xlsr-large-53-ur / README.md
anton-l's picture
anton-l HF staff
Upload README.md
6091035
metadata
language:
  - ur
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - ur
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: ''
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8.0
          type: mozilla-foundation/common_voice_8_0
          args: ur
        metrics:
          - name: Test WER
            type: wer
            value: 62.47

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8888
  • Wer: 0.6642

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: 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: 50
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
10.1224 1.96 100 3.5429 1.0
3.2411 3.92 200 3.1786 1.0
3.1283 5.88 300 3.0571 1.0
3.0044 7.84 400 2.9560 0.9996
2.9388 9.8 500 2.8977 1.0011
2.86 11.76 600 2.6944 0.9952
2.5538 13.73 700 2.0967 0.9435
2.1214 15.69 800 1.4816 0.8428
1.8136 17.65 900 1.2459 0.8048
1.6795 19.61 1000 1.1232 0.7649
1.5571 21.57 1100 1.0510 0.7432
1.4975 23.53 1200 1.0298 0.6963
1.4485 25.49 1300 0.9775 0.7074
1.3924 27.45 1400 0.9798 0.6956
1.3604 29.41 1500 0.9345 0.7092
1.3224 31.37 1600 0.9535 0.6830
1.2816 33.33 1700 0.9178 0.6679
1.2623 35.29 1800 0.9249 0.6679
1.2421 37.25 1900 0.9124 0.6734
1.2208 39.22 2000 0.8962 0.6664
1.2145 41.18 2100 0.8903 0.6734
1.1888 43.14 2200 0.8883 0.6708
1.1933 45.1 2300 0.8928 0.6723
1.1838 47.06 2400 0.8868 0.6679
1.1634 49.02 2500 0.8886 0.6657

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0