Dia: A Dialogue-to-Speech AI Model for Expressive Conversations

In the rapidly advancing field of AI, text-to-speech (TTS) models have seen major progress, and Dia by Nari Labs stands out with its 1.6 billion parameters. Designed to generate realistic dialogue from transcripts, Dia adds depth by including non-verbal cues like laughter and coughing, enhancing naturalness beyond traditional TTS. Whether you're a developer or researcher, Dia on GitHub offers powerful tools for creating nuanced, expressive speech in your projects.

👉 Check out the Dia GitHub repo to dive into the code and models, or visit the demo page for side-by-side comparisons with ElevenLabs and Sesame, plus fun audio examples.

Dia stands out in the crowded field of text-to-speech models due to its unique features that enhance user experience and output quality. One of the most notable aspects is its ability to generate dialogue using specific speaker tags, namely [S1] and [S2]. This feature allows users to create dynamic conversations that feel natural and engaging. Additionally, Dia supports the generation of non-verbal sounds, which can significantly enrich the dialogue. For instance, users can include tags for laughter, sighs, and even applause, making the generated audio more lifelike. Below is a table summarizing some of the key features of Dia:

These features not only enhance the quality of the generated speech but also provide users with the flexibility to create diverse audio outputs tailored to their specific needs.

Setting up Dia is a straightforward process, making it accessible for developers of all skill levels. To get started, users can install Dia directly from GitHub. The installation process involves cloning the repository and running a few simple commands. Here’s a step-by-step guide to help you get started:

1. Create a Folder

   Start by creating a folder (e.g., dia-setup) on your system where you'll run the setup commands. You can create the folder either manually or via the terminal commands below:

   ts
   1mkdir dia-setup

   bash
   1cd dia-setup

2. Clone the Repository: Open your terminal and run the following command:

   After navigating to the correct directory (e.g., C:/Users/username/dia-setup), run the following command in your terminal to clone the repository:

   bash
   1git clone https://github.com/nari-labs/dia.git

3. Navigate to the Cloned Directory: Change into the cloned directory:

   bash
   1cd dia

   You’re now inside the cloned project folder. Your terminal path should look something like:
   C:/Users/username/dia-setup/dia

4. Create a Conda Environment (Requires Python 3.10 or higher)

   ts
   1conda create -n dia python=3.10

   ts
   1conda activate dia

   After activating the environment, your terminal prompt will include (dia) to show that the environment is active. At this point, your terminal path should look something like: (dia) C:/Users/username/dia-setup/dia

5. Install Dependencies: Use pip to install the necessary dependencies:

   bash
   1pip install -e .

6. Enable GPU Support (CUDA)

   ts
   1pip install torch==2.6.0+cu118 torchaudio==2.6.0+cu118 torchvision==0.17.0+cu118 --index-url https://download.pytorch.org/whl/cu118

7. Run the Application: Finally, start the Gradio UI by executing:

   bash
   1python app.py

Once installed, you can begin exploring Dia's capabilities immediately within the Gradio app. The interface is simple and user-friendly, making it easy to interact with the dialogue model. Whether you're testing prompts or exploring Dia’s conversation flow, the Gradio UI offers an easy entry point into conversational AI.

Note: The first time you run python app.py, Dia will automatically download the required model. This may take some time depending on your internet speed.

Don't want to run Dia locally? You can try it out directly on Hugging Face Space — no setup needed.

Dia offers a simple and interactive Gradio interface for generating expressive spoken dialogue between multiple speakers. To get started, enter your text using speaker tags as [S1], [S2], and so on. For example:

ts
1[S1] FIRE?! OH NO, THIS IS NOT A DRILL, FOLKS! THE MICROWAVE JUST EXPLODED AGAIN! (claps)  
2[S2] OH SNAP! SOMEONE GRAB THE EMOTIONAL SUPPORT SNAKE, IT’S GOING DOWN! (inhales)  
3[S1] I WARNED YOU NOT TO REHEAT FISH (burps), KEVIN! NOW WE’RE TOAST! (laughs)  
4[S2] KEEP IT TOGETHER! STAY FREAKING CALM!—NO, GARY, YOU PANIC QUIETLY! (sighs)  
5[S1] DON’T TOUCH THAT DOOR HANDLE! UNLESS YOU WANT TO BE A CHARRED BAGEL! (groans)
6

Click "Generate" to synthesize the dialogue. Dia will produce a conversational back-and-forth audio output based on the speaker tags ([S1, [S2]).

Note: You can also upload an audio prompt (audio file) to attempt voice cloning, but be aware that this feature currently does not perform well. The generated voice sounds nothing like the uploaded reference.

To get the best results when using Dia through the Gradio app, it's important to follow some basic generation guidelines. These help maintain the quality and naturalness of the spoken dialogue and avoid unwanted artifacts or rushed delivery.

Generation Guidelines

• Keep input text at a moderate length.

• ⛔ Too short (<5s): Output sounds unnatural and abrupt.

• ⛔ Too long (>20s): Speech becomes rushed or compressed.

• Use non-verbal tags like (laughs), (coughs), etc., sparingly, and only from the official list below. Improper or excessive use may cause strange audio artifacts.

  Recognized non-verbal tags include:
  (laughs), (clears throat), (sighs), (gasps), (coughs), (singing), (sings), (mumbles), (beep), (groans), (sniffs), (claps), (screams), (inhales), (exhales), (applause), (burps), (humming), (sneezes), (chuckle), (whistles)

• Always begin your input text with [S1] and alternate between [S1], [S2], etc. Repeating the same speaker tag consecutively (e.g., [S1] ... [S1]) reduces clarity and naturalness.

• When attempting voice cloning:

  • Provide a transcript of the input audio using correct [S1]/[S2] speaker tags.

  • Use audio clips about 5–10 seconds long for best results.

  • In the Hugging Face Space, upload the audio you want to clone and place its transcript before your generation script. The model will then output only the scripted content.

✅ Strengths

• Supports multi-speaker conversational setups

• Delivers expressive speech with emotional variation

• Maintains conversational context across multiple turns

• Simple and responsive Gradio UI for quick experimentation

⚠️ Limitations & Considerations

• Speaker voices [S1], [S2] are randomly assigned and cannot be made consistent between generations

• Voice cloning is unreliable:

  • The output rarely resembles the uploaded voice

  • Generated speech is often unnaturally fast, even with well-prepared input

  • Overall, voice cloning is a weak and inconsistent feature

• Requires structured input — deviating from recommended tag formats ([S1], [S2]) or input length can degrade quality

• Limited non-verbal tag support — using unrecognized tags may lead to glitches or awkward outputs

The applications of Dia are vast and varied, making it a valuable tool across numerous industries. Its ability to generate emotionally expressive, multi-speaker dialogue opens up exciting possibilities in many fields:

Dia’s versatility and conversational strengths make it an essential asset for creators, educators, and developers looking to incorporate advanced speech generation into their projects. Whether you’re crafting immersive narratives, building educational tools, or improving customer interactions, Dia offers a compelling way to bring dialogue to life with nuance and emotion.

As we wrap up our exploration of the Dia model, it’s clear that Dia excels as a conversational dialogue-to-speech system, delivering impressively natural and emotionally expressive multi-speaker audio. Its ease of use and support for verbal and non-verbal cues make it a strong tool for applications focused on dynamic dialogue generation.

However, Dia’s capabilities are largely specialized—while it shines in capturing conversational flow and emotional nuance between speakers, it falls short in areas like consistent voice cloning and broader text-to-speech versatility. The voices assigned to speaker tags like [S1] and [S2] are random and cannot be controlled, and voice cloning currently produces results that don’t closely match the input audio.

Overall, Dia is a valuable resource for anyone aiming to generate rich, emotionally nuanced conversational speech, but it’s important to keep its limitations in mind. For those interested in pushing the boundaries of dialogue-based TTS, Dia offers an accessible and promising starting point.