metadata

language:
  - pl
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Large v2 PL
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: pl
          split: test
          args: pl
        metrics:
          - type: wer
            value: 6.912473345757989
            name: Wer
          - type: wer
            value: 6.89
            name: WER

Whisper Large v2 PL

This model is a fine-tuned version of openai/whisper-large-v2 on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.4222
Wer: 6.9125

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: 8
eval_batch_size: 4
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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1144	1.93	500	0.2016	7.4749
0.0441	3.86	1000	0.2193	7.3154
0.0099	5.79	1500	0.2983	7.0804
0.0048	7.72	2000	0.3514	7.0988
0.0017	9.65	2500	0.3614	7.0485
0.0014	11.58	3000	0.3814	7.1240
0.001	13.51	3500	0.3773	6.9931
0.0005	15.44	4000	0.4085	6.9662
0.0004	17.37	4500	0.4195	6.9192
0.0004	19.3	5000	0.4222	6.9125

Framework versions

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