We're super super excited about this release! 🦥

You can now train Text-to-Speech (TTS) models in [Unsloth](https://github.com/unslothai/unsloth)! Training is \~1.5x faster with 50% less VRAM compared to all other setups with FA2.

* We support models like `Sesame/csm-1b`, `OpenAI/whisper-large-v3`, `CanopyLabs/orpheus-3b-0.1-ft`, and pretty much any Transformer-compatible models including LLasa, Outte, Spark, and others.
* The goal is to clone voices, adapt speaking styles and tones, support new languages, handle specific tasks and more.
* We’ve made notebooks to train, run, and save these models for free on Google Colab. Some models aren’t supported by llama.cpp and will be saved only as safetensors, but others should work. See our TTS docs and notebooks: [https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning)
* The training process is similar to SFT, but the dataset includes audio clips with transcripts. We use a dataset called ‘Elise’ that embeds emotion tags like <sigh> or <laughs> into transcripts, triggering expressive audio that matches the emotion.
* Since TTS models are usually small, you can train them using 16-bit LoRA, or go with FFT. Loading a 16-bit LoRA model is simple.

We've uploaded most of the TTS models (quantized and original) to [Hugging Face here](https://huggingface.co/collections/unsloth/text-to-speech-tts-models-68007ab12522e96be1e02155).

And here are our TTS notebooks:

|[Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_(1B)-TTS.ipynb)|​[Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb)|[Whisper Large V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb)|​[Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_(0_5B).ipynb)|
|:-|:-|:-|:-|


Thank you for reading and please do ask any questions!! 

P.S. We also now support Qwen3 GRPO. We use the base model + a new custom proximity-based reward function to favor near-correct answers and penalize outliers. Pre-finetuning mitigates formatting bias and boosts evaluation accuracy via regex matching: [https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3\_(4B)-GRPO.ipynb](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)

Text-to-Speech (TTS) Finetuning now in Unsloth!