metadata

license: apache-2.0
base_model: qanastek/whisper-base-french-cased
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Base French
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs fr_fr
          type: google/fleurs
          config: fr_fr
          split: test
          args: fr_fr
        metrics:
          - name: Wer
            type: wer
            value: 23.795498749652683

Whisper Base French

This model is a fine-tuned version of qanastek/whisper-base-french-cased on the google/fleurs fr_fr dataset. It achieves the following results on the evaluation set:

Loss: 0.5402
Wer: 23.7955

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
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
0.3835	105.26	1000	0.5892	25.4237
0.2837	210.53	2000	0.5526	23.8955
0.2323	315.79	3000	0.5432	24.0122
0.1961	421.05	4000	0.5402	23.7955
0.1863	526.32	5000	0.5395	23.7955

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.16.2.dev0
Tokenizers 0.15.0