metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - google/fleurs
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Sr Fleurs
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Google Fleurs
          type: google/fleurs
          config: sr
          split: test
          args: sr
        metrics:
          - name: Wer
            type: wer
            value: 0.07884448305821025

Whisper Medium Sr Fleurs

This model is a fine-tuned version of openai/whisper-medium on combined Google Fleurs and Mozilla Common Volice 13 dataset. It achieves the following results on the evaluation set:

Loss: 0.1947
Wer Ortho: 0.1874
Wer: 0.0788

Model description

More information needed

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: 4
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 1500

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.072	1.34	500	0.1769	0.1896	0.0912
0.0223	2.67	1000	0.1774	0.1993	0.0832
0.0101	4.01	1500	0.1947	0.1874	0.0788

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3