metadata

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
      - 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

deepdml/whisper-medium-mix-fr

This model is a fine-tuned version of 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 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