Introduction DIA is a powerful open-source text-to-speech (TTS) framework developed by Nari Labs . With 1.6 billion parameters , it excels at generating ultra-realistic multi-character dialogues , complete with emotional tone control and non-verbal expressions like laughter, coughing, or throat clearing. In this guide, you'll learn step-by-step how to use DIA, along with its advantages, limitations, and real-world applications. ✅ How to Use DIA: Step-by-Step 1. Prerequisites GPU with at least 10 GB VRAM (e.g., NVIDIA RTX 4090) Python 3.8+ , PyTorch 2.0+, CUDA 12.6 Git , and optionally Docker or Cog for containerized setups 2. Installation (via Python or Docker) Option A: Clone and Install git clone https://github.com/nari-labs/dia.git cd dia python -m venv .venv source .venv/bin/activate pip install -e . python app.py This setup launches a Gradio interface to test DIA locally. Option B: Docker or Cog Use zsxkib/cog-dia to wrap DIA into a Cog container for easy local deployment. For extended API support, try devnen/Dia-TTS-Server , which provides OpenAI-compatible endpoints and advanced controls. 3. Using DIA Gradio UI: Open the interface at http://127.0.0.1:7860 Use [S1] , [S2] tags to indicate speakers Include non-verbal cues: (laughs) , (coughs) , etc. Optionally add voice conditioning via audio prompts Best Practices : Input length: keep within 5–20 seconds to avoid unnatural speech Alternate speaker tags properly Use optional seeds or audio prompts for consistent voice output 4. Python Library Example from dia.model import Dia model = Dia.from_pretrained("nari-labs/Dia-1.6B", compute_dtype="float16") script = "[S1] Hello! (laughs) [S2] Hi there!" output = model.generate(script, use_torch_compile=True, verbose=True) model.save_audio("dialogue.mp3", output) For zero-shot voice cloning, preface the transcript with a short audio clip (5–10 seconds) before the dialogue. ✅ Advantages Ultra-realistic dialogue : Handles multiple speakers and emotional tone Non-verbal expressions : Includes laughter, coughs, sighs, rare in open-source TTS Voice cloning & conditioning : Supports zero-shot cloning and seed-based consistency Open-source (Apache 2.0) : Accessible code, models, and community support ⚠️ Disadvantages English only (no multilingual support yet) Hardware requirements are high : Needs GPU (16 GB+ RAM) for decent performance Inconsistent results : Outputs vary unless audio prompts or seeds are used Ethical considerations : Voice cloning could be misused; users must follow usage guidelines 🎯 Use Cases Podcasts & Audiobooks : Multi-character dialogue without hiring voice actors Education : Simulated roleplay for language learners Game NPCs : Dynamic dialogue generation with character-specific voices Accessibility : Personalized TTS for speech-impaired users Research : Experimenting with open-source, expressive TTS 🔧 Tips & Best Practices Input length: aim for 5–20 seconds Alternate speaker tags properly Use (non-verbal tags) sparingly to maintain realism Test seed-based cloning for voice consistency Monitor VRAM use; GPU support recommended 📝 Conclusion DIA by Nari Labs is a breakthrough open-source TTS framework capable of generating natural multi-speaker dialogues , infused with emotion and non-verbal cues. Lightweight yet powerful, it offers developers an exceptional toolkit for voice systems—charges-free under Apache 2.0. While it requires decent hardware and currently supports English only, its open-source nature and feature-rich setup make it a go-to for modern TTS applications.