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sew-d-mid-400k-librispeech-clean-100h-ft

Automatic Speech Recognition
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sew-d
librispeech_asr
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metadata
license: apache-2.0
tags:
  - automatic-speech-recognition
  - librispeech_asr
  - generated_from_trainer
model-index:
  - name: sew-d-mid-400k-librispeech-clean-100h-ft
    results: []

sew-d-mid-400k-librispeech-clean-100h-ft

This model is a fine-tuned version of asapp/sew-d-mid-400k on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3540
  • Wer: 1.0536

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: 3e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
7.319 0.11 100 11.0572 1.0
3.6726 0.22 200 4.2003 1.0
2.981 0.34 300 3.5742 0.9919
2.9411 0.45 400 3.2599 1.0
2.903 0.56 500 2.9350 1.0
2.8597 0.67 600 2.9514 1.0
2.7771 0.78 700 2.8521 1.0
2.7926 0.9 800 2.7821 1.0120
2.6623 1.01 900 2.7027 0.9924
2.5893 1.12 1000 2.6667 1.0240
2.5733 1.23 1100 2.6341 1.0368
2.5455 1.35 1200 2.5928 1.0411
2.4919 1.46 1300 2.5695 1.0817
2.5182 1.57 1400 2.5559 1.1072
2.4766 1.68 1500 2.5229 1.1257
2.4267 1.79 1600 2.4991 1.1151
2.3919 1.91 1700 2.4768 1.1139
2.3883 2.02 1800 2.4452 1.0636
2.3737 2.13 1900 2.4304 1.0594
2.3569 2.24 2000 2.4095 1.0539
2.3641 2.35 2100 2.3997 1.0511
2.3281 2.47 2200 2.3856 1.0414
2.2912 2.58 2300 2.3750 1.0696
2.3028 2.69 2400 2.3684 1.0436
2.2906 2.8 2500 2.3613 1.0538
2.2822 2.91 2600 2.3558 1.0506

Framework versions

  • Transformers 4.12.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 1.13.4.dev0
  • Tokenizers 0.10.3