metadata

language:
  - es
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
base_model: openai/whisper-medium
model-index:
  - name: Whisper Medium Es - Juan Carlos Piñeros
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: es
          split: test
          args: es
        metrics:
          - type: wer
            value: 5.421819787985865
            name: Wer

Whisper Medium Es - Juan Carlos Piñeros

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

Loss: 0.1672
Wer: 5.4218

Using the script provided in the Whisper Sprint (Dec. 2022) the models achieves these results on the evaluation sets (WER):

google/fleurs: 5.88
mozilla-foundation/common_voice_11_0: XXX

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: 32
eval_batch_size: 16
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: 3000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0792	0.33	1000	0.1904	6.0493
0.0851	0.67	2000	0.1757	5.9558
0.0946	1.0	3000	0.1672	5.4218

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.1.dev0
Tokenizers 0.13.2