metadata

base_model: openai/whisper-medium
datasets:
  - fleurs
language:
  - pt
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Medium Portuguese 6000 - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: fleurs
          config: pt_br
          split: None
          args: 'config: pt split: test'
        metrics:
          - type: wer
            value: 6.398382774669738
            name: Wer

Whisper Medium Portuguese 6000 - Chee Li

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

Loss: 0.2240
Wer: 6.3984

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
0.0071	5.0251	1000	0.1866	6.2479
0.0004	10.0503	2000	0.2069	6.3091
0.0002	15.0754	3000	0.2203	6.3561
0.0002	20.1005	4000	0.2240	6.3984

Framework versions

Transformers 4.43.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1