import gradio as gr import whisper from transformers import pipeline model = whisper.load_model("base") sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions") def analyze_sentiment(text): results = sentiment_analysis(text) sentiment_results = {result['label']: result['score'] for result in results} return sentiment_results def get_sentiment_emoji(sentiment): # Define the emojis corresponding to each sentiment emoji_mapping = { "disappointment": "😞", "sadness": "😢", "annoyance": "😠", "neutral": "😐", "disapproval": "👎", "realization": "😮", "nervousness": "😬", "approval": "👍", "joy": "😄", "anger": "😡", "embarrassment": "😳", "caring": "🤗", "remorse": "😔", "disgust": "🤢", "grief": "😥", "confusion": "😕", "relief": "😌", "desire": "😍", "admiration": "😌", "optimism": "😊", "fear": "😨", "love": "❤️", "excitement": "🎉", "curiosity": "🤔", "amusement": "😄", "surprise": "😲", "gratitude": "🙏", "pride": "🦁" } return emoji_mapping.get(sentiment, "") def display_sentiment_results(sentiment_results, option): sentiment_text = "" for sentiment, score in sentiment_results.items(): emoji = get_sentiment_emoji(sentiment) if option == "Sentiment Only": sentiment_text += f"{sentiment} {emoji}\n" elif option == "Sentiment + Score": sentiment_text += f"{sentiment} {emoji}: {score}\n" return sentiment_text def inference(audio, sentiment_option): audio = whisper.load_audio(audio) audio = whisper.pad_or_trim(audio) mel = whisper.log_mel_spectrogram(audio).to(model.device) _, probs = model.detect_language(mel) lang = max(probs, key=probs.get) options = whisper.DecodingOptions(fp16=False) result = whisper.decode(model, mel, options) sentiment_results = analyze_sentiment(result.text) sentiment_output = display_sentiment_results(sentiment_results, sentiment_option) return lang.upper(), result.text, sentiment_output title = """

🎤 Multilingual ASR 💬

""" image_path = "/content/thmbnail.jpg" description = """ 💻 This demo showcases a general-purpose speech recognition model called Whisper. It is trained on a large dataset of diverse audio and supports multilingual speech recognition, speech translation, and language identification tasks.

📝 For more details, check out the [GitHub repository](https://github.com/openai/whisper).

⚙️ Components of the tool:

     - Real-time multilingual speech recognition
     - Language identification
     - Sentiment analysis of the transcriptions

🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.
✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.
❓ Use the "Input Audio" option to provide an audio file or use the microphone for real-time speech recognition.
⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.
😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.
""" custom_css = """ #banner-image { display: block; margin-left: auto; margin-right: auto; } #chat-message { font-size: 14px; min-height: 300px; } """ block = gr.Blocks(css=custom_css) with block: gr.HTML(title) with gr.Row(): with gr.Column(): gr.Image(image_path, elem_id="banner-image", show_label=False) with gr.Column(): gr.HTML(description) with gr.Group(): with gr.Box(): audio = gr.Audio( label="Input Audio", show_label=False, source="microphone", type="filepath" ) sentiment_option = gr.Radio( choices=["Sentiment Only", "Sentiment + Score"], label="Select an option", default="Sentiment Only" ) btn = gr.Button("Transcribe") lang_str = gr.Textbox(label="Language") text = gr.Textbox(label="Transcription") sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True) btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output]) gr.HTML(''' ''') block.launch()