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
python
1import outetts
23# Configure the model4model_config = outetts.HFModelConfig_v2(5 model_path="OuteAI/OuteTTS-0.3-1B",6 tokenizer_path="OuteAI/OuteTTS-0.3-1B"7)8# Initialize the interface9interface = outetts.InterfaceHF(model_version="0.3", cfg=model_config)1011# You can create a speaker profile for voice cloning, which is compatible across all backends.12# speaker = interface.create_speaker(audio_path="path/to/audio/file.wav")13# interface.save_speaker(speaker, "speaker.json")14# speaker = interface.load_speaker("speaker.json")1516# Print available default speakers17interface.print_default_speakers()18# Load a default speaker19speaker = interface.load_default_speaker(name="en_male_1")2021# Generate speech22gen_cfg = outetts.GenerationConfig(23 text="Speech synthesis is the artificial production of human speech.",24 temperature=0.1,25 repetition_penalty=1.1,26 max_length=4096,27 speaker=speaker,28)29output = interface.generate(config=gen_cfg)3031# Save the generated speech to a file32output.save("output.wav")
Additional Usage Examples
[!IMPORTANT]
For additional usage examples and recommendations, visit the: GitHub repository.
Generation Performance
[!IMPORTANT]
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.
Dataset Attribution
The OuteTTS-0.3-500M training data incorporates various publicly available speech datasets. Below is a summary of the key data sources:
