metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: whisper-base-finetuned-500v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 83.78378378378379
whisper-base-finetuned-500v2
This model is a fine-tuned version of openai/whisper-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0286
- Wer Ortho: 83.7838
- Wer: 83.7838
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0004 | 33.3333 | 100 | 1.0012 | 81.0811 | 81.0811 |
| 0.0001 | 66.6667 | 200 | 1.0110 | 78.3784 | 78.3784 |
| 0.0001 | 100.0 | 300 | 1.0186 | 78.3784 | 78.3784 |
| 0.0 | 133.3333 | 400 | 1.0241 | 81.0811 | 81.0811 |
| 0.0 | 166.6667 | 500 | 1.0286 | 83.7838 | 83.7838 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1