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---
license: apache-2.0
language:
- en
base_model:
- facebook/wav2vec2-base-960h
pipeline_tag: audio-classification
library_name: transformers
tags:
- voice-gender-detection
- male
- female
- biology
- SFT
---
# Common-Voice-Gender-Detection
> **Common-Voice-Gender-Detection** is a fine-tuned version of `facebook/wav2vec2-base-960h` for **binary audio classification**, specifically trained 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](https://arxiv.org/pdf/2006.11477)
```py
Classification Report:
precision recall f1-score support
female 0.9705 0.9916 0.9809 2622
male 0.9943 0.9799 0.9870 3923
accuracy 0.9846 6545
macro avg 0.9824 0.9857 0.9840 6545
weighted avg 0.9848 0.9846 0.9846 6545
```
---
## Label Space: 2 Classes
```
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-Geneder-Detection"
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",
description="Upload an audio clip to classify the speaker's gender as female or male."
)
if __name__ == "__main__":
iface.launch()
```
---
## Demo Inference
> [!note]
male
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/7woMf3_bgX_D99-1Uy3jH.mpga"></audio>
> [!note]
female
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/0d2rDf_DT-gjRWBwiPbm_.mpga"></audio>
---
## Intended Use
`Common-Voice-Gender-Detection` 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. |