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---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-sm-el-intlv-xl
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: 19.48365527488856
---
# whisper-sm-el-intlv-xl
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 (el) and the google/fleurs (el_gr) datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4725
- Wer: 19.4837
## Model description
The model was trained over 10000 steps on translation from Greek to English.
## Intended uses & limitations
This model was part of the Whisper Finetuning Event (Dec 2022) and was used primarily to compare relative improvements between transcription and translation tasks.
## Training and evaluation data
The training datasets combined examples from both train and evaluation splits and use the train split of the mozilla-foundation/common_voice_11_0 (el) dataset for evaluation and selection of the best checkpoint.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8.5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0545 | 2.49 | 1000 | 0.2891 | 22.4926 |
| 0.0093 | 4.98 | 2000 | 0.3927 | 20.1337 |
| 0.0018 | 7.46 | 3000 | 0.4031 | 20.1616 |
| 0.001 | 9.95 | 4000 | 0.4209 | 19.6880 |
| 0.0008 | 12.44 | 5000 | 0.4498 | 20.0966 |
| 0.0005 | 14.93 | 6000 | 0.4725 | 19.4837 |
| 0.0002 | 17.41 | 7000 | 0.4917 | 19.5951 |
| 0.0001 | 19.9 | 8000 | 0.5050 | 19.6230 |
| 0.0001 | 22.39 | 9000 | 0.5146 | 19.5672 |
| 0.0001 | 24.88 | 10000 | 0.5186 | 19.4837 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.12.1
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