Update app.py

app.py

@@ -1,152 +1,18 @@
- 
-import gradio as gr
-import whisper
+import torchaudio
 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
+# Load the Hugging Face ASR model
+asr_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
 
 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)
+    # Load and preprocess audio
+    audio_tensor, _ = torchaudio.load(audio)
+    
+    # Perform ASR
+    transcription = asr_pipeline(audio_tensor.numpy()[0], sampling_rate=audio_tensor.sampling_rate)
+
+    # Perform sentiment analysis
+    sentiment_results = analyze_sentiment(transcription[0]['sentence'])
     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 = "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>
-<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>
-<br>
-😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.<br>
-<br>
-✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.<br>
-<br>
-❓ Use the microphone for real-time speech recognition.<br>
-<br>
-⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.<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()
+    return "N/A", transcription[0]['sentence'], sentiment_output