metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- csdata
metrics:
- wer
model-index:
- name: whisper-small-cs
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: csdata
type: csdata
config: default
split: None
args: default
metrics:
- type: wer
value: 44.76744186046512
name: Wer
whisper-small-cs
This model is a fine-tuned version of openai/whisper-small on the csdata dataset. It achieves the following results on the evaluation set:
- Loss: 0.0010
- Wer: 44.7674
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: 10
- training_steps: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 10.0 | 10 | 2.9041 | 96.5116 |
| No log | 20.0 | 20 | 0.2452 | 109.3023 |
| 2.545 | 30.0 | 30 | 0.0064 | 53.4884 |
| 2.545 | 40.0 | 40 | 0.0015 | 44.7674 |
| 0.0053 | 50.0 | 50 | 0.0010 | 44.7674 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0