---
language:
- sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
- google/fleurs
- classla/ParlaSpeech-RS
- Sagicc/audio-lmb-ds
metrics:
- wer
model-index:
- name: Whisper Medium v3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 13
      type: mozilla-foundation/common_voice_16_0
      config: sr
      split: test
      args: sr
    metrics:
    - name: Wer
      type: wer
      value: 0.07912398445778877
---

<!-- 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 v3

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1501
- Wer Ortho: 0.1759
- Wer: 0.0791

## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.2054        | 0.03  | 500  | 0.2392          | 0.2715    | 0.1484 |
| 0.1782        | 0.05  | 1000 | 0.2056          | 0.2411    | 0.1155 |
| 0.1736        | 0.08  | 1500 | 0.1768          | 0.1990    | 0.0994 |
| 0.1662        | 0.11  | 2000 | 0.1677          | 0.1925    | 0.0940 |
| 0.1409        | 0.13  | 2500 | 0.1589          | 0.1891    | 0.0860 |
| 0.1346        | 0.16  | 3000 | 0.1565          | 0.1897    | 0.0881 |
| 0.1263        | 0.19  | 3500 | 0.1523          | 0.1805    | 0.0819 |
| 0.137         | 0.22  | 4000 | 0.1501          | 0.1759    | 0.0791 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1