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HarrisDePerceptron
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xls-r-300m-ur

Automatic Speech Recognition
Transformers
PyTorch
Urdu
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Inference Endpoints
Model card Files Community
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metadata
language:
  - ur
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - ur
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: ''
    results: []

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

  • Loss: 1.0517
  • WER: 0.5151291512915129
  • CER: 0.23689640940982254

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: 100
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.2991 1.96 100 0.9769 0.6627
1.3415 3.92 200 0.9701 0.6594
1.2998 5.88 300 0.9678 0.6668
1.2881 7.84 400 0.9650 0.6613
1.2369 9.8 500 0.9392 0.6502
1.2293 11.76 600 0.9536 0.6480
1.1709 13.73 700 0.9265 0.6402
1.1492 15.69 800 0.9636 0.6506
1.1044 17.65 900 0.9305 0.6351
1.0704 19.61 1000 0.9329 0.6280
1.0039 21.57 1100 0.9413 0.6295
0.9756 23.53 1200 0.9718 0.6185
0.9633 25.49 1300 0.9731 0.6133
0.932 27.45 1400 0.9659 0.6199
0.9252 29.41 1500 0.9766 0.6196
0.9172 31.37 1600 1.0052 0.6199
0.8733 33.33 1700 0.9955 0.6203
0.868 35.29 1800 1.0069 0.6240
0.8547 37.25 1900 0.9783 0.6258
0.8451 39.22 2000 0.9845 0.6052
0.8374 41.18 2100 0.9496 0.6137
0.8153 43.14 2200 0.9756 0.6122
0.8134 45.1 2300 0.9712 0.6096
0.8019 47.06 2400 0.9565 0.5970
0.7746 49.02 2500 0.9864 0.6096
0.7664 50.98 2600 0.9988 0.6092
0.7708 52.94 2700 1.0181 0.6255
0.7468 54.9 2800 0.9918 0.6148
0.7241 56.86 2900 1.0150 0.6018
0.7165 58.82 3000 1.0439 0.6063
0.7104 60.78 3100 1.0016 0.6037
0.6954 62.75 3200 1.0117 0.5970
0.6753 64.71 3300 1.0191 0.6037
0.6803 66.67 3400 1.0190 0.6033
0.661 68.63 3500 1.0284 0.6007
0.6597 70.59 3600 1.0060 0.5967
0.6398 72.55 3700 1.0372 0.6048
0.6105 74.51 3800 1.0048 0.6044
0.6164 76.47 3900 1.0398 0.6148
0.6354 78.43 4000 1.0272 0.6133
0.5952 80.39 4100 1.0364 0.6081
0.5814 82.35 4200 1.0418 0.6092
0.6079 84.31 4300 1.0277 0.5967
0.5748 86.27 4400 1.0362 0.6041
0.5624 88.24 4500 1.0427 0.6007
0.5767 90.2 4600 1.0370 0.5919
0.5793 92.16 4700 1.0442 0.6011
0.547 94.12 4800 1.0516 0.5982
0.5513 96.08 4900 1.0461 0.5989
0.5429 98.04 5000 1.0504 0.5996
0.5404 100.0 5100 1.0517 0.5967

Framework versions

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