whisper-large-v3-sr / README.md
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---
language:
- sr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
base_model: openai/whisper-large-v3
model-index:
- name: Whisper Large v3 Sr - Slavko Djogic
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
args: 'Config: sr'
metrics:
- type: wer
value: 17.2694
name: Wer
---
<!-- 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-v3-sr
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3961
- Wer: 17.2694
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0498 | 4.81 | 1000 | 0.2004 | 20.1799 |
| 0.0042 | 9.62 | 2000 | 0.3225 | 18.2395 |
| 0.0001 | 14.42 | 3000 | 0.3799 | 17.2694 |
| 0.0001 | 19.23 | 4000 | 0.3961 | 17.2694 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1