# 🎯 FAAS-Metric

**FAAS (Fairness Adjusted ASR Score)** is a fairness-aware evaluation metric for Automatic Speech Recognition (ASR) systems. It combines traditional ASR accuracy (like WER) with fairness evaluations across demographic groups to provide a holistic performance measure.

---

## 📦 Installation

```bash
pip install faas-metric
```

---

## 🚀 Usage

### ▶️ Python API

```python
from faas_metric import compute_faas

# Example input
wer_list = [12.5, 15.0, 9.0]
speaker_metadata = [
    {"gender": "male", "first_language": "Hindi"},
    {"gender": "female", "first_language": "Tamil"},
    {"gender": "male", "first_language": "Bengali"}
]

# Compute FAAS score
faas = compute_faas(wer_list, speaker_metadata)
print(f"FAAS Score: {faas}")
```

---

### 🖥️ Command Line Interface (CLI)

```bash
faas --input path/to/data.csv --output path/to/result.json
```

#### 📄 Input CSV Format

Your CSV should include:
- A `wer` column (Word Error Rate)
- Any number of fairness-related attributes like `gender`, `first_language`, `ethnicity`, etc.

**Example:**

```csv
wer,gender,first_language
12.5,male,Hindi
15.0,female,Tamil
9.0,male,Bengali
```

#### ✅ Output

A JSON file (if `--output` is specified), or the FAAS score printed to the terminal:

```json
{
  "faas_score": 88.0
}
```

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## 📚 Citation

If you use this work in your research, please cite:

```
@inproceedings{rai2025asrfairbench,
  title={ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems},
  author={Rai, Anand and Rahangdale, Satyam and Anand, Utkarsh and Mukherjee, Animesh},
  booktitle={Proceedings of Interspeech},
  year={2025}
}
```

---

## 🛠️ Development

To contribute:

```bash
git clone https://github.com/yourusername/faas-metric
cd faas-metric
pip install -e .
```

---

## 📬 Contact

For questions or suggestions, please open an [issue](https://github.com/yourusername/faas-metric/issues) or reach out via [email](mailto:your.email@example.com).

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## 📄 License

This project is licensed under the MIT License.
