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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
  - bleu
model-index:
  - name: wav2vec2-mms-1b-CV17.0-training_set_variations
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: ta
          split: validation
          args: ta
        metrics:
          - name: Wer
            type: wer
            value: 0.38488334784800843
          - name: Bleu
            type: bleu
            value: 0.3848277074951031

wav2vec2-mms-1b-CV17.0-training_set_variations

This model is a fine-tuned version of facebook/mms-1b-all on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2335
  • Wer: 0.3849
  • Cer: 0.0627
  • Bleu: 0.3848

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: 0.001
  • 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_ratio: 0.15
  • training_steps: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer Bleu
12.5537 1.5625 50 3.9513 1.0006 0.9854 0.0
2.2034 3.125 100 0.3019 0.4137 0.0683 0.3510
0.226 4.6875 150 0.2305 0.3794 0.0623 0.3981
0.1904 6.25 200 0.2262 0.3776 0.0618 0.3988
0.1798 7.8125 250 0.2275 0.3760 0.0621 0.4040
0.1724 9.375 300 0.2399 0.4021 0.0659 0.3610
0.1791 10.9375 350 0.2310 0.3883 0.0635 0.3797
0.1678 12.5 400 0.2405 0.3961 0.0666 0.3722
0.1527 14.0625 450 0.2335 0.3849 0.0627 0.3848

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1