---
language:
- el
tags:
- hf-asr-leaderboard, whisper-medium, mozilla-foundation/common_voice_11_0, greek,
  whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium El - Greek One
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Google FLEURS
      type: google/fleurs
      config: el_gr
      split: test
      args: el_gr
    metrics:
    - name: Wer
      type: wer
      value: 15.584586962259174
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Medium El - Greek One

This model is a fine-tuned version of [openai/medium-medium](https://huggingface.co/openai/medium-medium) on the Google FLEURS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2864
- Wer: 15.5846

## 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: 20
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0006        | 12.02 | 1000 | 0.2718          | 15.4394 |
| 0.0003        | 24.04 | 2000 | 0.2864          | 15.5846 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.14.0.dev20221206+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2