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

Loss: 0.0910
Wer: 3.4678

Model description

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

Zero-shot - 4.2 (MLS Spanish test)
Fine-tune MLS spanish train - 3.46 (MLS Spanish test) (-17.61%)

Zero-shot - 6.3 (CV11 test)
Fine-tune MLS spanish train - 8.38 (CV11 test)

When the model is fine-tuned on specific dataset, the model loose its ability to generalise across datasets. Here the model is fine-tuned on MLS Spanish and evaluated on CV11 Spanish test. We can observe the drop in performance on CV11 test data.

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: 32
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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
0.2176	0.25	1000	0.1200	5.3932
0.1845	0.5	2000	0.1055	4.2
0.4516	0.75	3000	0.0977	3.6768
0.1549	1.14	4000	0.0910	3.4678

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-es

Whisper largeV2 Spanish MLS