wav2vec2-librispeech-demo

This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0030
  • Wer: 1.0225

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: 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: 500
  • num_epochs: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.6329 100 3.9409 1.0
No log 1.2658 200 3.0441 1.0
No log 1.8987 300 2.9165 1.0
No log 2.5316 400 1.4925 1.9968
3.7012 3.1646 500 0.3010 1.9446
3.7012 3.7975 600 0.1713 1.8259
3.7012 4.4304 700 0.0990 1.6163
3.7012 5.0633 800 0.0692 1.5439
3.7012 5.6962 900 0.0463 1.4233
0.1686 6.3291 1000 0.0389 1.3469
0.1686 6.9620 1100 0.0290 1.3101
0.1686 7.5949 1200 0.0204 1.1994
0.1686 8.2278 1300 0.0161 1.1839
0.1686 8.8608 1400 0.0143 1.1499
0.0553 9.4937 1500 0.0110 1.1460
0.0553 10.1266 1600 0.0082 1.0953
0.0553 10.7595 1700 0.0088 1.1119
0.0553 11.3924 1800 0.0059 1.0574
0.0553 12.0253 1900 0.0054 1.0510
0.0295 12.6582 2000 0.0042 1.0356
0.0295 13.2911 2100 0.0039 1.0360
0.0295 13.9241 2200 0.0033 1.0269
0.0295 14.5570 2300 0.0031 1.0237

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.5.1
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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Dataset used to train suhas-hegde5/wav2vec2-librispeech-demo

Evaluation results