OuteTTS-1.0

OuteTTS Version 1.0

This update brings significant improvements in speech synthesis and voice cloning—delivering a more powerful, accurate, and user-friendly experience in a compact size.

OuteTTS Python Package v0.4.2

New version adds batched inference generation with the latest OuteTTS release.

⚡ Batched RTF Benchmarks

Tested with NVIDIA L40S GPU

Quick Start Guide

Getting started with OuteTTS is simple:

Installation

🔗 Installation instructions

Basic Setup

from outetts import Interface, ModelConfig, GenerationConfig, Backend, InterfaceVersion, Models, GenerationType

# Initialize the interface
interface = Interface(
    ModelConfig.auto_config(
        model=Models.VERSION_1_0_SIZE_0_6B,
        backend=Backend.HF,
    )
)

# Load the default **English** speaker profile
speaker = interface.load_default_speaker("EN-FEMALE-1-NEUTRAL")

# Or create your own speaker (Use this once)
# speaker = interface.create_speaker("path/to/audio.wav")
# interface.save_speaker(speaker, "speaker.json")

# Load your speaker from saved file
# speaker = interface.load_speaker("speaker.json")

# Generate speech & save to file
output = interface.generate(
    GenerationConfig(
        text="Hello, how are you doing?",
        speaker=speaker,
    )
)
output.save("output.wav")

⚡ Batch Setup

from outetts import Interface, ModelConfig, GenerationConfig, Backend, GenerationType

if __name__ == "__main__":
    # Initialize the interface with a batch-capable backend
    interface = Interface(
        ModelConfig(
            model_path="OuteAI/OuteTTS-1.0-0.6B-FP8",
            tokenizer_path="OuteAI/OuteTTS-1.0-0.6B",
            backend=Backend.VLLM
            # For EXL2, use backend=Backend.EXL2ASYNC + exl2_cache_seq_multiply={should be same as max_batch_size in GenerationConfig}
            # For LLAMACPP_ASYNC_SERVER, use backend=Backend.LLAMACPP_ASYNC_SERVER and provide server_host in GenerationConfig
        )
    )

    # Load your speaker profile
    speaker = interface.load_default_speaker("EN-FEMALE-1-NEUTRAL") # Or load/create custom speaker

    # Generate speech using BATCH type
    # Note: For EXL2ASYNC, VLLM, LLAMACPP_ASYNC_SERVER, BATCH is automatically selected.
    output = interface.generate(
        GenerationConfig(
            text="This is a longer text that will be automatically split into chunks and processed in batches.",
            speaker=speaker,
            generation_type=GenerationType.BATCH,
            max_batch_size=32,       # Adjust based on your GPU memory and server capacity
            dac_decoding_chunk=2048, # Adjust chunk size for DAC decoding
            # If using LLAMACPP_ASYNC_SERVER, add:
            # server_host="http://localhost:8000" # Replace with your server address
        )
    )

    # Save to file
    output.save("output_batch.wav")

More Configuration Options

For advanced settings and customization, visit the official repository:

Multilingual Capabilities

• Trained Languages: English, Chinese, Dutch, French, Georgian, German, Hungarian, Italian, Japanese, Korean, Latvian, Polish, Russian, Spanish

• Beyond Supported Languages: The model can generate speech in untrained languages with varying success. Experiment with unlisted languages, though results may not be optimal.

Usage Recommendations

Speaker Reference

The model is designed to be used with a speaker reference. Without one, it generates random vocal characteristics, often leading to lower-quality outputs. The model inherits the referenced speaker's emotion, style, and accent. When transcribing to other languages with the same speaker, you may observe the model retaining the original accent.

Multilingual Application

It is recommended to create a speaker profile in the language you intend to use. This helps achieve the best results in that specific language, including tone, accent, and linguistic features.

While the model supports cross-lingual speech, it still relies on the reference speaker. If the speaker has a distinct accent—such as British English—other languages may carry that accent as well.

Optimal Audio Length

• Best Performance: Generate audio around 42 seconds in a single run (approximately 8,192 tokens). It is recomended not to near the limits of this windows when generating. Usually, the best results are up to 7,000 tokens.
• Context Reduction with Speaker Reference: If the speaker reference is 10 seconds long, the effective context is reduced to approximately 32 seconds.

Temperature Setting Recommendations

Testing shows that a temperature of 0.4 is an ideal starting point for accuracy (with the sampling settings below). However, some voice references may benefit from higher temperatures for enhanced expressiveness or slightly lower temperatures for more precise voice replication.

Verifying Speaker Encoding

If the cloned voice quality is