metadata

language:
  - el
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: whisper-large-v2-greek
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: FLEURS
          type: google/fleurs
          config: el_gr
          split: test
          args: el_gr
        metrics:
          - name: Wer
            type: wer
            value: 0.17739223993006523

whisper-large-v2-greek

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

Loss: 0.2734
Wer Ortho: 0.2102
Wer: 0.1774

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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.1809	1.0	274	0.2244	0.2261	0.1947
0.0977	2.0	549	0.2306	0.2204	0.1856
0.0594	3.0	824	0.2332	0.2137	0.1814
0.0454	4.0	1099	0.2667	0.2315	0.1985
0.028	5.0	1374	0.2579	0.2151	0.1822
0.022	6.0	1649	0.2674	0.2188	0.1863
0.0202	6.98	1918	0.2734	0.2102	0.1774

Framework versions

Transformers 4.30.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3