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---
language: fr
license: apache-2.0
tags:
- generated_from_trainer
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
- wer_norm
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: fr
split: test
args: fr
metrics:
- name: Wer
type: wer
value: 11.1406
- name: Wer (without normalization)
type: wer_without_norm
value: 15.89689189275029
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# French Medium Whisper
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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!](https://medium.com/@pierre_guillou/speech-to-text-ia-transcreva-qualquer-%C3%A1udio-para-o-portugu%C3%AAs-com-o-whisper-openai-sem-ad0c17384681).
## New SOTA
The Normalized WER in the [OpenAI Whisper article](https://cdn.openai.com/papers/whisper.pdf) with the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0) test dataset is 16.0.
As this test dataset is similar to the [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/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](https://huggingface.co/openai/whisper-medium) 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 |