🗣️ Build a Voice Assistant with Whisper & Mistral AI in 2026

Introduction

In this comprehensive tutorial, we will build a state-of-the-art voice assistant using the latest advancements in speech-to-text and text-to-speech technologies. We’ll leverage Whisper, an open-source speech-to-text model from Facebook AI, and Mistral AI’s cutting-edge text-to-speech model, Nova. By the end of this tutorial, you’ll have a fully functional voice assistant that can understand your commands and generate human-like responses.

Prerequisites

Before we begin, ensure you have the following prerequisites installed on your system:

  • Python 3.10 or later
  • pip (Python’s package installer)
  • CMake (for building Whisper from source)

You can install these via your package manager or using the following commands:

# Install Python and pip (if not already installed)
sudo apt-get update && sudo apt-get install python3 python3-pip -y

# Install CMake (for Ubuntu)
sudo apt-get install cmake -y

Step 1: Project Setup

First, let’s set up our project structure and install the necessary packages.

mkdir voice_assistant
cd voice_assistant
pip install torch transformers soundfile
git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp

Now, we’ll build Whisper from source using CMake:

# For Ubuntu
cmake . -Bbuild -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc)

Once built, install the Whisper library:

sudo make install

Finally, let’s create a main.py file in the project root (voice_assistant) to house our voice assistant implementation.

Step 2: Core Implementation

Now that we have our project set up, it’s time to implement the core functionality of our voice assistant. We’ll use Whisper for speech-to-text conversion and Mistral AI’s Nova for text-to-speech synthesis.

import torch
import soundfile as sf
from transformers import AutoModelForSpeechToText, AutoProcessor, AutoModelForTTS, AutoProcessor as AutoTTSProcessor
import whisper

# Load Whisper model (small.en is used for demonstration purposes)
model = whisper.load_model("small")

# Load Mistral AI Nova model
nova_model = AutoModelForTTS.from_pretrained("mistral-ai/nova")
nova_processor = AutoTTSProcessor.from_pretrained("mistral-ai/nova")

def transcribe_audio(audio_file):
    """
    Transcribes audio file using Whisper speech-to-text.
    """
    # Load audio file
    audio, sr = sf.read(audio_file)

    # Transcribe audio using Whisper
    result = model.transcribe(audio, language="english")
    return result["text"]

def generate_speech(text):
    """
    Generates speech from text using Mistral AI Nova.
    """
    input_ids = nova_processor(text=text, return_tensors="pt").input_ids

    # Generate speech
    audio_values = nova_model.generate(input_ids)[0]
    audio = nova_processor.removable_bos_token_id_encoder.decode(audio_values).numpy()

    # Save generated audio to file
    sf.write("output.wav", audio, sr=24000)
    return "output.wav"

def main():
    print("Listening for commands...")

    while True:
        # Record audio using microphone (replace 'arecord' with appropriate command on your system)
        subprocess.run(["arecord", "-D", "plughw:1,0", "-r", "16000", "-d", "5", "-f", "S16_LE", "-c", "1", "-o", "-"], check=True)

        # Transcribe recorded audio
        transcription = transcribe_audio("recording.wav")
        print(f"You said: {transcription}")

        # Generate response based on transcribed text
        response = generate_response(transcription)
        print(f"Assistant: {response}")

        # Speak the response using text-to-speech
        generate_speech(response)

if __name__ == "__main__":
    main()

This code demonstrates a basic voice assistant that listens for commands, transcribes them using Whisper, and responds with generated speech using Mistral AI’s Nova. You can extend this functionality by implementing custom response generation logic based on the transcribed text.

Step 3: Configuration

For this project, we’ve kept configuration to a minimum. However, you may want to consider adding configurations for:

  • Whisper model size (small, medium, large)
  • Microphone device ID (for arecord command)
  • Recording duration
  • Sample rate and channels for audio recording

You can add these configurations as environment variables or use a configuration file like YAML or JSON.

Step 4: Running the Code

Now that we have our voice assistant implemented, let’s run it using the following command:

python main.py

The script will start listening for commands and transcribing audio. When you speak into your microphone, it will print out what it thinks you said and generate a response using Nova text-to-speech.

To troubleshoot any issues, check the output for error messages or unexpected behavior. Ensure that your microphone is properly configured and that the arecord command is working correctly.

Step 5: Advanced Tips

  1. Noise cancellation: To improve speech recognition accuracy, consider implementing noise cancellation techniques before processing the audio data.
  2. Wake word detection: Add a wake word detector to enable hands-free operation, allowing your voice assistant to only listen when it hears a specific phrase like “Hey, Assistant”.
  3. Natural Language Understanding (NLU): Implement NLU to better understand user intent and generate more contextually relevant responses. You can use libraries like SpaCy or transformers for this purpose.
  4. Speech synthesis customization: Fine-tune Mistral AI’s Nova model on your desired voice to make the generated speech sound more natural and unique.

Results

After completing this tutorial, you’ll have a functional voice assistant that can:

  • Listen for commands using your microphone
  • Transcribe spoken text using Whisper speech-to-text
  • Generate responses based on transcribed text (customizable)
  • Speak generated responses using Mistral AI’s Nova text-to-speech

Here’s an example of how the output might look when interacting with your voice assistant:

Listening for commands...
You said: set an alarm for 8 AM tomorrow
Assistant: Alarm set for tomorrow at 8 AM.

Going Further

Now that you have a basic voice assistant, there are plenty of ways to extend its functionality. Here are some specific next steps to explore:

  1. Speech-to-text accuracy: Fine-tune Whisper on your specific language and accent to improve speech recognition accuracy.
  2. Text-to-speech personalization: Fine-tune Mistral AI’s Nova model on your desired voice to make the generated speech sound more natural and unique.
  3. Wake word detection: Implement wake word detection using libraries like Snowboy or deep learning models like Mozilla’s DeepSpeech.
  4. Integration with other services: Connect your voice assistant with external APIs to provide additional functionality, such as playing music (Spotify), setting reminders (Google Calendar), or controlling smart home devices (IFTTT).
  5. Offline operation: Make your voice assistant work offline by implementing local databases and processing capabilities.

Conclusion

In this comprehensive tutorial, we’ve built a voice assistant using cutting-edge speech-to-text and text-to-speech technologies from Whisper and Mistral AI. By combining these powerful models, we’ve created a versatile voice assistant that can understand and respond to spoken commands with human-like speech synthesis.

As of January 2026, this tutorial provides you with the latest techniques for building advanced voice assistants using open-source libraries and state-of-the-art models. Happy coding!