Spark TTS (0 5B)
To run this, press "Runtime" and press "Run all" on a free Tesla T4 Google Colab instance!
To install Unsloth on your local device, follow our guide. This notebook is licensed LGPL-3.0.
You will learn how to do data prep, how to train, how to run the model, & how to save it
News
Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. Blog
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3x faster LLM training with 30% less VRAM and 500K context. 3x faster • 500K Context
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Visit our docs for all our model uploads and notebooks.
Installation
Unsloth
FastModel supports loading nearly any model now! This includes Vision and Text models!
Thank you to Etherl for creating this notebook!
==((====))== Unsloth 2025.3.19: Fast Qwen2 patching. Transformers: 4.51.3. \\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux. O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0 \ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False] "-____-" Free license: http://github.com/unslothai/unsloth Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! Unsloth: Float16 full finetuning uses more memory since we upcast weights to float32.
We now add LoRA adapters so we only need to update 1 to 10% of all parameters!
Unsloth: Making `model.base_model.model.model` require gradients
Data Prep
We will use the MrDragonFox/Elise, which is designed for training TTS models. Ensure that your dataset follows the required format: text, audio for single-speaker models or source, text, audio for multi-speaker models. You can modify this section to accommodate your own dataset, but maintaining the correct structure is essential for optimal training.
/usr/local/lib/python3.11/dist-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. WeightNorm.apply(module, name, dim)
Missing tensor: mel_transformer.spectrogram.window Missing tensor: mel_transformer.mel_scale.fb
Map: 0%| | 0/1195 [00:00<?, ? examples/s]
Moving Bicodec model and Wav2Vec2Model to cpu.
Unsloth: Tokenizing ["text"] (num_proc=2): 0%| | 0/1195 [00:00<?, ? examples/s]
GPU = Tesla T4. Max memory = 14.741 GB. 5.713 GB of memory reserved.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 \\ /| Num examples = 1,195 | Num Epochs = 1 | Total steps = 149 O^O/ \_/ \ Batch size per device = 2 | Gradient accumulation steps = 4 \ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8 "-____-" Trainable parameters = 506,634,112/506,634,112 (100.00% trained)
Generating speech for: 'Hey there my name is Elise, <giggles> and I'm a speech generation model that can sound like a person.' Generating token sequence... Token sequence generated. Found 348 semantic tokens. Found 32 global tokens. Detokenizing audio tokens... Detokenization complete. Audio saved to generated_speech_controllable.wav
('lora_model/tokenizer_config.json',
, 'lora_model/special_tokens_map.json',
, 'lora_model/vocab.json',
, 'lora_model/merges.txt',
, 'lora_model/added_tokens.json',
, 'lora_model/tokenizer.json') Saving to float16
We also support saving to float16 directly. Select merged_16bit for float16 or merged_4bit for int4. We also allow lora adapters as a fallback. Use push_to_hub_merged to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See our docs for more deployment options.
Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower. We shall switch to Pytorch saving, which might take 3 minutes and not 30 minutes. To force `safe_serialization`, set it to `None` instead. Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab. Unsloth: Will remove a cached repo with size 15.1G
Unsloth: Merging 4bit and LoRA weights to 16bit... Unsloth: Will use up to 3.99 out of 12.67 RAM for saving. Unsloth: Saving model... This might take 5 minutes ...
100%|██████████| 28/28 [00:01<00:00, 27.83it/s]
Unsloth: Saving tokenizer... Done. Unsloth: Saving model/pytorch_model-00001-of-00002.bin... Unsloth: Saving model/pytorch_model-00002-of-00002.bin... Done.
And we're done! If you have any questions on Unsloth, we have a Discord channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!
Some other resources:
- Looking to use Unsloth locally? Read our Installation Guide for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.
- Learn how to do Reinforcement Learning with our RL Guide and notebooks.
- Read our guides and notebooks for Text-to-speech (TTS) and vision model support.
- Explore our LLM Tutorials Directory to find dedicated guides for each model.
- Need help with Inference? Read our Inference & Deployment page for details on using vLLM, llama.cpp, Ollama etc.
