README.md

metadata

base_model: openai/whisper-small
datasets:
  - mozilla-foundation/common_voice_17_0
  - google/fleurs
  - facebook/multilingual_librispeech
language:
  - pt
license: apache-2.0
metrics:
  - wer
tags:
  - whisper-event
  - generated_from_trainer
model-index:
  - name: Whisper Small Mixed-Portuguese
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_17_0 pt
          type: mozilla-foundation/common_voice_17_0
          config: pt
          split: test
          args: pt
        metrics:
          - type: wer
            value: 10.634930784232232
            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: 8.15
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: facebook/multilingual_librispeech
          type: facebook/multilingual_librispeech
          config: portuguese
          split: test
        metrics:
          - type: wer
            value: 9.69
            name: WER

Whisper Small Mixed-Portuguese

This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_17_0 pt dataset. It achieves the following results on the evaluation set:

• Loss: 0.2000
• Wer: 10.6349

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
0.1575	0.2	1000	0.2125	12.1855
0.1986	0.4	2000	0.2062	11.5701
0.0942	1.131	3000	0.1979	11.0154
0.0577	1.331	4000	0.2000	10.6349
0.0516	2.062	5000	0.2007	10.6701

Framework versions

• Transformers 4.42.0.dev0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1