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OuteAI/
Llama-OuteTTS-1.0-1B-FP8

Name: Llama-OuteTTS-1.0-1B-FP8
Rating: 2.5 (7 reviews)

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Llama OuteTTS 1.0 1B Llama OuteTTS 1.0 1B GGUF Llama OuteTTS 1.0 1B FP8 Llama OuteTTS 1.0 1B 8bpw GitHub Library

[!IMPORTANT] Important Sampling Considerations

When using OuteTTS version 1.0, it is crucial to use the settings specified in the Sampling Configuration section. The repetition penalty implementation is particularly important - this model requires penalization applied to a 64-token recent window, rather than across the entire context window. Penalizing the entire context will cause the model to produce broken or low-quality output.

To address this limitation, all necessary samplers and patches for all backends are set up automatically in the outetts library. If using a custom implementation, ensure you correctly implement these requirements.

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.

What's New

1. Prompt Revamp & Dependency Removal

Automatic Word Alignment: The model now performs word alignment internally. Simply input raw text—no pre-processing required—and the model handles the rest, streamlining your workflow. For optimal results, use normalized, readable text without newlines (light normalization is applied automatically in outetts library).
Native Multilingual Text Support: Direct support for native text across multiple languages eliminates the need for romanization.
Enhanced Metadata Integration: The updated prompt system incorporates additional metadata (time, energy, spectral centroid, pitch) at both global and word levels, improving speaker flow and synthesis quality.
Special Tokens for Audio Codebooks: New tokens for c1 (codebook 1) and c2 (codebook 2).

2. New Audio Encoder Model

DAC Encoder: Integrates a DAC audio encoder from ibm-research/DAC.speech.v1.0, utilizing two codebooks for high quality audio reconstruction.
Performance Trade-off: Improved audio fidelity increases the token generation rate from 75 to 150 tokens per second. This trade-off prioritizes quality, especially for multilingual applications.

3. Voice Cloning

One-Shot Voice Cloning: To achieve one-shot cloning, the model typically requires only around 10 seconds of reference audio to produce an accurate voice representation.
Improved Accuracy: Enhanced by the new encoder and additional training metadata, voice cloning is now more natural and precise.

4. Auto Text Alignment & Numerical Support

Automatic Text Alignment: Aligns raw text at the word level, even for languages without clear boundaries (e.g., Japanese, Chinese), using insights from pre-processed training data.
Direct Numerical Input: Built-in multilingual numerical support allows direct use of numbers in prompts—no textual conversion needed. (The model typically chooses the dominant language present. Mixing languages in a single prompt may lead to mistakes.)

5. Multilingual Capabilities

Supported Languages: OuteTTS offers varying proficiency levels across languages, based on training data exposure.
High Training Data Languages: These languages feature extensive training: English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Korean, Lithuanian, Russian, Spanish
Moderate Training Data Languages: These languages received moderate training, offering good performance with occasional limitations: Portuguese, Belarusian, Bengali, Georgian, Hungarian, Latvian, Persian/Farsi, Polish, Swahili, Tamil, Ukrainian
Beyond Supported Languages: The model can generate speech in untrained languages with varying success. Experiment with unlisted languages, though results may not be optimal.

Video Showcase

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 Usage

python

import outetts

# Initialize the interface
interface = outetts.Interface(
    config=outetts.ModelConfig.auto_config(
        model=outetts.Models.VERSION_1_0_SIZE_1B,
        # For llama.cpp backend
        backend=outetts.Backend.LLAMACPP,
        quantization=outetts.LlamaCppQuantization.FP16
        # For transformers backend
        # backend=outetts.Backend.HF,
    )
)

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

# Or create your own speaker profiles in seconds and reuse them instantly
# speaker = interface.create_speaker("path/to/audio.wav")
# interface.save_speaker(speaker, "speaker.json")
# speaker = interface.load_speaker("speaker.json")

# Generate speech
output = interface.generate(
    config=outetts.GenerationConfig(
        text="Hello, how are you doing?",
        generation_type=outetts.GenerationType.CHUNKED,
        speaker=speaker,
        sampler_config=outetts.SamplerConfig(
            temperature=0.4
        ),
    )
)

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

⚡ Batch Setup

python

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/Llama-OuteTTS-1.0-1B-FP8",
            tokenizer_path="OuteAI/Llama-OuteTTS-1.0-1B",
            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:

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 subpar, check the encoded speaker sample.

interface.decode_and_save_speaker(speaker=your_speaker, path="speaker.wav")

The DAC audio reconstruction model is lossy, and samples with clipping, excessive loudness, or unusual vocal features may introduce encoding issues that impact output quality.

Sampling Configuration

For optimal results with this TTS model, use the following sampling settings.

Parameter	Value
Temperature	0.4
Repetition Penalty	1.1
Repetition Range	64
Top-k	40
Top-p	0.9
Min-p	0.05

Model Specifications

Training Data: Trained on ~60k hours of audio
Context Length: Supports a maximum context window of 8,192 tokens

Training Parameters

Pre-Training

Optimizer: AdamW
Batch Size: 1 million tokens
Max Learning Rate: 3e-4
Min Learning Rate: 3e-5
Context Length: 8192

Fine-Tuning

Optimizer: AdamW
Max Learning Rate: 1e-5
Min Learning Rate: 5e-6
Data: 10,000 diverse, high-quality examples

License Information

Initial Llama3.2 Components: Llama 3.2 Community License Agreement
Our Continued Pre-Training, Fine-Tuning, and Additional Components: CC-BY-NC-SA-4.0

Acknowledgments

Big thanks to Hugging Face for their continued resource support through their grant program!
Audio encoding and decoding utilize ibm-research/DAC.speech.v1.0
OuteTTS is built with Llama3.2-1B as the base model, with continued pre-training and fine-tuning.

Ethical Use Guidelines

This text-to-speech model is intended for legitimate applications that enhance accessibility, creativity, and communication; prohibited uses include impersonation without consent, creation of deliberately misleading content, generation of harmful or harassing material, distribution of synthetic audio without proper disclosure, voice cloning without permission, and any uses that violate applicable laws, regulations, or copyrights.