---
language:
- el
license: apache-2.0
tags:
- hf-asr-leaderboard
- whisper-large
- mozilla-foundation/common_voice_11_0
- greek
- whisper-event
- generated_from_trainer
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-lg-el-intlv-xs
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: el
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 9.8997
---

# whisper-lg-el-intlv-xs

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co./openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2913
- Wer: 9.8997

## 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: 3.5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0311        | 2.49  | 1000  | 0.1809          | 10.5498 |
| 0.0074        | 4.98  | 2000  | 0.2470          | 10.2805 |
| 0.0019        | 7.46  | 3000  | 0.3008          | 10.0297 |
| 0.0011        | 9.95  | 4000  | 0.2913          | 9.8997  |
| 0.0009        | 12.44 | 5000  | 0.3092          | 10.1876 |
| 0.0005        | 14.93 | 6000  | 0.3495          | 10.1969 |
| 0.0002        | 17.41 | 7000  | 0.3659          | 10.2526 |
| 0.0001        | 19.9  | 8000  | 0.3846          | 10.2619 |
| 0.0001        | 22.39 | 9000  | 0.3941          | 10.2897 |
| 0.0001        | 24.88 | 10000 | 0.3990          | 10.3269 |


### Framework versions

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