README.md

---
language:
- fr
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: deepdml/whisper-medium-mix-fr
  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: 11.227820307400155
    - name: Cer
      type: cer
      value: 4.2141
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: FLEURS ASR
      type: google/fleurs
      config: fr_fr
      split: test
      args: fr
    metrics:
    - name: WER
      type: wer
      value: 9.3526
    - name: Cer
      type: cer
      value: 4.144
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: french
      split: test
      args:
        language: fr
    metrics:
    - name: WER
      type: wer
      value: 6.3468
    - name: Cer
      type: cer
      value: 3.1561
  - task:
      type: Automatic Speech Recognition
      name: speech-recognition
    dataset:
      name: VoxPopuli
      type: facebook/voxpopuli
      config: fr
      split: test
      args:
        language: fr
    metrics:
    - name: WER
      type: wer
      value: 10.0653
    - name: Cer
      type: cer
      value: 6.5456
---

<!-- 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. -->

# deepdml/whisper-medium-mix-fr

This model is a fine-tuned version of [deepdml/whisper-medium-mix-fr](https://huggingface.co/deepdml/whisper-medium-mix-fr) on the mozilla-foundation/common_voice_11_0, google/fleurs, facebook/multilingual_librispeech and facebook/voxpopuli datasets.
It achieves the following results on the evaluation set:
- Loss: 0.2599
- Wer: 11.2278

Using the [evalutaion script](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_eval_whisper_streaming.py) provided in the Whisper Sprint the model achieves these results on the test sets (WER):

- **google/fleurs: 9.3526 %**  
(python run_eval_whisper_streaming.py --model_id="deepdml/whisper-medium-mix-fr" --dataset="google/fleurs" --config="fr_fr" --device=0 --language="fr")
- **facebook/multilingual_librispeech: 6.3468 %**  
(python run_eval_whisper_streaming.py --model_id="deepdml/whisper-medium-mix-fr" --dataset="facebook/multilingual_librispeech" --config="french" --device=0 --language="fr")
- **facebook/voxpopuli: 10.0653 %**  
(python run_eval_whisper_streaming.py --model_id="deepdml/whisper-medium-mix-fr" --dataset="facebook/voxpopuli" --config="fr" --device=0 --language="fr")

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Training data used:
- **mozilla-foundation/common_voice_11_0:** fr, train+validation
- **google/fleurs:** fr_fr, train
- **facebook/multilingual_librispeech:** french, train
- **facebook/voxpopuli:** fr, train
- 
Evaluating over test split from mozilla-foundation/common_voice_11_0 dataset.

## 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0855        | 0.25  | 1000 | 0.2826          | 12.4230 |
| 0.0569        | 0.5   | 2000 | 0.2768          | 11.9577 |
| 0.0724        | 0.75  | 3000 | 0.2670          | 11.6106 |
| 0.069         | 1.0   | 4000 | 0.2599          | 11.2278 |


### Framework versions

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