Creating Voice-Activated Assistants with Ollama

What is Ollama?

Getting Started with Ollama

1. Ollama
   • the server that runs LLMs locally. You can find Ollama here.
2. Audio Libraries
   • Essential libraries like
     1 sounddevice
     &
     1 PyAudio
     will help in capturing and playback audio signals.
3. Whisper
   • OpenAI’s Whisper can be utilized for speech-to-text processing. You can check out Whisper here.
4. Bark
   • This is a state-of-the-art text-to-speech library suitable for synthesizing audio responses (you can find it here).

Setting Up Your Environment

• Create a virtual Python environment using Poetry or virtualenv. Both options help manage dependencies effectively.
• Install the required libraries:
  1 2 bash pip install rich openai-whisper suno-bark langchain sounddevice pyaudio speechrecognition

Architecture of the Assistant

1. Speech Recognition: Using Whatspeech, input audio is converted into text. Whisper supports multiple languages, giving it an edge in diverse environments.
2. Conversational Chain: To add conversational abilities, you can utilize Langchain paired with an LLM like Llama-2, served through Ollama. This setup ensures a smooth & engaging conversational flow.
3. Speech Synthesizer: This component transforms the generated text response back into speech using Bark, mimicking lifelike voice outputs.

Writing the Code

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import sounddevice as sd
import numpy as np
import whispers
import suno.bark
import langchain

class TextToSpeechService:
    def __init__(self):
        self.model = bark.load_model('suno/bark-small')
        
    def synthesize(self, text):
        audio_tensor = self.model.synthesize(text)
        return audio_tensor

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def record_audio():
    recorded_audio = []
    with sd.InputStream(callback=lambda indata, frames, time, status: recorded_audio.append(indata)):
        sd.sleep(10000)  # Record for 10 seconds
    return np.concatenate(recorded_audio)

Tying Features Together

1. Capture Speech: Record user audio input and process it through Whisper to convert it into text using the
   1 transcribe
   function.
2. Generate Response: Feed the transcribed text into the LLM to receive a contextually relevant answer.
3. Output Speech: Finally, the generated response can be synthesized back into audio and played to the user through the speaker.

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if __name__ == "__main__":
    while True:
        # Capture audio from user
        audio = record_audio()
        # Convert audio to text
        input_text = whispers.transcribe(audio)
        # Get response from LLM
        response = generate_response(input_text)
        # Speak back to user
        tts.synthesize(response)

Enhancing Functionality

1. Customizable Prompts: Allow users to adjust how friendly/formal the assistant’s responses should be. You could save profiles for different users.
2. Memory Capabilities: Implement a system that lets the assistant remember user preferences or information across sessions, making interactions feel more seamless.
3. Integration with Other Services: Consider integrating your assistant with existing services, such as calendars or smart home devices to perform tasks REAL-TIME.
4. Natural Language Understanding: Utilize additional libraries for better understanding of user queries, enhancing the depth of responses.

The Power of Arsturn

• Design unique chatbots without the need for coding skills.
• Utilize insightful analytics to understand audience behavior.
• Integrate seamlessly into your existing applications, enhancing user engagement.

Conclusion