metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- lyhourt/clean_6
metrics:
- wer
model-index:
- name: whisper-small-clean_6-v4
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: lyhourt/clean_6
type: lyhourt/clean_6
metrics:
- name: Wer
type: wer
value: 24.014921893215202
whisper-small-clean_6-v4
This model is a fine-tuned version of openai/whisper-small on the lyhourt/clean_6 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2886
- Wer: 24.0149
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: 64
- 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: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1207 | 0.5 | 200 | 0.3080 | 25.4138 |
| 0.1827 | 1.0 | 400 | 0.2953 | 24.7144 |
| 0.0907 | 1.1342 | 600 | 0.2921 | 24.3413 |
| 0.0904 | 1.5123 | 800 | 0.2900 | 24.3064 |
| 0.0823 | 1.8904 | 1000 | 0.2886 | 24.0149 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1