Create app.py

app.py

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+import streamlit as st
+import whisper
+from transformers import pipeline
+from streamlit_mic_recorder import mic_recorder
+import wave
+import numpy as np
+import os
+
+temp_audio_file_path = "./output.wav"
+
+# Streamlit app structure
+st.title("🎤 English ASR 💬")
+# Load models
+model = whisper.load_model("base")
+st.write("Whisper Model Loaded!")
+sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions")
+
+st.write("Record your voice, and play the recorded audio:")
+audio=mic_recorder(start_prompt="▶️",stop_prompt="🛑",key='recorder')
+# audio=mic_recorder(start_prompt="Start",stop_prompt="Stop",key='recorder')
+
+if audio:       
+    st.audio(audio['bytes'])
+    
+    audio_bytes = audio["bytes"]
+    # Set the audio file parameters
+    sample_width = audio["sample_width"]  # 2 bytes per sample for 16-bit PCM
+    sample_rate = audio["sample_rate"]  # 44.1 kHz sample rate
+    num_channels = 1  # 1 channel for mono, 2 for stereo
+
+    # Create a new wave file and write the audio bytes
+    with wave.open(temp_audio_file_path, 'w') as wave_file:
+        wave_file.setnchannels(num_channels)
+        wave_file.setsampwidth(sample_width)
+        wave_file.setframerate(sample_rate)
+        wave_file.writeframes(audio_bytes)    
+    
+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(ans, sentiment_option):
+    sentiment_results = analyze_sentiment(ans)
+    sentiment_output = display_sentiment_results(sentiment_results, sentiment_option)
+    return sentiment_output
+
+# Sentiment Option Radio
+sentiment_option = st.radio("Select an option", ["Sentiment Only", "Sentiment + Score"], index=0)
+
+# Button to trigger the processing
+if st.button("Get sentiments"):
+    st.write("Transcribing Audio...")
+    result = model.transcribe(temp_audio_file_path)
+    ans = result["text"]
+    st.write(ans)
+
+    # Call the inference function with inputs and get outputs
+    sentiment_output_value = inference(ans, sentiment_option)
+    st.write(sentiment_output_value)
+
+# Add a footer
+st.markdown('''
+    Whisper Model by [OpenAI](https://github.com/openai/whisper)
+            ''')