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---
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- MatthiasZ/whisper_large_v3_turbo_annota_2
metrics:
- wer
model-index:
- name: whisper_large_v3_turbo_annota_2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: whisper_large_v3_turbo_annota_2
type: MatthiasZ/whisper_large_v3_turbo_annota_2
args: 'config: de, split: test'
metrics:
- name: Wer
type: wer
value: 21.886674395921897
---
<!-- 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_turbo_annota_2
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the whisper_large_v3_turbo_annota_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3910
- Wer: 21.8867
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4409 | 0.3333 | 2000 | 0.4489 | 23.5761 |
| 0.4317 | 0.6667 | 4000 | 0.4141 | 22.9669 |
| 0.3881 | 1.0 | 6000 | 0.3910 | 21.8867 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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