metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- neddamj/patois-sr-base
metrics:
- wer
model-index:
- name: Whisper base - Patois
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Patois Music Transcription
type: neddamj/patois-sr-base
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 0.5878805645127518
Whisper base - Patois
This model is a fine-tuned version of openai/whisper-base on the Patois Music Transcription dataset. It achieves the following results on the evaluation set:
- Loss: 1.0740
- Wer: 0.5879
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 OptimizerNames.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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.3852 | 1.3746 | 400 | 1.3994 | 0.8946 |
| 0.9297 | 2.7491 | 800 | 1.1046 | 0.7298 |
| 0.5738 | 4.1237 | 1200 | 1.0175 | 0.6647 |
| 0.4621 | 5.4983 | 1600 | 0.9841 | 0.6448 |
| 0.3866 | 6.8729 | 2000 | 0.9733 | 0.5982 |
| 0.2647 | 8.2474 | 2400 | 0.9925 | 0.6296 |
| 0.2166 | 9.6220 | 2800 | 1.0156 | 0.6006 |
| 0.185 | 10.9966 | 3200 | 1.0366 | 0.5932 |
| 0.1521 | 12.3711 | 3600 | 1.0650 | 0.5983 |
| 0.1168 | 13.7457 | 4000 | 1.0740 | 0.5879 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1