metadata
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-16e-c
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: en
split: test
args: en
metrics:
- type: wer
value: 60.342956370740175
name: Wer
whisper-large-v3-turbo-16e-c
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the common_voice_16_1 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2775
- Wer: 60.3430
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: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0 | 0 | 7.6611 | 130.4102 |
| 1.3625 | 0.1 | 500 | 2.0087 | 73.6922 |
| 0.9901 | 0.2 | 1000 | 1.4311 | 58.1072 |
| 0.7534 | 0.3 | 1500 | 1.3291 | 55.8715 |
| 0.8052 | 0.4 | 2000 | 1.3063 | 50.5318 |
| 0.7024 | 0.5 | 2500 | 1.2896 | 46.6464 |
| 0.926 | 0.6 | 3000 | 1.2833 | 59.4747 |
| 0.6204 | 0.7 | 3500 | 1.2788 | 49.9674 |
| 0.7267 | 0.8 | 4000 | 1.2791 | 58.9755 |
| 0.8442 | 0.9 | 4500 | 1.2775 | 50.2930 |
| 0.9145 | 1.0 | 5000 | 1.2775 | 60.3430 |
Framework versions
- Transformers 4.54.0
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.6.0
- Tokenizers 0.21.2