README.md

metadata

language:
  - pt
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - M2LabOrg/jwlang
metrics:
  - wer
model-index:
  - name: Whisper small pt jwlang - Michel Mesquita
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: jwlang 1.0
          type: M2LabOrg/jwlang
          args: 'config: pt, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 23.25581395348837

Whisper small pt jwlang - Michel Mesquita

This model is a fine-tuned version of openai/whisper-small on the jwlang 1.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6913
• Wer: 23.2558

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0053	14.0845	1000	0.6128	22.3990
0.0002	28.1690	2000	0.6528	22.6438
0.0001	42.2535	3000	0.6806	23.0110
0.0001	56.3380	4000	0.6913	23.2558

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1