metadata

language:
  - fr
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Base French
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 fr
          type: mozilla-foundation/common_voice_11_0
          config: fr
          split: test
          args: fr
        metrics:
          - name: Wer
            type: wer
            value: 24.064827553489256
      - 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: 24.2
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: facebook/voxpopuli fr
          type: facebook/voxpopuli
          config: fr
          split: test
          args: fr
        metrics:
          - name: Wer
            type: wer
            value: 23.66

Whisper Base French

This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set:

Loss: 0.4968
Wer on mozilla-foundation/common_voice_11_0 fr: 24.0648
Wer on google/fleurs fr_fr: 24.20
Wer on facebook/voxpopuli fr: 23.66

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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.534	0.2	1000	0.5710	27.4408
0.4409	1.2	2000	0.5279	25.1981
0.3095	2.2	3000	0.5117	25.0818
0.3285	3.2	4000	0.4995	24.0601
0.3032	4.2	5000	0.4968	24.0648

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.7.1.dev0
Tokenizers 0.13.2