El Bruno

⚠️ This blog post was created with the help of AI tools. Yes, I used a bit of magic from language models to organize my thoughts and automate the boring parts, but the geeky fun and the 🤖 in C# are 100% mine.

Hi!

Let’s look at these 2 code snippets… what’s behind them?

🧠 Snippet 1 — VibeVoice (Native TTS in .NET)

using ElBruno.VibeVoiceTTS;

using var tts = new VibeVoiceSynthesizer();
await tts.EnsureModelAvailableAsync(); // auto-download model if needed

float[] audio = await tts.GenerateAudioAsync("Hello! Welcome to VibeVoiceTTS.", "Carter");
tts.SaveWav("output.wav", audio);

This generates a WAV file from text using the VibeVoice-Realtime-0.5B model, running locally via ONNX.
The first time you run it, the model is automatically downloaded.

No REST calls. No API keys. No cloud dependency.

🧠 Snippet 2 — QwenTTS (Local TTS + Voice Cloning Ready)

using ElBruno.QwenTTS.Pipeline;

// Models are downloaded automatically on first execution
using var pipeline = await TtsPipeline.CreateAsync("models");
await pipeline.SynthesizeAsync("Hello world!", "ryan", "hello.wav", "english");

This example uses a Qwen3-TTS ONNX pipeline to generate speech locally, fully in C#.

Why I Built This

My goal has always been simple:

Make AI easy and natural for .NET developers.

We’ve made great progress in:

• Embeddings
• Agents
• RAG
• Local models
• AI orchestration

But when it came to Text-to-Speech, there was a gap.

Most solutions required:

• Python
• External services
• Complex wrappers
• Non-.NET idioms

I didn’t like that. IMHO, then TTS should feel like C# — not like glue code around another ecosystem. With these repositories I’ll give a try.

What Makes This Different?

Both libraries are built around a few core principles:

✅ 100% Local Execution

Models run on your machine (or your server).

✅ ONNX + .NET Runtime

No Python in production.

✅ Auto Model Management

Models download automatically the first time you use them.

✅ Idiomatic C# APIs

Async/await. Disposable patterns. Clean abstractions.

If you can use HttpClient, you can use these libraries.
If you understand Task, you can generate AI-powered speech.

VibeVoice — Simple and Direct

Repository: https://github.com/elbruno/ElBruno.VibeVoiceTTS

NuGet: https://www.nuget.org/packages/ElBruno.VibeVoiceTTS

VibeVoice is ideal if you want:

• Fast setup
• Built-in voice presets
• Clean WAV output
• Minimal configuration

It uses the VibeVoice-Realtime-0.5B ONNX model and exposes a straightforward synthesizer API.

QwenTTS — Flexible and Powerful

Repository: https://github.com/elbruno/ElBruno.QwenTTS

NuGet: https://www.nuget.org/packages/ElBruno.QwenTTS

QwenTTS is built around Qwen3-TTS, exported to ONNX and integrated into a C# pipeline.

It supports:

• Multiple speakers
• Multi-language scenarios
• More advanced synthesis control
• Voice cloning capabilities (via dedicated pipeline)

This opens the door to:

• Custom AI assistants
• Personalized voice experiences
• Voice-enabled RAG systems
• AI avatars

Why Local TTS Matters

Running TTS locally gives you:

• 🔒 Privacy — no text leaves your machine
• 💰 No per-request costs
• ⚡ Low latency
• 🧪 A safe playground for experimentation
• 📦 Full control over deployment

If you’re exploring:

• Local AI
• Foundry Local
• Offline AI scenarios
• Edge deployments

These libraries are a practical starting point.

Bonus: Voice Cloning (Work in progress)

The QwenTTS repository includes support for voice cloning via a dedicated pipeline.

This means you can:

• Generate speech in a reference voice
• Personalize assistant experiences
• Experiment with identity-driven AI systems

Final Thoughts

For me, generating natural speech locally should be as simple as:

• Adding a NuGet package
• Writing a few lines of C#
• Running your app

That’s it.