Speech and Language AI

# The complete voice assistant pipeline

def voice_assistant_pipeline(audio_input):
    """
    Illustrates the steps in a voice assistant response.
    In real systems, each step uses a specialized model.
    """
    
    print("Voice Assistant Pipeline:")
    print()
    
    # Step 1: Speech to Text
    # Real: Whisper or similar ASR model processes audio waveform
    transcribed_text = "What is the weather like today in London?"
    print(f"Step 1 - Speech Recognition:")
    print(f"  Audio -> '{transcribed_text}'")
    print()
    
    # Step 2: Intent Understanding (NLP)
    # Real: NLP classifier identifies intent and entities
    intent = "weather_query"
    entities = {"location": "London", "time": "today"}
    print(f"Step 2 - Intent and Entity Extraction:")
    print(f"  Intent: {intent}")
    print(f"  Entities: {entities}")
    print()
    
    # Step 3: Action / API Call
    # Real: query a weather API
    weather_data = {"temp": "15C", "condition": "partly cloudy", "rain": "20%"}
    print(f"Step 3 - Action (Weather API call):")
    print(f"  Result: {weather_data}")
    print()
    
    # Step 4: Response Generation (NLP)
    response = f"Today in London it is {weather_data['temp']} and {weather_data['condition']}, with a {weather_data['rain']} chance of rain."
    print(f"Step 4 - Response Generation:")
    print(f"  '{response}'")
    print()
    
    # Step 5: Text to Speech
    print(f"Step 5 - Text-to-Speech:")
    print(f"  Text -> [Audio waveform spoken aloud to user]")
    print()
    
    return response

# Run the pipeline
result = voice_assistant_pipeline("audio_waveform_data")
print(f"Final spoken response: '{result}'"  )