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---
language:
- sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- google/fleurs
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Medium Sr Fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 0.07884448305821025
---
<!-- 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 Sr Fleurs
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on combined Google Fleurs and Mozilla Common Volice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1947
- Wer Ortho: 0.1874
- Wer: 0.0788
## 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: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.072 | 1.34 | 500 | 0.1769 | 0.1896 | 0.0912 |
| 0.0223 | 2.67 | 1000 | 0.1774 | 0.1993 | 0.0832 |
| 0.0101 | 4.01 | 1500 | 0.1947 | 0.1874 | 0.0788 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3