metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-tiny-fa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: fa
split: None
args: fa
metrics:
- name: Wer
type: wer
value: 51.8555393407073
whisper-tiny-fa
This model is a fine-tuned version of openai/whisper-tiny on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5542
- Wer: 51.8555
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5002 | 0.8110 | 1000 | 0.7493 | 67.9014 |
| 0.3239 | 1.6221 | 2000 | 0.6166 | 58.6680 |
| 0.2198 | 2.4331 | 3000 | 0.5782 | 54.3310 |
| 0.1695 | 3.2441 | 4000 | 0.5619 | 52.7925 |
| 0.1309 | 4.0552 | 5000 | 0.5542 | 51.8555 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1