---
language:
- el
license: apache-2.0
tags:
- hf-asr-leaderboard
- whisper-medium
- 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
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium El Greco
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: el
      split: test
    metrics:
    - type: wer
      value: 10.7448
      name: Wer
---


<!-- 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 Greco

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4245
- eval_wer: 10.7448
- eval_runtime: 1107.1212
- eval_samples_per_second: 1.532
- eval_steps_per_second: 0.096
- epoch: 33.98
- step: 7000

## 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: 32
- eval_batch_size: 16
- 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: 7000
- mixed_precision_training: Native AMP

### Framework versions

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