What is the benefit of using Unsloth for TTS?

It provides 1.5x faster training speeds and 50% less memory consumption compared to standard transformer implementations, allowing for high-quality training on consumer hardware.

Which models are supported by this skill?

It is specifically optimized for Llama-based architectures like Orpheus-TTS, but generally supports any transformers-compatible TTS model supported by the Unsloth library.

Can I include emotions in the generated speech?

Yes, you can annotate transcripts with special tags like , , or which the model treats as special tokens for realistic synthesis.

How much audio data is needed for a voice clone?

While quick tests can be done with minimal data, a full replication typically requires 1-3 hours of high-quality, annotated audio data trained for one full epoch.

Can I use the fine-tuned models in llama.cpp?

Yes, since Orpheus-TTS is Llama-based, fine-tuned models and adapters can be exported to GGUF format for use in llama.cpp.

Unsloth TTS Fine-Tuning

Name: Unsloth TTS Fine-Tuning
Author: cuba6112

bycuba6112

Data Science & ML

Optimizes Text-to-Speech model training and voice cloning using the Unsloth library for faster performance and lower VRAM usage.

About

Unsloth-tts is a specialized skill designed to streamline the fine-tuning of Llama-based speech synthesis models, specifically optimized for the Orpheus-TTS architecture. It leverages Unsloth's performance optimizations to provide high-quality voice cloning and speech synthesis with 50% less memory and 1.5x faster speeds than standard implementations. This skill is ideal for developers looking to create realistic custom voices that capture emotional nuances like laughter or sighs, while maintaining compatibility with deployment tools like llama.cpp and GGUF.

Key Features

Seamless export to GGUF and llama.cpp formats
Built-in audio preprocessing and 24,000Hz sampling optimization
0 GitHub stars
Optimized Orpheus-TTS fine-tuning for Llama-based architectures
Support for emotional tag annotation (e.g., laughter, sighs)
Memory-efficient voice cloning requiring 50% less VRAM

Use Cases

Creating custom voice clones for digital avatars or narrators
Deploying specialized TTS models on consumer-grade hardware with low VRAM
Fine-tuning models to include realistic speech quirks and emotional tags

About

Key Features

Seamless export to GGUF and llama.cpp formats
Built-in audio preprocessing and 24,000Hz sampling optimization
0 GitHub stars
Optimized Orpheus-TTS fine-tuning for Llama-based architectures
Support for emotional tag annotation (e.g., laughter, sighs)
Memory-efficient voice cloning requiring 50% less VRAM

Use Cases

Creating custom voice clones for digital avatars or narrators
Deploying specialized TTS models on consumer-grade hardware with low VRAM
Fine-tuning models to include realistic speech quirks and emotional tags