metadata

language:
  - da
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small da - Common Voice+FLEURS
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0, FLEURS
          type: mozilla-foundation/common_voice_11_0
          config: da
          split: test
          args: da
        metrics:
          - name: Wer
            type: wer
            value: 138.8532351394003

Whisper Small da - Common Voice+FLEURS

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

Loss: 0.9242
Wer: 138.8532

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-07
train_batch_size: 64
eval_batch_size: 32
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
1.22	15.0	1000	1.2238	197.5434
1.004	30.0	2000	1.0386	197.9221
0.9104	45.01	3000	0.9704	156.6544
0.8455	60.01	4000	0.9352	142.3619
0.8237	75.01	5000	0.9242	138.8532

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2