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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - automatic-speech-recognition
  - google/fleurs
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice-en-finetune
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: GOOGLE/FLEURS - EN_US
          type: fleurs
          config: en_us
          split: test
          args: 'Config: en_us, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.25982311149775267

wav2vec2-common_voice-en-finetune

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the GOOGLE/FLEURS - EN_US dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3436
  • Wer: 0.2598

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: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 1.0870 100 3.5106 1.0
No log 2.1739 200 2.9226 1.0
No log 3.2609 300 2.8745 1.0
No log 4.3478 400 1.8100 0.9804
3.7609 5.4348 500 0.4771 0.4207
3.7609 6.5217 600 0.3808 0.3484
3.7609 7.6087 700 0.3408 0.2872
3.7609 8.6957 800 0.3479 0.2719
3.7609 9.7826 900 0.3437 0.2604

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1