metadata

language:
  - pt
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Large v2 Portuguese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 pt
          type: mozilla-foundation/common_voice_11_0
          config: pt
          split: test
          args: pt
        metrics:
          - name: Wer
            type: wer
            value: 5.590020342630419

Whisper Large V2 Portuguese 🇧🇷🇵🇹

Bem-vindo ao whisper large-v2 para transcrição em português 👋🏻

Transcribe Portuguese audio to text with the highest precision.

Loss: 0.282
Wer: 5.590

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11 dataset. If you want a lighter model, you may be interested in jlondonobo/whisper-medium-pt. It achieves faster inference with almost no difference in WER.

Comparable models

Reported WER is based on the evaluation subset of Common Voice.

Model	WER	# Parameters
jlondonobo/whisper-large-v2-pt	5.590 🤗	1550M
openai/whisper-large-v2	6.300	1550M
jlondonobo/whisper-medium-pt	6.579	769M
jonatasgrosman/wav2vec2-large-xlsr-53-portuguese	11.310	317M
Edresson/wav2vec2-large-xlsr-coraa-portuguese	20.080	317M

Training hyperparameters

We used the following hyperparameters for training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
0.0828	1.09	1000	0.1868	6.778
0.0241	3.07	2000	0.2057	6.109
0.0084	5.06	3000	0.2367	6.029
0.0015	7.04	4000	0.2469	5.709
0.0009	9.02	5000	0.2821	5.590 🤗

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2