metadata
library_name: transformers
language:
- en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- ntnu-smil/lttc-augmented-ft-1
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-augmented
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: ntnu-smil/lttc-augmented-ft-1
type: ntnu-smil/lttc-augmented-ft-1
metrics:
- type: wer
value: 32.36001374098248
name: Wer
whisper-large-v3-turbo-augmented
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the ntnu-smil/lttc-augmented-ft-1 dataset. It achieves the following results on the evaluation set:
- Loss: 1.3566
- Wer: 32.3600
- Cer: 18.4747
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: 0.0005
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.0483 | 1.0 | 190 | 1.2801 | 35.8640 | 20.7045 |
| 0.0503 | 2.0 | 380 | 1.3510 | 32.5318 | 20.3283 |
| 0.0033 | 3.0 | 570 | 1.2776 | 39.3336 | 22.9891 |
| 0.0007 | 4.0 | 760 | 1.3057 | 32.6692 | 18.6594 |
| 0.0002 | 5.0 | 950 | 1.3566 | 32.3600 | 18.4747 |
Framework versions
- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.2.0+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0