How to Use ONNX Models for Voice Conversion

ONNX (Open Neural Network Exchange) is an open format for representing machine learning models. In voice conversion, ONNX models provide cross-platform compatibility and efficient performance. This guide will walk you through everything you need to know about using ONNX models for RVC voice conversion.

What Makes ONNX Special?

ONNX models offer several advantages for voice conversion:

• Universal Compatibility: Works across different frameworks and platforms
• Optimized Performance: Efficient inference for faster processing
• Smaller File Sizes: Compressed models that are easier to distribute
• Hardware Acceleration: Support for GPU and specialized AI hardware

Getting ONNX Models

There are several ways to obtain ONNX models for voice conversion:

1. Pre-trained Models

Many communities and developers share pre-trained ONNX models. Look for models that specify:

• Input/output specifications
• Sample rate compatibility (typically 40kHz or 48kHz)
• Model version and framework
• Training data characteristics

2. Converting Models to ONNX

If you have a PyTorch or TensorFlow model, you can convert it to ONNX format using built-in conversion tools. This process involves:

1. Loading your trained model
2. Defining input shapes and specifications
3. Exporting to ONNX format
4. Validating the converted model

Using ONNX Models in Momentum

Step 1: Prepare Your Model

Ensure your ONNX model file has the .onnx extension and is properly formatted. Check that:

• The file isn't corrupted (try opening with an ONNX viewer)
• Model metadata is present and accurate
• File size is reasonable (typically 50-200MB for voice models)

Step 2: Load the Model

In Momentum, loading an ONNX model is straightforward:

1. Open Momentum application
2. Navigate to the model selection interface
3. Click "Load Model" or drag-and-drop your ONNX file
4. Wait for model validation and initialization

Step 3: Configure Settings

Optimize your voice conversion by adjusting key parameters:

• Pitch: Adjust to match target voice characteristics
• Index Rate: Controls feature retrieval strength (0.0 to 1.0)
• Filter Radius: Smooths pitch changes for natural sound
• Volume Envelope: Preserves original volume dynamics

Step 4: Process Your Audio

With your model loaded and configured:

1. Import your audio file or record in real-time
2. Select the loaded ONNX model
3. Apply voice conversion
4. Preview and adjust settings as needed
5. Export your converted audio

Troubleshooting Common Issues

Model Won't Load

If your ONNX model fails to load, try these solutions:

• Verify file integrity (re-download if necessary)
• Check ONNX version compatibility
• Ensure sufficient system memory
• Update to the latest version of Momentum

Poor Output Quality

For suboptimal results:

• Adjust pitch settings incrementally
• Try different index rate values
• Use higher quality input audio
• Experiment with filter radius settings

Best Practices

To get the most out of ONNX models in voice conversion:

Performance Optimization

Maximize performance when working with ONNX models:

• Use GPU acceleration when available
• Close unnecessary applications to free up resources
• Process audio in batches for efficiency
• Consider model quantization for faster inference

ONNX models have made voice conversion more accessible and efficient. With Momentum's native ONNX support, you can leverage these powerful models for high-quality voice transformations.