---
license: mit
datasets:
- mozilla-foundation/common_voice_17_0
language:
- fa
metrics:
- wer
- cer
base_model:
- openai/whisper-large-v3
tags:
- ASR
- Whisper
- Common_Voice
- Farsi
- Persian
- automatic-speech-recognition
library_name: transformers
pipeline_tag: automatic-speech-recognition
widget:
  - src: example.wav
    example_title: "Sample"
---


Whisper Large v3 has been fine-tuned on Common Voice 17, leveraging over 250,000 Persian audio samples—a significant improvement over earlier models trained on Common Voice 11, which contained only 83,000 samples. This larger dataset has resulted in a lower Word Error Rate (WER), enhancing the model’s accuracy and robustness in recognizing Persian speech.

This update marks a major step forward in Persian ASR, and we hope it benefits the Persian-speaking community, making high-quality speech recognition more accessible and reliable. 🚀


| Feature            | Description |
|-------------------|-------------|
| **Model Name**     | Whisper Large v3 - Persian (Common Voice 17) |
| **Base Model**     | Whisper Large v3 |
| **Language**       | Persian (Farsi) |
| **Dataset**        | Mozilla Common Voice 17 (Persian subset) |
| **Hardware Used**  | NVIDIA A100 GPU |
| **Batch Size**     | 16 |
| **Training Steps** | 5000 |
| **WER (Word Error Rate)** | **21.43** |


# How to Use

```python
from transformers import pipeline



asr_pipe = pipeline(
    "automatic-speech-recognition",
    model="msghol/whisper-large-v3-persian-common-voice-17",
    return_timestamps=False
)

text = asr_pipe("your_file")["text"]
print(text)

```

```bibtex
@misc{whisper_persian_cv17,
  author = {msghol},
  title = {Whisper Large v3 - Persian (Common Voice 17)},
  year = {2025},
  url = {https://huggingface.co/msghol/whisper-large-v3-persian-common-voice-17}
}
```