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xlsr-mk

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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xlsr-mk / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: xlsr-mk
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: mk
          split: validation
          args: mk
        metrics:
          - name: Wer
            type: wer
            value: 0.4437212531458821

Visualize in Weights & Biases

xlsr-mk

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6273
  • Wer: 0.4437
  • Cer: 0.1074

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: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.541 1.8868 100 3.5532 1.0 1.0
2.966 3.7736 200 2.9438 1.0 1.0
2.298 5.6604 300 2.1673 1.0 0.7080
0.5999 7.5472 400 0.7521 0.7476 0.2035
0.3941 9.4340 500 0.7249 0.6911 0.1845
0.2226 11.3208 600 0.6970 0.6602 0.1725
0.3031 13.2075 700 0.7692 0.6506 0.1680
0.1621 15.0943 800 0.7229 0.6232 0.1583
0.2052 16.9811 900 0.6990 0.5722 0.1471
0.1441 18.8679 1000 0.6829 0.5591 0.1400
0.0548 20.7547 1100 0.6560 0.5309 0.1333
0.1312 22.6415 1200 0.6590 0.5375 0.1332
0.0582 24.5283 1300 0.7023 0.5268 0.1321
0.1163 26.4151 1400 0.6900 0.5170 0.1293
0.0491 28.3019 1500 0.6499 0.5089 0.1274
0.063 30.1887 1600 0.6478 0.4869 0.1221
0.0735 32.0755 1700 0.6678 0.4967 0.1256
0.0437 33.9623 1800 0.6651 0.4803 0.1188
0.0514 35.8491 1900 0.6741 0.4724 0.1168
0.0306 37.7358 2000 0.6564 0.4717 0.1168
0.0458 39.6226 2100 0.6428 0.4679 0.1140
0.0398 41.5094 2200 0.6385 0.4531 0.1103
0.0574 43.3962 2300 0.5991 0.4392 0.1063
0.0481 45.2830 2400 0.6394 0.4468 0.1087
0.0376 47.1698 2500 0.6184 0.4434 0.1072
0.0275 49.0566 2600 0.6273 0.4437 0.1074

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1