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import gradio as gr |
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import whisper |
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from transformers import pipeline |
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model = whisper.load_model("base") |
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sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions") |
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def analyze_sentiment(text): |
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results = sentiment_analysis(text) |
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sentiment_results = {result['label']: result['score'] for result in results} |
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return sentiment_results |
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def get_sentiment_emoji(sentiment): |
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emoji_mapping = { |
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"disappointment": "😞", |
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"sadness": "😢", |
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"annoyance": "😠", |
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"neutral": "😐", |
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"disapproval": "👎", |
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"realization": "😮", |
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"nervousness": "😬", |
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"approval": "👍", |
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"joy": "😄", |
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"anger": "😡", |
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"embarrassment": "😳", |
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"caring": "🤗", |
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"remorse": "😔", |
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"disgust": "🤢", |
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"grief": "😥", |
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"confusion": "😕", |
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"relief": "😌", |
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"desire": "😍", |
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"admiration": "😌", |
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"optimism": "😊", |
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"fear": "😨", |
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"love": "❤️", |
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"excitement": "🎉", |
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"curiosity": "🤔", |
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"amusement": "😄", |
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"surprise": "😲", |
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"gratitude": "🙏", |
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"pride": "🦁" |
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} |
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return emoji_mapping.get(sentiment, "") |
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def display_sentiment_results(sentiment_results, option): |
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sentiment_text = "" |
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for sentiment, score in sentiment_results.items(): |
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emoji = get_sentiment_emoji(sentiment) |
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if option == "Sentiment Only": |
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sentiment_text += f"{sentiment} {emoji}\n" |
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elif option == "Sentiment + Score": |
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sentiment_text += f"{sentiment} {emoji}: {score}\n" |
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return sentiment_text |
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def inference(audio, sentiment_option): |
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audio = whisper.load_audio(audio) |
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audio = whisper.pad_or_trim(audio) |
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mel = whisper.log_mel_spectrogram(audio).to(model.device) |
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_, probs = model.detect_language(mel) |
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lang = max(probs, key=probs.get) |
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options = whisper.DecodingOptions(fp16=False) |
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result = whisper.decode(model, mel, options) |
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sentiment_results = analyze_sentiment(result.text) |
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sentiment_output = display_sentiment_results(sentiment_results, sentiment_option) |
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return lang.upper(), result.text, sentiment_output |
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title = """<h1 align="center">🎤 Multilingual ASR 💬</h1>""" |
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image_path = "thmbnail.jpg" |
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description = """ |
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💻 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.<br><br> |
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<br> |
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⚙️ Components of the tool:<br> |
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<br> |
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- Real-time multilingual speech recognition<br> |
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- Language identification<br> |
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- Sentiment analysis of the transcriptions<br> |
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<br> |
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🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.<br> |
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<br> |
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😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.<br> |
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<br> |
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✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.<br> |
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<br> |
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❓ Use the microphone for real-time speech recognition.<br> |
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<br> |
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⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.<br> |
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""" |
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custom_css = """ |
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#banner-image { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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} |
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#chat-message { |
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font-size: 14px; |
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min-height: 300px; |
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} |
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""" |
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block = gr.Blocks(css=custom_css) |
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with block: |
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gr.HTML(title) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Image(image_path, elem_id="banner-image", show_label=False) |
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with gr.Column(): |
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gr.HTML(description) |
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with gr.Group(): |
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with gr.Box(): |
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audio = gr.Audio( |
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label="Input Audio", |
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show_label=False, |
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source="microphone", |
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type="filepath" |
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) |
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sentiment_option = gr.Radio( |
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choices=["Sentiment Only", "Sentiment + Score"], |
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label="Select an option", |
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default="Sentiment Only" |
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) |
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btn = gr.Button("Transcribe") |
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lang_str = gr.Textbox(label="Language") |
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text = gr.Textbox(label="Transcription") |
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sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True) |
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btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output]) |
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gr.HTML(''' |
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<div class="footer"> |
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<p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> |
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</p> |
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</div> |
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''') |
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block.launch() |
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