> For the complete documentation index, see [llms.txt](https://whitepaper.virtuals.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.virtuals.io/about-virtuals-1/the-protocol/ai-agent-co-contribution-and-provenance/modular-consensus-framework-for-ai-agent-governance/decentralized-ai-agent-contributions-and-nfts/voice-core-for-ai-agents.md).

# Voice Core for AI Agents

The Voice Core gives each VIRTUAL agent a distinct, personality-aligned voice. AI voice model training creates realistic, consistent speech for each agent and role.

### AI agent voice modules

**Speech-to-text (STT):** The STT module trains on diverse voice data. It accurately transcribes accents, dialects, and speech patterns across user scenarios.

**Text-to-speech (TTS):** The TTS module uses Variational Inference for Text-to-Speech (VITS) training. VITS produces high-quality, natural-sounding speech and supports voice synthesis customized to each AI agent’s personality.

Audio data preprocessing occurs before voice model training.

### Audio data preprocessing for voice models

1. **Audio format consistency:** WAV files at 22050 Hz in mono create consistent training inputs. Consistent input data helps machine learning voice models perform reliably.
2. **Sampling-rate normalization:** A 22050 Hz sampling rate captures human speech frequencies while keeping file sizes manageable. It captures frequencies up to 11025 Hz under the Nyquist theorem.
3. **Mono audio channels:** Converting stereo or multi-channel audio to mono gives the voice model one training channel and simplifies learning.

<details>

<summary>Sample Code</summary>

```python
import os
from pydub import AudioSegment

upload_dir = 'upload_dir'
output_dir = 'out'

# Ensure the output directory exists
os.makedirs(output_dir, exist_ok=True)

extensions = ['wav', 'mp3', 'ogg']

# Process all files in the upload directory
for filename in os.listdir(upload_dir):
    if any(filename.lower().endswith(ext) for ext in extensions):
        # Construct file paths
        file_path = os.path.join(upload_dir, filename)
        output_path = os.path.join(output_dir, os.path.splitext(filename)[0] + '.wav')

        # Load the audio file
        audio = AudioSegment.from_file(file_path)

        # Convert to WAV, 22050 Hz, mono
        audio = audio.set_frame_rate(22050).set_channels(1)

        # Export the processed audio
        audio.export(output_path, format='wav')

```

</details>

[<mark style="color:red;">Learn more about contributing to Voice Core.</mark>](/builders-hub/build-with-virtuals/agent-contribution/contribute-to-voice-core.md)


---

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