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| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| def load_models(): | |
| """Load and verify models with error checking""" | |
| try: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print(f"Using device: {device}") | |
| # Load Whisper for speech recognition | |
| transcriber = pipeline( | |
| "automatic-speech-recognition", | |
| model="openai/whisper-tiny", | |
| device=device | |
| ) | |
| # Load emotion recognition model | |
| emotion_analyzer = pipeline( | |
| "text-classification", | |
| model="j-hartmann/emotion-english-distilroberta-base", | |
| device=device | |
| ) | |
| return transcriber, emotion_analyzer | |
| except Exception as e: | |
| print(f"Error loading models: {str(e)}") | |
| return None, None | |
| def analyze_audio(audio_path): | |
| """ | |
| Analyze audio for emotional content with detailed output | |
| """ | |
| if audio_path is None: | |
| return "Please provide audio", "No audio detected" | |
| try: | |
| # Load models | |
| transcriber, emotion_analyzer = load_models() | |
| if transcriber is None or emotion_analyzer is None: | |
| return "Error loading models", "Model initialization failed" | |
| # Transcribe speech | |
| try: | |
| result = transcriber(audio_path) | |
| text = result["text"] | |
| if not text.strip(): | |
| return "No speech detected", "Empty transcription" | |
| print(f"Transcribed text: {text}") # Debug output | |
| except Exception as e: | |
| return f"Transcription error: {str(e)}", "Failed to process audio" | |
| # Analyze emotion | |
| try: | |
| emotion_result = emotion_analyzer(text)[0] | |
| emotion = emotion_result["label"].title() # Capitalize emotion | |
| confidence = f"{emotion_result['score']:.2%}" | |
| # Map technical emotion labels to more natural descriptions | |
| emotion_mapping = { | |
| "Joy": "Happy/Joyful", | |
| "Sadness": "Sad/Melancholic", | |
| "Anger": "Angry/Frustrated", | |
| "Fear": "Anxious/Fearful", | |
| "Surprise": "Surprised/Astonished", | |
| "Love": "Warm/Affectionate", | |
| "Neutral": "Neutral/Calm" | |
| } | |
| display_emotion = emotion_mapping.get(emotion, emotion) | |
| return display_emotion, confidence | |
| except Exception as e: | |
| return f"Emotion analysis error: {str(e)}", "Analysis failed" | |
| except Exception as e: | |
| return f"Unexpected error: {str(e)}", "Process failed" | |
| # Create interface with better labeling | |
| interface = gr.Interface( | |
| fn=analyze_audio, | |
| inputs=gr.Audio( | |
| sources=["microphone", "upload"], | |
| type="filepath", | |
| label="Record or Upload Audio" | |
| ), | |
| outputs=[ | |
| gr.Textbox(label="Detected Emotion"), | |
| gr.Textbox(label="Confidence Score") | |
| ], | |
| title="Speech Emotion Analyzer", | |
| description=""" | |
| This tool analyzes the emotional tone of speech, detecting emotions like: | |
| - Happy/Joyful | |
| - Sad/Melancholic | |
| - Angry/Frustrated | |
| - Anxious/Fearful | |
| - Surprised/Astonished | |
| - Warm/Affectionate | |
| - Neutral/Calm | |
| """, | |
| theme=gr.themes.Base() | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch( | |
| debug=True, | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=True | |
| ) |