---
language:
- sr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
datasets:
- google/fleurs
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Large Sr Combined
  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: 0.06233709817549957
---

<!-- 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 Large v2 Sr Fleurs and CommonVoice

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the combined Google Fleurs and Mozilla Foundation Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1749
- Wer Ortho: 0.1678
- Wer: 0.0623

## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0737        | 1.34  | 500  | 0.1735          | 0.1865    | 0.0908 |
| 0.0304        | 2.67  | 1000 | 0.1622          | 0.1670    | 0.0728 |
| 0.0156        | 4.01  | 1500 | 0.1749          | 0.1678    | 0.0623 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3