metadata
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- fsicoli/common_voice_18_0
metrics:
- wer
model-index:
- name: Whisper Turbo Train
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 18.0
type: fsicoli/common_voice_18_0
split: None
metrics:
- name: Wer
type: wer
value: 15.246076710047603
Whisper Turbo Train
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Common Voice 18.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1156
- Wer: 15.2461
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3715 | 0.4257 | 1000 | 0.3457 | 40.4692 |
| 0.251 | 0.8514 | 2000 | 0.2181 | 27.7065 |
| 0.1569 | 1.2771 | 3000 | 0.1814 | 24.1533 |
| 0.1436 | 1.7029 | 4000 | 0.1531 | 20.3812 |
| 0.0931 | 2.1286 | 5000 | 0.1374 | 18.4662 |
| 0.0891 | 2.5543 | 6000 | 0.1252 | 16.9349 |
| 0.0738 | 2.9800 | 7000 | 0.1199 | 15.5610 |
| 0.0544 | 3.4057 | 8000 | 0.1156 | 15.2461 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.1.0
- Datasets 3.0.1
- Tokenizers 0.20.0