French Medium Whisper

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: 0.2664
Wer (without normalization): 15.8969
Wer (with normalization): 11.1406

Blog post

All information about this model in this blog post: Speech-to-Text & IA | Transcreva qualquer áudio para o português com o Whisper (OpenAI)... sem nenhum custo!.

New SOTA

The Normalized WER in the OpenAI Whisper article with the Common Voice 9.0 test dataset is 16.0.

As this test dataset is similar to the Common Voice 11.0 test dataset used to evaluate our model (WER and WER Norm), it means that our French Medium Whisper is better than the Medium Whisper model at transcribing audios French in text.

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	Wer Norm
0.2695	0.2	1000	0.3080	17.8083	12.9791
0.2099	0.4	2000	0.2981	17.4792	12.4242
0.1978	0.6	3000	0.2864	16.7767	12.0913
0.1455	0.8	4000	0.2752	16.4597	11.8966
0.1712	1.0	5000	0.2664	15.8969	11.1406

Framework versions

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

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whisper-medium-french