Building Voice Agents with Nvidia’s Open Models 🎤✨

Introduction

In this comprehensive guide, we’ll delve into creating voice agents using advanced models from Nvidia. A voice agent is a digital assistant that can understand and respond to spoken commands or questions. It has immense potential in sectors like healthcare, automotive, and smart homes due to its user-friendly interface. By the end of this tutorial, you will have a basic understanding of how to build a speech-to-text engine using Nvidia’s latest offerings.

Prerequisites

Before we start coding, ensure your development environment is properly set up:

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  • Python 3.10+
  • torch version >= 2.0.0 (for PyTorch [2])
  • torchaudio version >= 2.0.0 (for audio processing in PyTorch)
  • nemo-cli version >= 1.25.0
  • pip install torch==2.0.0 torchaudio==2.0.0 nemo-cli==1.25.0

Step 1: Project Setup

Let’s initialize our project by setting up the necessary directories and installing required libraries. Ensure you have Python installed, then open a terminal or command prompt.

# Create a new directory for your project
mkdir voice-agent-nvidia
cd voice-agent-nvidia

# Initialize a virtual environment (optional but recommended)
python -m venv env
source env/bin/activate  # On Windows use `env\Scripts\activate`

# Install dependencies
pip install torch==2.0.0 torchaudio==2.0.0 nemo-cli==1.25.0

# Verify installation by checking the versions of installed packages
python -c "import torch; print(torch.__version__)"

Step 2: Core Implementation

Our main goal is to build a voice agent that can convert spoken words into text using Nvidia’s pre-trained models. This involves setting up an ASR (Automatic Speech Recognition) pipeline.

# Import necessary libraries from Nvidia NeMo
import nemo.collections.asr as nemo_asr
from omegaconf import OmegaConf

def main_function():
    # Load the pre-trained model provided by Nvidia for ASR
    asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_distilphone12x512")

    # Define a function to perform speech-to-text conversion
    def recognize_speech(audio_file):
        audio_data, sample_rate = torchaudio.load(audio_file)
        waveform = asr_model.preprocessor(audio_data)  # Preprocess the input data
        
        with torch.no_grad():
            log_probs, encoded_len, _ = asr_model.forward(input_signal=waveform, input_signal_length=[audio_data.shape[1]])
        
        predictions = asr_model.decoder(log_probs)
        transcriptions = [asr_model.tokenizer.arrays_to_text([pred]) for pred in predictions]
        return transcriptions

    # Example usage
    audio_file = "path/to/your/audiofile.wav"
    print("Transcribing speech...")
    transcription_result = recognize_speech(audio_file)
    
if __name__ == "__main__":
    main_function()

Step 3: Configuration

Configuring your voice agent involves setting up paths for your model and specifying which audio files it should process. This example also demonstrates how to save transcriptions in a file.

def configure_asr_model():
    # Path configuration for the input audio and output transcription
    config = OmegaConf.create({
        'input_audio': "path/to/your/audiofile.wav",
        'output_transcription': "./transcribed_text.txt"
    })
    
    return config

config = configure_asr_model()
print(f"Input Audio: {config.input_audio}")
print(f"Output Transcription Path: {config.output_transcription}")

# Save the transcription result to a file
with open(config.output_transcription, 'w') as output_file:
    for line in main_function():
        output_file.write(line + "\n")

Step 4: Running the Code

To test your voice agent, you need an audio input file. Make sure it’s placed correctly and run the script.

# Run the Python script
python main.py

# Expected output:
# > Transcribing speech...
# > Output file saved at ./transcribed_text.txt

Step 5: Advanced Tips

For production-grade voice agents, consider optimizing your pipeline by:

  • Batch Processing: Enhance performance for large-scale applications.
  • Error Handling: Improve the robustness of your application by adding error handling.
  • Real-time Streaming: Implement real-time speech-to-text capabilities.
# Example: Adding a simple error handler
def recognize_speech(audio_file):
    try:
        return super().recognize_speech(audio_file)
    except Exception as e:
        print(f"Error during transcription: {e}")

Results

By following this guide, you have successfully built and configured a voice agent that can convert spoken words to text using Nvidia’s open models. The output should be stored in transcribed_text.txt and include the transcriptions from your audio file.

Going Further

Conclusion

In this tutorial, we embarked on a journey to create a speech-to-text voice agent using cutting-edge models from Nvidia. We covered the entire process, from setting up your development environment to running and refining your code. With these skills, you’re well-equipped to build sophisticated AI-driven applications that interact with users through voice commands.

📚 References & Sources

Wikipedia

  1. Wikipedia - PyTorch - Wikipedia. Accessed 2026-01-08.

GitHub Repositories

  1. GitHub - pytorch/pytorch - Github. Accessed 2026-01-08.

All sources verified at time of publication. Please check original sources for the most current information.