Instructions to use OuteAI/OuteTTS-0.3-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OuteAI/OuteTTS-0.3-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OuteAI/OuteTTS-0.3-1B-GGUF",
	filename="OuteTTS-0.3-1B-FP16.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OuteAI/OuteTTS-0.3-1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use OuteAI/OuteTTS-0.3-1B-GGUF with Ollama:
```
ollama run hf.co/OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use OuteAI/OuteTTS-0.3-1B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OuteAI/OuteTTS-0.3-1B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OuteAI/OuteTTS-0.3-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OuteAI/OuteTTS-0.3-1B-GGUF to start chatting

Docker Model Runner
How to use OuteAI/OuteTTS-0.3-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M
```

Lemonade

How to use OuteAI/OuteTTS-0.3-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OuteAI/OuteTTS-0.3-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OuteTTS-0.3-1B-GGUF-Q4_K_M

List all available models

lemonade list

Oute A I

🌐 OuteAI.com 💬 Join our Discord 𝕏 @OuteAI

OuteTTS 0.3 1B OuteTTS 0.3 1B GGUF OuteTTS 0.3 500M OuteTTS 0.3 500M GGUF OuteTTS 0.3 Demo Space GitHub - OuteTTS

OuteTTS Version 0.3

OuteTTS version 0.3 introduces multiple model variants tailored for diverse use cases. This release significantly enhances the naturalness and coherence of speech synthesis by adding punctuation support, improving the flow and clarity of generated speech. The following punctuation marks are supported: '.', '!', '?', ',', '"', '„', '¡', '¿', '…', '...', '。', '！', '？', '，', '؟'. These are converted into special tokens, for instance, . is transformed into <|period|>. Additionally, the models were trained on refined and extended datasets, offering broader linguistic coverage. With this version, two new languages, German (de) and French (fr), are supported, bringing the total to six languages: English (en), Japanese (jp), Korean (ko), Chinese (zh), French (fr), and German (de).

OuteTTS is a solution designed to extend any existing large language model (LLM) with text-to-speech (TTS) and speech-to-speech capabilities. By preserving the original architecture, it ensures high compatibility with a broad range of libraries and tools, making it easy to integrate speech functionalities without compromising on flexibility.

Experimental voice control features are also included, though they are in very early stage of development. Due to limited data, these features may produce inconsistent results and might sometimes be ignored by the model.

Special thanks to Hugging Face 🤗 for providing the GPU grant that made training this model possible!

Available Variants

OuteTTS-0.3-500M

Base: Qwen2.5-0.5B (Apache-2.0)
TTS Model License: CC-BY-SA-4.0
Training: 10,000 hours of speech audio (~4 billion tokens)
Supported Languages: en, jp, ko (small dataset), zh, fr, de

OuteTTS-0.3-1B

Base: OLMo-1B (Apache-2.0)
TTS Model License: CC-BY-NC-SA-4.0 (Incorporates the Emilia dataset, for improved quality)
Training: 20,000 hours of speech audio (~8 billion tokens)
Supported Languages: en, jp, ko, zh, fr, de

Showcase Video

Installation

Install the OuteTTS package via pip:

pip install outetts --upgrade

Usage

Quick Start: Full Basic Example

import outetts

# Configure the model
model_config = outetts.HFModelConfig_v2(
    model_path="OuteAI/OuteTTS-0.3-1B",
    tokenizer_path="OuteAI/OuteTTS-0.3-1B"
)
# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.3", cfg=model_config)

# You can create a speaker profile for voice cloning, which is compatible across all backends.
# speaker = interface.create_speaker(audio_path="path/to/audio/file.wav")
# interface.save_speaker(speaker, "speaker.json")
# speaker = interface.load_speaker("speaker.json")

# Print available default speakers
interface.print_default_speakers()
# Load a default speaker
speaker = interface.load_default_speaker(name="en_male_1")

# Generate speech
gen_cfg = outetts.GenerationConfig(
    text="Speech synthesis is the artificial production of human speech.",
    temperature=0.4,
    repetition_penalty=1.1,
    max_length=4096,
    speaker=speaker,
)
output = interface.generate(config=gen_cfg)

# Save the generated speech to a file
output.save("output.wav")

Additional Usage Examples

For additional usage examples and recommendations, visit the: GitHub repository.

Generation Performance

The model performs best with 30-second generation batches. This window is reduced based on the length of your speaker samples. For example, if the speaker reference sample is 10 seconds, the effective window becomes approximately 20 seconds. I am currently working on adding batched generation capabilities to the library, along with further improvements that are not yet implemented.