Create app.py

app.py

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+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 = """<h1 align="center">🎤 Multilingual ASR 💬</h1>"""
+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.<br><br>
+📝 For more details, check out the [GitHub repository](https://github.com/openai/whisper).<br><br>
+⚙️ Components of the tool:<br>
+<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Real-time multilingual speech recognition<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Language identification<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Sentiment analysis of the transcriptions<br>
+<br>
+🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.<br>
+✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.<br>
+❓ Use the "Input Audio" option to provide an audio file or use the microphone for real-time speech recognition.<br>
+⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.<br>
+😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.<br>
+"""
+
+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('''
+        <div class="footer">
+            <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a>
+            </p>
+        </div>
+        ''')
+
+block.launch()