README.md

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: 6.89
            name: WER
          - type: wer_without_norm
            value: 19.79
            name: WER unnormalized
          - type: cer
            value: 1.88
            name: CER
          - type: mer
            value: 6.84
            name: MER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: facebook/voxpopuli
          type: facebook/voxpopuli
          config: pl
          split: test
        metrics:
          - type: wer
            value: 9.26
            name: WER
          - type: wer_without_norm
            value: 30.25
            name: WER unnormalized
          - type: cer
            value: 5.32
            name: CER
          - type: mer
            value: 9.1
            name: MER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: pl_pl
          split: test
        metrics:
          - type: wer
            value: 9.88
            name: WER
          - type: wer_without_norm
            value: 29.53
            name: WER unnormalized
          - type: cer
            value: 5.09
            name: CER
          - type: mer
            value: 9.73
            name: MER

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