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---
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 |