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

sgangireddy/whisper-largev2-mls-fr

Whisper largeV2 French MLS