6255 0.2608
0.1192 99.94 1200 1.5867 0.6240 0.2579

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0