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library_name: transformers
license: apache-2.0
language:
- fa
base_model: openai/whisper-small
tags:
- generated_from_trainer
- automatic-speech-recognition
- whisper
- persian
- speech
- ASR
- common voice
- emotion-recognition
datasets:
- aliyzd95/common_voice_21_0_fa
metrics:
- wer
model-index:
- name: Whisper Small Pesrian V1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 21.0
type: aliyzd95/common_voice_21_0_fa
config: fa
split: None
args: 'split: test'
metrics:
- name: Wer
type: wer
value: 31.930087051142547
---
# Whisper Small Persian
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 21.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3323
- Wer: 31.9300
## 🧠 Model Details
- Base model: `openai/whisper-small`
- Fine-tuned on:
- Common Voice 21 (Persian subset)
- Language: Persian (fa)
## 🧪 Evaluation
| Metric | Value |
|--------|-------|
| WER | `31.93%`
## 📦 Usage
```python
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="aliyzd95/whisper-small-persian-v1")
result = pipe("your-audio.wav")
print(result["text"])
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.7.1+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1 |