Update README.md

README.md

@@ -11,8 +11,16 @@ tags:
 - 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.
+
+> **Wav2Vec2**: Self-Supervised Learning for Speech Recognition
+> [https://arxiv.org/pdf/2006.11477](https://arxiv.org/pdf/2006.11477)
+
 ```py
 Classification Report:
 
@@ -29,3 +37,90 @@ weighted avg     0.9848    0.9846    0.9846      6545
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/pBnrVbG8uyZYq6Nb4GOuG.png)
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/QtZdfaXE-W-C4QDZUWuVC.png)
+
+---
+
+## Label Space: 2 Classes
+
+```
+Class 0: female  
+Class 1: male
+```
+
+---
+
+## Install Dependencies
+
+```py
+%%capture
+!pip install -q 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()
+```
+
+---
+
+## 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.