metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-small-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: test
args: fa
metrics:
- name: Wer
type: wer
value: 35.497333642476235
whisper-small-fa
This model is a fine-tuned version of openai/whisper-small on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9258
- Wer: 35.4973
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0193 | 8.1103 | 20000 | 0.5349 | 36.7125 |
| 0.0046 | 16.2206 | 40000 | 0.6839 | 36.0033 |
| 0.0018 | 24.3309 | 60000 | 0.7936 | 36.2543 |
| 0.0003 | 32.4412 | 80000 | 0.8729 | 35.9406 |
| 0.0 | 40.5515 | 100000 | 0.9258 | 35.4973 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0