Common-Voice-Gender-Detection EU AI Act Compliance Profile

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

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

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)

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Library

Base Model