metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - generated_from_trainer
datasets:
  - google/fleurs
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Large Sr Combined
    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: 0.06233709817549957

Whisper Large v2 Sr Fleurs and CommonVoice

This model is a fine-tuned version of openai/whisper-large-v2 on the combined Google Fleurs and Mozilla Foundation Common Voice 13 dataset. It achieves the following results on the evaluation set:

Loss: 0.1749
Wer Ortho: 0.1678
Wer: 0.0623

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: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
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
0.0737	1.34	500	0.1735	0.1865	0.0908
0.0304	2.67	1000	0.1622	0.1670	0.0728
0.0156	4.01	1500	0.1749	0.1678	0.0623

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3