---
license: apache-2.0
language:
- en
base_model:
- facebook/wav2vec2-base-960h
pipeline_tag: audio-classification
library_name: transformers
tags:
- en-IN
- voice-gender-detection
- male
- female
- SFT
---

![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YB_vNyYii-CnkSTNB3eSS.png)

# Common-Voice-Gender-Detection-IND-En | India (English)

> **Common-Voice-Gender-Detection-IND-En** is a fine-tuned version of `facebook/wav2vec2-base-960h` for **binary audio classification**, specifically trained on **English (IND-En) speech** to detect speaker gender as **female** or **male**. This model leverages the `Wav2Vec2ForSequenceClassification` architecture for efficient and accurate voice-based gender classification.

> [!note]
> Wav2Vec2: Self-Supervised Learning for Speech Recognition: https://arxiv.org/pdf/2006.11477

```py
Classification Report:

              precision    recall  f1-score   support

      Female     0.9986    0.9913    0.9950      3579
        Male     0.9900    0.9984    0.9942      3077

    accuracy                         0.9946      6656
   macro avg     0.9943    0.9949    0.9946      6656
weighted avg     0.9946    0.9946    0.9946      6656
```

![download-1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YYInmd59bFeJhgnJLE2qV.png)
![download-2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/zxi8M3HeToG9IBuKHVkR0.png)

---

## Label Space: 2 Classes

```text
Class 0: female
Class 1: male
```

---

## Install Dependencies

```bash
pip install gradio transformers torch librosa hf_xet
```

---

## Inference Code

```python
import gradio as gr
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
import torch
import librosa

# Load model and processor
model_name = "prithivMLmods/Common-Voice-Gender-Detection-IND-En"
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
processor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)

# Label mapping
id2label = {
    "0": "female",
    "1": "male"
}

def classify_audio(audio_path):
    # Load and resample audio to 16kHz
    speech, sample_rate = librosa.load(audio_path, sr=16000)

    # Process audio
    inputs = processor(
        speech,
        sampling_rate=sample_rate,
        return_tensors="pt",
        padding=True
    )

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {
        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
    }

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_audio,
    inputs=gr.Audio(type="filepath", label="Upload Audio (WAV, MP3, etc.)"),
    outputs=gr.Label(num_top_classes=2, label="Gender Classification"),
    title="Common Voice Gender Detection - IND-En",
    description="Upload an English (IND-En) speech clip to classify the speaker's gender as female or male."
)

if __name__ == "__main__":
    iface.launch()
```

---

## Demo Inference

![Gender](https://img.shields.io/badge/Gender-Male-blue?style=plastic)

<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-P5gimu8tRiv82o_iHPUN.wav"></audio>

![Screenshot 2026-09-02 120946](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/RUaAfQQ7DLRz4me1Nm6qW.png)

![Gender](https://img.shields.io/badge/Gender-Female-ff69b4?style=plastic)

<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/_9ieMr5DpVN730QTD4hyf.wav"></audio>

![gradio.live](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/RS61W8XdzZerkFqAnMhG5.png)

---

## Intended Use

`Common-Voice-Gender-Detection-IND-En` is designed for:

* **Speech Analytics** – Assist in analyzing speaker demographics in call centers or customer service recordings.
* **Conversational AI Personalization** – Adjust tone or dialogue based on gender detection for more personalized voice assistants.
* **Voice Dataset Curation** – Automatically tag or filter voice datasets by speaker gender for better dataset management.
* **Research Applications** – Enable linguistic and acoustic research involving gender-specific speech patterns.
* **Multimedia Content Tagging** – Automate metadata generation for gender identification in podcasts, interviews, or video content.