How to Run and Use Dia-1.6B Locall

About 9 min

How to Run and Use Dia-1.6B Locally - A Complete Guide

Ever been frustrated with robotic-sounding text-to-speech voices? Or perhaps you're tired of paying subscription fees for cloud-based TTS services with limited customization? I certainly was, until I discovered Dia-1.6B - a game-changing open-source model that's redefining what's possible with text-to-speech technology.

When I first heard audio samples generated by Dia-1.6B, I couldn't believe it was machine-generated. The natural pauses, emotional inflections, and even non-verbal cues like laughter and throat clearing sounded genuinely human. After spending a week testing it on various scripts, from simple narrations to complex multi-character dialogues, I'm convinced this is one of the most impressive open-source TTS solutions available today.

In this guide, I'll walk you through everything you need to know about running Dia-1.6B on your local machine, from setup to advanced usage techniques. By the end, you'll be generating studio-quality dialogue right from your own computer, with complete control and privacy.

What is Dia-1.6B?

Dia-1.6B is a groundbreaking text-to-speech model developed by Nari Labs, a small team of dedicated researchers. Unlike traditional TTS models that focus on single-voice narration, Dia is purpose-built for dialogue generation. With its 1.6 billion parameters, this model can directly convert written scripts into realistic conversational speech, complete with natural inflections, pacing, and even non-verbal elements.

Released under the Apache 2.0 license, Dia-1.6B offers a compelling open-source alternative to proprietary solutions like ElevenLabs Studio and Sesame CSM-1B. What makes it particularly special is its ability to:

Generate dynamic, multi-speaker conversations with distinct voices
Produce non-verbal sounds (laughs, coughs, sighs) when instructed in text
Clone voices from audio samples for consistent speech generation
Control emotional tone and delivery through audio conditioning

At its core, Dia-1.6B represents a significant advance in democratizing high-quality speech synthesis technology. The completely open nature of the model means you can run it locally without internet connectivity, avoid subscription fees, and maintain full privacy over your content.

Hardware and Software Requirements

Before diving into installation, let's make sure your system is ready to run Dia-1.6B. While the model is remarkably efficient for its capabilities, it does have some specific requirements.

Hardware Requirements

Running a 1.6 billion parameter model locally isn't trivial, but you don't need a supercomputer either. Here's what you'll need:

Component	Minimum Requirement	Recommended
GPU	NVIDIA GPU with CUDA support	RTX 3070/4070 or better
VRAM	8GB (with some limitations)	10GB+
RAM	16GB	32GB
Storage	10GB free space	20GB+ SSD
CPU	Quad-core	6+ cores

The most critical component is your GPU. While I managed to get Dia-1.6B running on an older GTX 1080 Ti with 11GB VRAM, the generation was noticeably slower compared to a more modern RTX 3080. If you don't have a suitable GPU, you might consider using Hugging Face's ZeroGPU Space to try the model online, or wait for the planned CPU support in future updates.

Software Prerequisites

For a smooth installation, you'll need:

Operating System: Windows 10/11, macOS (M1/M2/M3 with MPS), or Linux
Python: Version 3.8 or newer (I used Python 3.10 with excellent results)
CUDA Toolkit: Version 12.6 (for NVIDIA GPUs)
Git: For cloning the repository
Virtual Environment Manager: Either venv, conda, or uv (recommended)

I found that using the uv package manager significantly simplified the setup process, so I'll include instructions for both the standard approach and the uv approach.

Installing Dia-1.6B Locally

Now that we know what we need, let's get Dia-1.6B up and running on your machine. I'll guide you through each step of the process.

Step 1: Clone the Repository

First, we need to get the code from GitHub. Open a terminal or command prompt and run:

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

This will create a new directory called "dia" containing all the necessary code.

Step 2: Set Up the Environment

You have two options here. The simplest approach uses uv, which I highly recommend:

Option A: Using uv (Recommended)

If you don't have uv installed, you can get it with:

pip install uv

Then, with a single command, uv will handle everything:

uv run app.py

This automatically creates a virtual environment, installs all dependencies, and launches the Gradio interface. When I tried this method, it completed in about 5 minutes on a decent internet connection.

Option B: Manual Setup

If you prefer the traditional approach:

# Create a virtual environment
python -m venv .venv

# Activate the environment
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

# Install dependencies
pip install -e .

# Run the application
python app.py

When I first tried this approach, I ran into a dependency conflict with an older library on my system. If you encounter similar issues, try creating a fresh virtual environment in a different directory.

Step 3: First Launch

The first time you run Dia-1.6B, it will download the model weights from Hugging Face (approximately 3GB) and also fetch the Descript Audio Codec. This might take several minutes depending on your internet speed.

Once everything is downloaded, you should see output in your terminal indicating that the Gradio server is running, with a URL like http://127.0.0.1:7860. Open this URL in your web browser to access the interface.

If all goes well, you'll see the Dia-1.6B Gradio interface, ready to generate speech from your scripts!

Using Dia-1.6B with the Gradio Interface

The Gradio interface provides an intuitive way to interact with Dia-1.6B. Let's explore how to use it effectively.

Basic Text-to-Speech Generation

To generate your first dialogue:

In the text input field, enter a script using speaker tags to indicate different speakers:

[S1] Welcome to Dia, an incredible text-to-speech model. [S2] It can generate realistic dialogue with multiple speakers. [S1] And it even handles non-verbal cues like laughter! (laughs)

Click the "Generate" button and wait for processing to complete.
Once finished, you can play the audio using the provided controls or download it for later use.

When I first tested this, I was surprised by how well Dia handled the speaker transitions and the natural-sounding laugh at the end. The voices were distinct for each speaker, though they'll change with each generation unless you provide audio prompting or set a fixed seed.

Working with Speaker Tags and Non-verbal Cues

Dia-1.6B uses a simple notation system:

Speaker Tags: Use [S1], [S2], etc., to indicate different speakers
Non-verbal Cues: Place descriptions like (laughs), (coughs), or (sighs) within parentheses

For example:

[S1] Did you hear that joke? (laughs) It was hilarious! [S2] (clears throat) I don't think I got it. Can you explain? [S1] (sighs) Never mind.

The model will interpret these cues and generate appropriate sounds, creating a truly immersive dialogue experience.

Voice Cloning with Audio Prompts

One of the most powerful features of Dia-1.6B is its ability to clone voices from audio samples. Here's how to use it:

Prepare an audio file of the voice you want to clone (MP3 or WAV format)
In the Gradio interface, upload your audio file to the "Audio Prompt" section
In the "Transcript of Audio Prompt" field, enter the exact text of what's said in the audio
Add your new script in the main text field
Generate as usual

The model will analyze your audio sample and condition its output to match the voice characteristics. I've had the best results with clear, high-quality recordings of at least 10-15 seconds in length.

It's worth noting that the voice cloning isn't perfect - there can be some drift over longer generations - but it's remarkably effective for maintaining consistent character voices across multiple generations.

Integrating Dia-1.6B into Python Applications

While the Gradio interface is convenient for experimentation, you might want to integrate Dia-1.6B into your own Python applications. Fortunately, the model is easily accessible as a Python library.

Basic Integration Example

Here's a simple example to get you started:

import soundfile as sf
from dia.model import Dia

# Load the model
model = Dia.from_pretrained("nari-labs/Dia-1.6B")

# Define your script
text = "[S1] Python integration with Dia is really straightforward. [S2] Yes, you can add it to any application. [S1] That's amazing! (laughs)"

# Generate audio
output = model.generate(text)

# Save to file
sf.write("output.wav", output, 44100)
print("Audio generated and saved to output.wav")

This code loads the model, generates speech from your script, and saves it as a WAV file. When running this for the first time, you might notice it takes a moment to initialize, but subsequent generations are much faster.

Advanced Voice Cloning in Python

For more control over voice cloning, you can use the audio_prompt_path parameter:

import soundfile as sf
from dia.model import Dia

# Load the model
model = Dia.from_pretrained("nari-labs/Dia-1.6B")

# Audio prompt details
clone_from_audio = "your_voice_sample.mp3"
clone_from_text = "[S1] This is the transcript of my voice sample that I want to clone."

# New script to generate with the cloned voice
new_script = "[S1] This will sound like the voice in my audio sample. [S2] But this will be a different voice altogether."

# Generate with voice cloning
output = model.generate(
    clone_from_text + new_script,  # Combine prompt transcript and new script
    audio_prompt_path=clone_from_audio
)

# Save to file
sf.write("cloned_voice.wav", output, 44100)

I found this approach particularly useful for maintaining character consistency across multiple generated files for a podcast project I was working on.

Batch Processing Multiple Scripts

If you need to process multiple scripts, it's more efficient to load the model once and reuse it:

import soundfile as sf
from dia.model import Dia

# Load the model once
model = Dia.from_pretrained("nari-labs/Dia-1.6B")

# List of scripts to process
scripts = [
    "[S1] This is the first script. [S2] With multiple speakers.",
    "[S1] Here's another example. [S2] Dia handles them all efficiently.",
    "[S1] Processing in batch is much faster. [S2] Agreed! (laughs)"
]

# Process each script
for i, script in enumerate(scripts):
    output = model.generate(script)
    sf.write(f"output_{i+1}.wav", output, 44100)
    print(f"Generated output_{i+1}.wav")

This approach saves significant time by avoiding repeated model loading.

Advanced Techniques and Optimizations

After experimenting with Dia-1.6B for several days, I discovered some techniques to get the most out of the model.

Improving Voice Consistency

Since Dia-1.6B wasn't fine-tuned on specific voices, you might notice voice variations between generations. To improve consistency:

Fix the Random Seed: While not currently documented in the public API, you can experiment with setting PyTorch's random seed before generation
Use Longer Audio Prompts: I found that audio prompts of 20+ seconds produced more consistent voice cloning results
Maintain Speaker Patterns: Keep consistent patterns in your scripts, such as always using [S1] for the main narrator

Optimizing for Performance

To get the best performance out of Dia-1.6B:

Enable torch.compile: For compatible GPUs, this can significantly boost inference speed:

import torch
from dia.model import Dia

model = Dia.from_pretrained("nari-labs/Dia-1.6B")
# Enable compilation for faster inference
if torch.cuda.is_available() and hasattr(torch, 'compile'):
    model.generator = torch.compile(model.generator)

# Rest of your code...

Batch Similar Scripts: Process scripts with similar speakers or tones together for more consistent results
Monitor VRAM Usage: If you're experiencing out-of-memory errors, try shorter scripts or consider using a cloud instance with more memory

Creative Applications

During my testing, I found several interesting applications for Dia-1.6B:

Podcast Generation: Creating interview-style content with distinct host and guest voices
Audiobook Production: Bringing dialogue-heavy passages to life with distinct character voices
Language Learning: Generating conversational examples with natural intonation
Game Development: Creating dynamic NPC dialogues with emotional range

Comparing Dia-1.6B with Other TTS Solutions

To help you understand where Dia-1.6B fits in the TTS ecosystem, here's a comparison with other popular solutions:

Feature	Dia-1.6B	ElevenLabs	OpenAI TTS	Sesame CSM-1B
Cost	Free (Open Source)	Subscription-based	Pay-per-use	Free (Open Source)
Dialogue Support	Native multi-speaker	Limited	Limited	Basic
Non-verbal Sounds	Yes (native)	Limited	No	No
Voice Cloning	Yes	Yes (premium)	Limited	Basic
Local Deployment	Yes	No	No	Yes
Language Support	English only	29+ languages	10+ languages	English only
VRAM Required	~10GB	Cloud-based	Cloud-based	~4GB
License	Apache 2.0	Proprietary	Proprietary	Apache 2.0

While ElevenLabs and OpenAI offer more language options and don't require local hardware, Dia-1.6B stands out for its dialogue capabilities, non-verbal sound generation, and complete freedom from subscription fees. Compared to Sesame CSM-1B, Dia requires more resources but delivers noticeably better quality and more features.

After comparing numerous samples, I found Dia-1.6B consistently produced more natural dialogue transitions and emotional expressiveness than any of the alternatives.

Troubleshooting Common Issues

During my testing, I encountered a few issues that you might face as well. Here's how to resolve them:

CUDA Out of Memory Errors

Symptom: Error message about CUDA running out of memory.
Solution:

Close other GPU-intensive applications
Reduce the length of your scripts
Try running on a machine with more VRAM

Slow Generation

Symptom: Audio generation takes much longer than expected.
Solution:

Ensure you're using a CUDA-compatible GPU
Enable torch.compile as mentioned in the optimization section
Check for background processes using GPU resources

Voice Inconsistency

Symptom: Voices change dramatically between generations.
Solution:

Use audio prompting for more consistent results
Keep scripts within a similar domain or emotional range
Experiment with fixed random seeds

Installation Dependency Conflicts

Symptom: Errors during dependency installation.
Solution:

Use a fresh virtual environment
Try the uv method instead of pip
Update your CUDA toolkit and GPU drivers

Future Developments and Limitations

While Dia-1.6B is impressive, it's worth noting its current limitations and the roadmap for future improvements.

Current Limitations

English-only Support: As of now, Dia-1.6B only works with English text
GPU Dependency: No CPU support yet, making it less accessible to some users
VRAM Requirements: Needs substantial GPU memory to run efficiently
Voice Consistency: Can produce different voices across generations without prompting

Future Roadmap

According to the Nari Labs roadmap, upcoming features might include:

CPU support for broader accessibility
Quantized versions requiring less VRAM
Docker support for easier deployment
Optimized inference speed
Possibly multilingual support

Conclusion

After spending considerable time with Dia-1.6B, I'm genuinely impressed by what this small team at Nari Labs has accomplished. They've created an open-source TTS model that rivals and in some ways surpasses proprietary alternatives, particularly for dialogue generation.

Whether you're a developer looking to add realistic speech to your applications, a content creator working on podcasts or audiobooks, or just a tech enthusiast interested in cutting-edge AI, Dia-1.6B is well worth exploring.

The installation process is straightforward, the model is remarkably capable, and being able to run everything locally without subscription fees or privacy concerns is a significant advantage. Plus, with the active development and supportive community, Dia-1.6B is likely to become even more capable in the future.

I hope this guide helps you get started with Dia-1.6B. If you encounter any issues not covered here, check out the Nari Labs GitHub repository or join their Discord community for assistance. Happy generating!