Create app.py

app.py

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+import os
+import torch
+import torchaudio
+import gradio as gr
+import numpy as np
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+import torchaudio.transforms as transforms
+
+
+MODEL_NAME = "alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)
+model = AutoModelForAudioClassification.from_pretrained(MODEL_NAME).to(device)
+
+label2id = {"female": 0, "male": 1}
+id2label = {0: "Female", 1: "Male"}
+
+
+def preprocess_audio(audio):
+    """Convert stereo to mono, normalize, resample, and pad audio if needed."""
+    # Check if audio is not blank
+    if audio is None:
+        return None
+    sr, audio_data = audio
+    if audio_data is None:
+        return None
+
+    if audio_data.ndim > 1:
+        audio_data = np.mean(audio_data, axis=0)
+
+    audio_tensor = torch.tensor(audio_data, dtype=torch.float32)
+    resampler = torchaudio.transforms.Resample(sr, 16000)
+    audio_data_resampled = resampler(audio_tensor).numpy()
+    
+    
+    min_length = 16000
+    if audio_data_resampled.shape[0] < min_length:
+        padding = np.zeros(min_length - audio_data_resampled.shape[0], dtype=audio_data_resampled.dtype)
+        audio_data_resampled = np.concatenate([audio_data_resampled, padding])
+    
+    return audio_data_resampled
+
+def predict_gender(audio):
+
+    if audio is None:
+        return {"Error": "No audio provided."}
+    audio_data = preprocess_audio(audio)
+    if audio_data is None:
+        return {"Error": "Invalid audio input."}
+    
+    inputs = feature_extractor(audio_data, sampling_rate=16000, return_tensors="pt", padding=True)
+    # Move each tensor in the inputs dictionary to the desired device.
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        scores = torch.nn.functional.softmax(logits, dim=-1).squeeze().tolist()
+    
+    return { id2label[0]: scores[0], id2label[1]: scores[1] }
+
+
+demo = gr.Interface(
+    fn=predict_gender,
+    inputs=gr.Audio(type="numpy"),  
+    outputs=gr.Label(num_top_classes=2), 
+    title="Voice Gender Detection",
+    description="Please use the microphone option and speak into the microphone to predict real time gender from voice."
+)
+
+demo.launch(debug=False, share=True)