Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoFeatureExtractor, AutoModelForAudioClassification
+import torch
+import librosa
+import numpy as np
+
+# Load model and feature extractor
+model_id = "your-username/speech-emotion-recognition-model" 
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+model = AutoModelForAudioClassification.from_pretrained(model_id)
+
+# Define emotions
+emotions = ["neutral", "happy", "sad", "angry", "fearful", "disgust", "surprised"]
+
+def predict_emotion(audio_path):
+    # Load audio
+    audio, sampling_rate = librosa.load(audio_path, sr=16000)
+    
+    # Process through feature extractor
+    inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
+    
+    # Get prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+        predicted_class_id = torch.argmax(probs, dim=1).item()
+        predicted_label = emotions[predicted_class_id]
+        confidence = probs[0][predicted_class_id].item()
+    
+    # Return result
+    result = {emotion: float(probs[0][i].item()) for i, emotion in enumerate(emotions)}
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=gr.Label(num_top_classes=7),
+    title="Speech Emotion Recognition",
+    description="Upload audio or record your voice to identify the emotion. This model can detect neutral, happy, sad, angry, fearful, disgust, and surprised emotions."
+)
+
+demo.launch()