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| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| def create_speech_analyzer(): | |
| """Initialize the speech-to-text and sentiment analysis models""" | |
| try: | |
| # Load Faster Whisper for improved speech recognition | |
| transcriber = pipeline( | |
| "automatic-speech-recognition", | |
| model="openai/whisper-small", # Using smaller model for faster processing | |
| max_new_tokens=128 | |
| ) | |
| # Load RoBERTa model for more nuanced sentiment analysis | |
| sentiment_model = pipeline( | |
| "sentiment-analysis", | |
| model="cardiffnlp/twitter-roberta-base-sentiment-latest" | |
| ) | |
| return transcriber, sentiment_model | |
| except Exception as e: | |
| raise RuntimeError(f"Error loading models: {str(e)}") | |
| def analyze_speech(audio_file): | |
| """ | |
| Analyze speech for transcription and emotional content. | |
| Returns both the transcription and detailed sentiment analysis. | |
| """ | |
| try: | |
| transcriber, sentiment_model = create_speech_analyzer() | |
| # Get transcription | |
| transcription = transcriber(audio_file)["text"] | |
| # RoBERTa provides more detailed sentiment analysis | |
| sentiment_result = sentiment_model(transcription)[0] | |
| # Map sentiment labels to more readable format | |
| sentiment_mapping = { | |
| 'LABEL_0': 'Negative', | |
| 'LABEL_1': 'Neutral', | |
| 'LABEL_2': 'Positive' | |
| } | |
| # Get the sentiment label and score | |
| sentiment = sentiment_mapping.get(sentiment_result['label'], sentiment_result['label']) | |
| confidence = sentiment_result['score'] | |
| # Analyze sentiment of smaller chunks for longer texts | |
| if len(transcription.split()) > 50: | |
| # Split into sentences or chunks | |
| chunks = transcription.split('.') | |
| chunk_sentiments = [sentiment_model(chunk)[0] for chunk in chunks if len(chunk.strip()) > 0] | |
| # Calculate average sentiment | |
| avg_sentiment = np.mean([s['score'] for s in chunk_sentiments]) | |
| sentiment_variation = np.std([s['score'] for s in chunk_sentiments]) | |
| detailed_analysis = ( | |
| f"Overall Sentiment: {sentiment} ({confidence:.2%})\n" | |
| f"Sentiment Stability: {1 - sentiment_variation:.2%}\n" | |
| f"Text chunks analyzed: {len(chunk_sentiments)}" | |
| ) | |
| else: | |
| detailed_analysis = f"Sentiment: {sentiment} ({confidence:.2%})" | |
| return { | |
| "transcription": transcription, | |
| "sentiment": sentiment, | |
| "analysis": detailed_analysis | |
| } | |
| except Exception as e: | |
| return { | |
| "transcription": f"Error in processing: {str(e)}", | |
| "sentiment": "ERROR", | |
| "analysis": "Analysis failed" | |
| } | |
| def create_interface(): | |
| """Create and configure the Gradio interface""" | |
| return gr.Interface( | |
| fn=analyze_speech, | |
| inputs=gr.Audio( | |
| sources=["microphone", "upload"], | |
| type="filepath", | |
| label="Upload or Record Audio" | |
| ), | |
| outputs=[ | |
| gr.Textbox(label="Transcription", lines=3), | |
| gr.Textbox(label="Sentiment"), | |
| gr.Textbox(label="Detailed Analysis", lines=3) | |
| ], | |
| title="Advanced Speech Sentiment Analyzer", | |
| description=""" | |
| This tool performs advanced sentiment analysis on speech using RoBERTa. | |
| It provides detailed sentiment analysis for longer texts and handles | |
| both audio uploads and microphone recordings. | |
| """, | |
| theme=gr.themes.Soft(), | |
| examples=[] | |
| ) | |
| def main(): | |
| """Launch the application""" | |
| interface = create_interface() | |
| interface.launch( | |
| share=True, | |
| debug=True, | |
| server_name="0.0.0.0" | |
| ) | |
| if __name__ == "__main__": | |
| main() |