Instructions to use sesame/csm-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sesame/csm-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="sesame/csm-1b")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("sesame/csm-1b") model = AutoModelForTextToWaveform.from_pretrained("sesame/csm-1b") - Notebooks
- Google Colab
- Kaggle
How to fine tune it?
#9
by manzarimalik - opened
Any ways to fine tune this model on a specific voice?
Just pointing out, fine tuning is different from cloning.
Use this one for finetuning and training from scratch: https://github.com/knottwill/sesame-finetune
It finetunes by modifying the original weights, as opposed to the LoRA approach in the previous responses, which is optimal for domain shifts like new languages.