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metadata
base_model: wav2vec2-pretrained-base-hyperVQ
tags:
  - automatic-speech-recognition
  - timit_asr
  - generated_from_trainer
datasets:
  - timit_asr
metrics:
  - wer
model-index:
  - name: wav2vec2-base-hyperVQ-timit-fine-tuned
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: TIMIT_ASR - NA
          type: timit_asr
          config: clean
          split: test
          args: 'Config: na, Training split: train, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.5904486251808972

wav2vec2-base-hyperVQ-timit-fine-tuned

This model is a fine-tuned version of wav2vec2-pretrained-base-hyperVQ on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6917
  • Wer: 0.5904

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Wer
0.6057 10.0 1450 0.6450 0.6166
0.327 20.0 2900 0.6917 0.5904

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.3.0.dev20231229+cu118
  • Datasets 2.16.0
  • Tokenizers 0.15.0