README.md

metadata

language:
  - pl
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
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: 7.280175959972464
            name: WER
          - type: wer
            value: 7.31
            name: WER
          - type: wer_without_norm
            value: 20.18
            name: WER unnormalized
          - type: cer
            value: 2.08
            name: CER
          - type: mer
            value: 7.27
            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: 9.61
            name: WER
          - type: wer_without_norm
            value: 30.33
            name: WER unnormalized
          - type: cer
            value: 5.5
            name: CER
          - type: mer
            value: 9.45
            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: 8.68
            name: WER
          - type: wer_without_norm
            value: 29.33
            name: WER unnormalized
          - type: cer
            value: 3.63
            name: CER
          - type: mer
            value: 8.62
            name: MER

Whisper Large v2 PL

This model is a fine-tuned version of bardsai/whisper-large-v2-pl on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set:

• Loss: 0.3684
• Wer: 7.2802

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: 2100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0047	1.35	700	0.3428	8.5562
0.0011	2.7	1400	0.3605	7.5505
0.0003	4.05	2100	0.3684	7.2802

Framework versions

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