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kids_phoneme_sm_model

Automatic Speech Recognition
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PyTorch
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wav2vec2
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kids_phoneme_sm_model / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - mirfan899/kids_phoneme_sm
base_model: facebook/wav2vec2-large-xlsr-53
model-index:
  - name: kids_phoneme_sm_model
    results: []

kids_phoneme_sm_model

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the https://huggingface.co/datasets/mirfan899/kids_phoneme_sm dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5405
  • Cer: 0.2770

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: 4e-05
  • train_batch_size: 2
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Cer
3.2595 0.74 500 3.7094 1.0
2.8393 1.48 1000 3.2563 1.0
2.7916 2.22 1500 3.0450 1.0
1.9585 2.96 2000 1.0280 0.8428
1.0099 3.7 2500 0.6477 0.5162
0.7968 4.44 3000 0.5551 0.4592
0.6977 5.19 3500 0.5107 0.4065
0.609 5.93 4000 0.4763 0.3916
0.5941 6.67 4500 0.4817 0.3850
0.5411 7.41 5000 0.4755 0.3639
0.5021 8.15 5500 0.4649 0.3622
0.4884 8.89 6000 0.4630 0.3569
0.4484 9.63 6500 0.4675 0.3420
0.4432 10.37 7000 0.4192 0.3402
0.399 11.11 7500 0.4508 0.3310
0.4215 11.85 8000 0.4406 0.3345
0.366 12.59 8500 0.4620 0.3248
0.3708 13.33 9000 0.4594 0.3327
0.3352 14.07 9500 0.4649 0.3121
0.3468 14.81 10000 0.4413 0.3020
0.3283 15.56 10500 0.4948 0.2915
0.3222 16.3 11000 0.4870 0.3025
0.3081 17.04 11500 0.4779 0.2919
0.3099 17.78 12000 0.4927 0.2871
0.2485 18.52 12500 0.5013 0.2831
0.3163 19.26 13000 0.4929 0.2888
0.2555 20.0 13500 0.5234 0.2888
0.2705 20.74 14000 0.5259 0.2818
0.2632 21.48 14500 0.5105 0.2831
0.2374 22.22 15000 0.5284 0.2845
0.2565 22.96 15500 0.5237 0.2875
0.2394 23.7 16000 0.5368 0.2818
0.2458 24.44 16500 0.5386 0.2814
0.2383 25.19 17000 0.5366 0.2788
0.2152 25.93 17500 0.5320 0.2770
0.231 26.67 18000 0.5441 0.2779
0.2061 27.41 18500 0.5448 0.2796
0.245 28.15 19000 0.5413 0.2796
0.2119 28.89 19500 0.5379 0.2774
0.2155 29.63 20000 0.5405 0.2770

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.0
  • Datasets 2.13.0
  • Tokenizers 0.13.3