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.
Currently, llama.cpp delivers the most reliable and consistent output quality by default.
Both llama.cpp and EXL2 support this windowed sampling approach, while Transformers doesn't.
To address this limitation, I've implemented a windowed repetition penalty for the Hugging Face Transformers backend in the OuteTTS library,
which significantly improves output quality and resolves sampling issues, providing comparable results to llama.cpp.
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.
1import outetts
23# Initialize the interface4interface = outetts.Interface(5 config=outetts.ModelConfig.auto_config(6 model=outetts.Models.VERSION_1_0_SIZE_1B,7# For llama.cpp backend8 backend=outetts.Backend.LLAMACPP,9 quantization=outetts.LlamaCppQuantization.FP16
10# For transformers backend11# backend=outetts.Backend.HF,12)13)1415# Load the default speaker profile16speaker = interface.load_default_speaker("EN-FEMALE-1-NEUTRAL")1718# Or create your own speaker profiles in seconds and reuse them instantly19# speaker = interface.create_speaker("path/to/audio.wav")20# interface.save_speaker(speaker, "speaker.json")21# speaker = interface.load_speaker("speaker.json")2223# Generate speech24output = interface.generate(25 config=outetts.GenerationConfig(26 text="Hello, how are you doing?",27 generation_type=outetts.GenerationType.CHUNKED,28 speaker=speaker,29 sampler_config=outetts.SamplerConfig(30 temperature=0.431),32)33)3435# Save to file36output.save("output.wav")
More Configuration Options
For advanced settings and customization, visit the official repository:
🔗 interface_usage.md
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.
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
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.
