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 

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Label Space: 2 Classes

Class 0: female Class 1: male 

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Install Dependencies

pip install gradio transformers torch librosa hf_xet 

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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() 

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Demo Inference

male

female

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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.