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em_ctc

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
wav_sub-P001
Generated from Trainer
Eval Results
Inference Endpoints
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em_ctc / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - automatic-speech-recognition
  - wav_sub-P001
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
model-index:
  - name: em_ctc
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: WAV_SUB-P001 - TR
          type: audiofolder
          config: default
          split: validation
          args: 'Config: tr, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.9843253066787824

em_ctc

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the WAV_SUB-P001 - TR dataset. It achieves the following results on the evaluation set:

  • Loss: 2.7286
  • Wer: 0.9843

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.16 100 3.8990 1.0
No log 0.32 200 2.8678 1.0
No log 0.48 300 2.7795 1.0
No log 0.65 400 2.7316 1.0
4.389 0.81 500 2.6889 0.9784
4.389 0.97 600 3.2727 0.9784
4.389 1.13 700 2.7057 0.9784
4.389 1.29 800 2.8525 0.9964
4.389 1.45 900 2.6685 0.9968
2.5649 1.61 1000 2.7403 1.0
2.5649 1.78 1100 2.7790 1.0
2.5649 1.94 1200 2.8130 0.9786
2.5649 2.1 1300 2.8031 1.0
2.5649 2.26 1400 2.9683 1.0
2.5421 2.42 1500 2.9459 0.9784
2.5421 2.58 1600 2.7052 0.9784
2.5421 2.74 1700 2.7879 0.9786
2.5421 2.91 1800 2.7956 1.0
2.5421 3.07 1900 2.7760 0.9784
2.5357 3.23 2000 2.8594 0.9995
2.5357 3.39 2100 2.9048 0.9796
2.5357 3.55 2200 3.0098 0.9784
2.5357 3.71 2300 2.7079 0.9784
2.5357 3.87 2400 3.2403 1.0
2.5203 4.04 2500 3.0476 0.9784
2.5203 4.2 2600 2.8510 1.0
2.5203 4.36 2700 2.7907 0.9784
2.5203 4.52 2800 2.7486 0.9784
2.5203 4.68 2900 3.1701 1.0
2.5191 4.84 3000 2.9529 0.9784
2.5191 5.0 3100 3.1192 0.9650
2.5191 5.17 3200 2.8596 1.0
2.5191 5.33 3300 2.9193 1.0
2.5191 5.49 3400 3.0367 0.9784
2.5422 5.65 3500 2.9162 0.9784
2.5422 5.81 3600 3.0334 1.0
2.5422 5.97 3700 2.8514 0.9784
2.5422 6.13 3800 2.9654 1.0
2.5422 6.3 3900 3.2616 0.9784
2.5062 6.46 4000 3.3320 0.9793
2.5062 6.62 4100 2.7141 0.9784
2.5062 6.78 4200 3.2108 0.9784
2.5062 6.94 4300 3.0015 0.9784
2.5062 7.1 4400 3.0244 1.0
2.5114 7.26 4500 2.8742 0.9784
2.5114 7.43 4600 3.1471 0.9784
2.5114 7.59 4700 2.7006 0.9773
2.5114 7.75 4800 3.1189 1.0
2.5114 7.91 4900 3.1604 0.9784
2.5065 8.07 5000 2.9297 0.9784
2.5065 8.23 5100 3.0998 0.9784
2.5065 8.39 5200 2.8184 0.9843
2.5065 8.56 5300 2.7133 0.9861
2.5065 8.72 5400 2.7399 0.9811
2.4956 8.88 5500 2.7186 0.9889
2.4956 9.04 5600 2.9872 0.9955
2.4956 9.2 5700 3.0825 0.9993
2.4956 9.36 5800 3.0589 0.9855
2.4956 9.52 5900 2.8177 0.9784
2.4774 9.69 6000 2.8104 0.9993
2.4774 9.85 6100 2.9498 0.9796
2.4774 10.01 6200 3.0006 0.9784
2.4774 10.17 6300 2.8100 0.9784
2.4774 10.33 6400 3.1577 0.9786
2.4689 10.49 6500 2.7814 0.9977
2.4689 10.65 6600 2.7271 0.9836
2.4689 10.82 6700 2.8403 0.9784
2.4689 10.98 6800 2.7257 0.9998
2.4689 11.14 6900 2.6728 0.9898
2.486 11.3 7000 2.7348 0.9809
2.486 11.46 7100 2.7054 0.9982
2.486 11.62 7200 2.7254 0.9948
2.486 11.78 7300 2.7498 0.9891
2.486 11.95 7400 2.7076 0.9898
2.4616 12.11 7500 2.6398 0.9995
2.4616 12.27 7600 2.7626 0.9846
2.4616 12.43 7700 2.6804 0.9814
2.4616 12.59 7800 2.8212 0.9834
2.4616 12.75 7900 2.6535 0.9959
2.4573 12.91 8000 2.7547 0.9993
2.4573 13.08 8100 2.7253 0.9798
2.4573 13.24 8200 2.6851 0.9936
2.4573 13.4 8300 2.7627 0.9907
2.4573 13.56 8400 2.6607 0.9857
2.4487 13.72 8500 2.6645 0.9800
2.4487 13.88 8600 2.7558 0.9973
2.4487 14.04 8700 2.7665 0.9961
2.4487 14.21 8800 2.7697 0.9827
2.4487 14.37 8900 2.8531 0.9918
2.4416 14.53 9000 2.8974 0.9920
2.4416 14.69 9100 2.7308 0.9975
2.4416 14.85 9200 2.7919 0.9816
2.4416 15.01 9300 2.6605 0.9893
2.4416 15.17 9400 2.6058 0.9816
2.4405 15.33 9500 2.6366 0.9911
2.4405 15.5 9600 2.5653 0.9818
2.4405 15.66 9700 2.7026 0.9807
2.4405 15.82 9800 2.7358 0.9796
2.4405 15.98 9900 2.6954 0.9848
2.4352 16.14 10000 2.6610 0.9857
2.4352 16.3 10100 2.7686 0.9811
2.4352 16.46 10200 2.7758 0.9798
2.4352 16.63 10300 2.7515 0.9848
2.4352 16.79 10400 2.7264 0.9911
2.4354 16.95 10500 2.7039 0.9791
2.4354 17.11 10600 2.7580 0.9843
2.4354 17.27 10700 2.7187 0.9855
2.4354 17.43 10800 2.7545 0.9798
2.4354 17.59 10900 2.7452 0.9809
2.4321 17.76 11000 2.6804 0.9836
2.4321 17.92 11100 2.6586 0.9891
2.4321 18.08 11200 2.6805 0.9830
2.4321 18.24 11300 2.6626 0.9871
2.4321 18.4 11400 2.7002 0.9809
2.4193 18.56 11500 2.7054 0.9839
2.4193 18.72 11600 2.7171 0.9900
2.4193 18.89 11700 2.7122 0.9852
2.4193 19.05 11800 2.7058 0.9871
2.4193 19.21 11900 2.7004 0.9839
2.4276 19.37 12000 2.7250 0.9852
2.4276 19.53 12100 2.7126 0.9861
2.4276 19.69 12200 2.7388 0.9834
2.4276 19.85 12300 2.7311 0.9850

Framework versions

  • Transformers 4.34.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.0