metadata

language:
  - el
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Greek - Robust
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 el
          type: mozilla-foundation/common_voice_11_0
          config: el
          split: test
          args: el
        metrics:
          - type: wer
            value: 17.709881129271917
            name: Wer
          - type: wer
            value: 13.25
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: el_gr
          split: test
        metrics:
          - type: wer
            value: 39.59
            name: WER

Whisper Medium Greek - Robust

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set:

Loss: 0.2807
Wer: 17.7099

IMPORTANT The model has been trained using data augmentation to improve its generalization capabilities and robustness. The results on the eval set during training are biased towards data augmentation applied to evaluation data.

Results on eval set

Mozilla CV 11.0 - Greek: 13.250 WER (using official script)
Google Fluers - Greek: 39.59 WER (using official script)

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 20000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0407	4.69	2000	0.2484	20.8767
0.0128	9.39	4000	0.2795	21.2017
0.0041	14.08	6000	0.2744	19.1308
0.0017	18.78	8000	0.2759	17.9978
0.0005	23.47	10000	0.2751	18.5457
0.0015	28.17	12000	0.2928	19.2051
0.0004	32.86	14000	0.2819	18.2857
0.0002	37.56	16000	0.2831	17.7285
0.0007	42.25	18000	0.2776	17.8399
0.0	46.95	20000	0.2792	17.0970

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.7.1
Tokenizers 0.12.1