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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-lv60
tags:
  - automatic-speech-recognition
  - edinburghcstr/ami
  - generated_from_trainer
datasets:
  - ami
metrics:
  - wer
model-index:
  - name: wav2vec2-large-ami-fine-tuned
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: EDINBURGHCSTR/AMI - IHM
          type: ami
          config: ihm
          split: None
          args: 'Config: ihm, Training split: train, Eval split: validation'
        metrics:
          - name: Wer
            type: wer
            value: 0.9958454640117305

wav2vec2-large-ami-fine-tuned

This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the EDINBURGHCSTR/AMI - IHM dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9555
  • Wer: 0.9958

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: 0.00014
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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: 2.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.5455 0.1565 1000 1.3698 0.8373
1.3019 0.3131 2000 0.7275 0.4146
0.9922 0.4696 3000 0.6047 0.3663
0.5129 0.6262 4000 0.5773 0.3658
0.85 0.7827 5000 0.5387 0.3538
1.4588 0.9393 6000 0.5581 0.3326
0.2646 1.0958 7000 0.5216 0.3294
0.1923 1.2523 8000 0.4975 0.3159
0.2897 1.4089 9000 0.4757 0.3066
0.1536 1.5654 10000 0.4784 0.3066
0.3964 1.7220 11000 0.4899 0.3097
1.1026 1.8785 12000 0.9830 0.8711

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.0a0+gitcd033a1
  • Datasets 2.19.1
  • Tokenizers 0.19.1