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wav2vec2-xslr-tr-testv2

Automatic Speech Recognition
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-1b
tags:
  - automatic-speech-recognition
  - genbed
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xslr-tr-testv2
    results: []

wav2vec2-xslr-tr-testv2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the GENBED - BEM dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2789
  • Wer: 0.4783

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: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.1375 100 2.9646 1.0
No log 0.2749 200 0.9689 0.9848
No log 0.4124 300 0.8561 0.9170
No log 0.5498 400 0.7970 0.912
1.9898 0.6873 500 0.8464 0.9258
1.9898 0.8247 600 0.7358 0.8872
1.9898 0.9622 700 0.6374 0.8608
1.9898 1.0997 800 0.5180 0.7297
1.9898 1.2371 900 0.4852 0.7212
0.663 1.3746 1000 0.4840 0.7278
0.663 1.5120 1100 0.4626 0.7135
0.663 1.6495 1200 0.4493 0.676
0.663 1.7869 1300 0.4419 0.6813
0.663 1.9244 1400 0.4306 0.6749
0.5455 2.0619 1500 0.4329 0.6846
0.5455 2.1993 1600 0.4227 0.6685
0.5455 2.3368 1700 0.4097 0.6472
0.5455 2.4742 1800 0.4035 0.6343
0.5455 2.6117 1900 0.4041 0.6304
0.433 2.7491 2000 0.3962 0.6542
0.433 2.8866 2100 0.3601 0.6041
0.433 3.0241 2200 0.3473 0.5864
0.433 3.1615 2300 0.3456 0.5723
0.433 3.2990 2400 0.3380 0.5617
0.3509 3.4364 2500 0.3267 0.5563
0.3509 3.5739 2600 0.3208 0.5570
0.3509 3.7113 2700 0.3124 0.5397
0.3509 3.8488 2800 0.3038 0.5272
0.3509 3.9863 2900 0.2994 0.5254
0.2871 4.1237 3000 0.3073 0.5247
0.2871 4.2612 3100 0.3009 0.5122
0.2871 4.3986 3200 0.2975 0.4953
0.2871 4.5361 3300 0.2898 0.4938
0.2871 4.6735 3400 0.2835 0.4902
0.2198 4.8110 3500 0.2804 0.4802
0.2198 4.9485 3600 0.2789 0.4783

Framework versions

  • Transformers 4.46.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0