metadata

language:
  - pt
license: cc-by-4.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Tiny PT
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: pt
          split: test
          args: pt
        metrics:
          - type: wer
            value: 29.11
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: pt_br
          split: test
        metrics:
          - type: wer
            value: 26.36
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_9_0
          type: mozilla-foundation/common_voice_9_0
          config: pt
          split: test
        metrics:
          - type: wer
            value: 28.68
            name: WER

Whisper Tiny PT

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.6077
Wer: 29.9844

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: 64
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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4143	1.04	500	0.5325	32.7399
0.2693	3.03	1000	0.4718	29.4867
0.1724	5.01	1500	0.4758	28.7218
0.0849	7.0	2000	0.5070	29.2211
0.0659	8.04	2500	0.5223	29.3169
0.0539	10.03	3000	0.5402	30.1458
0.0376	12.02	3500	0.5755	29.9995
0.0217	14.0	4000	0.6067	29.6565
0.0168	15.04	4500	0.6082	29.8162
0.0205	17.03	5000	0.6077	29.9844

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu116
Datasets 2.7.1.dev0
Tokenizers 0.13.2