README.md

metadata

library_name: transformers
language:
  - qve
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - cportoca/Quechua_dataset
metrics:
  - wer
model-index:
  - name: Whisper Small Ja-Qve - cportoca
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Quechua_dataset
          type: cportoca/Quechua_dataset
          args: 'config: Qve, split: train/test'
        metrics:
          - name: Wer
            type: wer
            value: 92.72042673360528

Whisper Small Ja-Qve - cportoca

This model is a fine-tuned version of openai/whisper-small on the Quechua_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 1.5509
• Wer: 92.7204

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.7598	0.1355	100	1.5509	92.7204

Framework versions

• Transformers 4.46.3
• Pytorch 2.4.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3