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M_RN

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: MilladRN
    results: []

MilladRN

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 3.4355
  • Wer: 0.4907
  • Cer: 0.2802

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 4000
  • num_epochs: 750
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.3347 33.9 2000 2.2561 0.9888 0.6087
1.3337 67.8 4000 1.8137 0.6877 0.3407
0.6504 101.69 6000 2.0718 0.6245 0.3229
0.404 135.59 8000 2.2246 0.6004 0.3221
0.2877 169.49 10000 2.2624 0.5836 0.3107
0.2149 203.39 12000 2.3788 0.5279 0.2802
0.1693 237.29 14000 1.8928 0.5502 0.2937
0.1383 271.19 16000 2.7520 0.5725 0.3103
0.1169 305.08 18000 2.2552 0.5446 0.2968
0.1011 338.98 20000 2.6794 0.5725 0.3119
0.0996 372.88 22000 2.4704 0.5595 0.3142
0.0665 406.78 24000 2.9073 0.5836 0.3194
0.0538 440.68 26000 3.1357 0.5632 0.3213
0.0538 474.58 28000 2.5639 0.5613 0.3091
0.0493 508.47 30000 3.3801 0.5613 0.3119
0.0451 542.37 32000 3.5469 0.5428 0.3158
0.0307 576.27 34000 4.2243 0.5390 0.3126
0.0301 610.17 36000 3.6666 0.5297 0.2929
0.0269 644.07 38000 3.2164 0.5 0.2838
0.0182 677.97 40000 3.0557 0.4963 0.2779
0.0191 711.86 42000 3.5190 0.5130 0.2921
0.0133 745.76 44000 3.4355 0.4907 0.2802

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.12.0+cu113
  • Datasets 1.18.3
  • Tokenizers 0.12.1