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Unsloth Kaggle Sesame CSM (1B) TTS

Kaggle Sesame CSM (1B) TTS

unsloth-notebooksunslothnb
alph-notebooks/unsloth-notebooks / Kaggle-Sesame_CSM_(1B)-TTS.ipynb

To run this, press "Runtime" and press "Run all" on a free Tesla T4 Google Colab instance!

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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

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Installation

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Unsloth

FastModel supports loading nearly any model now! This includes Vision and Text models!

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==((====))==  Unsloth 2025.5.4: Fast Csm patching. Transformers: 4.52.0.dev0.
   \\   /|    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: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.
unsloth/csm-1b does not have a padding token! Will use pad_token = <|PAD_TOKEN|>.

We now add LoRA adapters so we only need to update 1 to 10% of all parameters!

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Unsloth: Making `model.base_model.model.backbone_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.

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Train the model

Now let's use Hugging Face Trainer! More docs here: Transformers docs. We do 60 steps to speed things up, but you can set num_train_epochs=1 for a full run, and turn off max_steps=None.

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GPU = Tesla T4. Max memory = 14.741 GB.
6.719 GB of memory reserved.
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==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1
   \\   /|    Num examples = 400 | Num Epochs = 2 | Total steps = 60
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 = 14,516,224/1,646,616,385 (0.88% trained)
`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.

Unsloth: Will smartly offload gradients to save VRAM!
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313.8963 seconds used for training.
5.23 minutes used for training.
Peak reserved memory = 6.719 GB.
Peak reserved memory for training = 0.0 GB.
Peak reserved memory % of max memory = 45.58 %.
Peak reserved memory for training % of max memory = 0.0 %.

Inference

Let's run the model! You can change the prompts

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Voice and style consistency

Sesame CSM's power comes from providing audio context for each speaker. Let's pass a sample utterance from our dataset to ground speaker identity and style.

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Saving, loading finetuned models

To save the final model as LoRA adapters, either use Hugging Face's push_to_hub for an online save or save_pretrained for a local save.

[NOTE] This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!

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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.

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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:

  1. Looking to use Unsloth locally? Read our Installation Guide for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.
  2. Learn how to do Reinforcement Learning with our RL Guide and notebooks.
  3. Read our guides and notebooks for Text-to-speech (TTS) and vision model support.
  4. Explore our LLM Tutorials Directory to find dedicated guides for each model.
  5. Need help with Inference? Read our Inference & Deployment page for details on using vLLM, llama.cpp, Ollama etc.

Join Discord if you need help + ⭐️ Star us on Github ⭐️

This notebook and all Unsloth notebooks are licensed LGPL-3.0