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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 small Greek Farsipal and El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr
type: mozilla-foundation/common_voice_11_0,google/fleurs
config: el
split: None
metrics:
- name: Wer
type: wer
value: 16.493313521545318
---
<!-- 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 small Greek Farsipal and El Greco
This model is a fine-tuned version of [emilios/whisper-sm-farsipal-e5](https://huggingface.co/emilios/whisper-sm-farsipal-e5) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5015
- Wer: 16.4933
## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0004 | 2.49 | 1000 | 0.4797 | 16.7348 |
| 0.0003 | 4.98 | 2000 | 0.4895 | 16.5397 |
| 0.0002 | 7.46 | 3000 | 0.4963 | 16.5119 |
| 0.0002 | 9.95 | 4000 | 0.5015 | 16.4933 |
| 0.0002 | 12.44 | 5000 | 0.5034 | 16.5676 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|