metadata

license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - facebook/multilingual_librispeech
metrics:
  - wer
model-index:
  - name: Whisper largeV2 French MLS
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: facebook/multilingual_librispeech french
          type: facebook/multilingual_librispeech
          config: french
          split: test
          args: french
        metrics:
          - name: Wer
            type: wer
            value: 4.561620226935377

Whisper largeV2 French MLS

This model is a fine-tuned version of openai/whisper-large-v2 on the facebook/multilingual_librispeech french dataset. It achieves the following results on the evaluation set:

Loss: 0.0903
Wer: 4.5616

Model description

The model is fine-tuned for 4000 updates/steps on multilingual librispeech French train data.

Zero-shot - 7.3 (MLS French test)
Fine-tune MLS French train - 4.56 (MLS French test) (-37.5%)

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
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
0.1303	0.25	1000	0.1219	6.3618
0.0751	0.5	2000	0.1033	5.3905
0.0613	0.75	3000	0.0970	4.9193
0.0796	1.0	4000	0.0903	4.5616

Framework versions

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