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.

Wav2Vec2: Self-Supervised Learning for Speech Recognition : https://arxiv.org/pdf/2006.11477

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

👁 download.png

👁 download (1).png

Label Space: 2 Classes

Class 0: female 
Class 1: male

Install Dependencies

pip install gradio transformers torch librosa hf_xet

Inference Code

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

male

👁 Screenshot 2025-05-31 at 20-19-39 Common Voice Gender Detection.png

female

👁 Screenshot 2025-05-31 at 20-21-57 Common Voice Gender Detection.png

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.