metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- ntnu-smil/sandi2025-ds
metrics:
- wer
model-index:
- name: whisper-large-v3-sandi-7k-64-448steps
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: ntnu-smil/sandi2025-ds
type: ntnu-smil/sandi2025-ds
metrics:
- type: wer
value: 24.09465733000756
name: Wer
whisper-large-v3-sandi-7k-64-448steps
This model is a fine-tuned version of openai/whisper-large-v3 on the ntnu-smil/sandi2025-ds dataset. It achieves the following results on the evaluation set:
- Loss: 0.5722
- Wer: 24.0947
- Cer: 56.2387
- Decode Runtime: 203.1841
- Wer Runtime: 0.1735
- Cer Runtime: 0.3276
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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 448
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Runtime | Wer Runtime | Cer Runtime |
|---|---|---|---|---|---|---|---|---|
| 0.6419 | 1.0223 | 112 | 0.6663 | 20.0083 | 24.8032 | 187.4701 | 0.1653 | 0.2986 |
| 0.6651 | 2.0446 | 224 | 0.6117 | 20.0564 | 34.0018 | 189.8527 | 0.1717 | 0.3134 |
| 0.4682 | 3.0670 | 336 | 0.5826 | 21.0683 | 32.6385 | 190.6981 | 0.1750 | 0.3082 |
| 0.8059 | 4.0893 | 448 | 0.5722 | 24.0947 | 56.2387 | 203.1841 | 0.1735 | 0.3276 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1