xls-r-300m-ur-cv7 / README.md
HarrisDePerceptron's picture
update model card README.md
2ded961
metadata
language:
  - ur
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2924
  • Wer: 0.7201

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: 200.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
11.2783 4.17 100 4.6409 1.0
3.5578 8.33 200 3.1649 1.0
3.1279 12.5 300 3.0335 1.0
2.9944 16.67 400 2.9526 0.9983
2.9275 20.83 500 2.9291 1.0009
2.8077 25.0 600 2.5633 0.9895
2.4438 29.17 700 1.9045 0.9564
1.9659 33.33 800 1.4114 0.7960
1.7092 37.5 900 1.2584 0.7637
1.517 41.67 1000 1.2040 0.7507
1.3966 45.83 1100 1.1273 0.7463
1.3197 50.0 1200 1.1054 0.6957
1.2476 54.17 1300 1.1035 0.7001
1.1796 58.33 1400 1.0890 0.7097
1.1237 62.5 1500 1.0883 0.7167
1.0777 66.67 1600 1.1067 0.7219
1.0051 70.83 1700 1.1115 0.7236
0.9521 75.0 1800 1.0867 0.7132
0.9147 79.17 1900 1.0852 0.7210
0.8798 83.33 2000 1.1411 0.7097
0.8317 87.5 2100 1.1634 0.7018
0.7946 91.67 2200 1.1621 0.7201
0.7594 95.83 2300 1.1482 0.7036
0.729 100.0 2400 1.1493 0.7062
0.7055 104.17 2500 1.1726 0.6931
0.6622 108.33 2600 1.1938 0.7001
0.6583 112.5 2700 1.1832 0.7149
0.6299 116.67 2800 1.1996 0.7175
0.5903 120.83 2900 1.1986 0.7132
0.5816 125.0 3000 1.1909 0.7010
0.5583 129.17 3100 1.2079 0.6870
0.5392 133.33 3200 1.2109 0.7228
0.5412 137.5 3300 1.2353 0.7245
0.5136 141.67 3400 1.2390 0.7254
0.5007 145.83 3500 1.2273 0.7123
0.4883 150.0 3600 1.2773 0.7289
0.4835 154.17 3700 1.2678 0.7289
0.4568 158.33 3800 1.2592 0.7350
0.4525 162.5 3900 1.2705 0.7254
0.4379 166.67 4000 1.2717 0.7306
0.4198 170.83 4100 1.2618 0.7219
0.4216 175.0 4200 1.2909 0.7158
0.4305 179.17 4300 1.2808 0.7167
0.399 183.33 4400 1.2750 0.7193
0.3937 187.5 4500 1.2719 0.7149
0.3905 191.67 4600 1.2816 0.7158
0.3892 195.83 4700 1.2951 0.7210
0.3932 200.0 4800 1.2924 0.7201

Framework versions

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