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---
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
---
<!-- 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 Greek - Robust
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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
|