Instructions to use khmerttsopensource/khmer-tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khmerttsopensource/khmer-tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="khmerttsopensource/khmer-tts")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("khmerttsopensource/khmer-tts") model = AutoModelForPreTraining.from_pretrained("khmerttsopensource/khmer-tts") - Notebooks
- Google Colab
- Kaggle
Training Artifacts
This folder contains reproducibility notes and the training hyperparameters used to produce the published model.
khmer_tts_training.yamlis the project-level training config.finetune_mms_khm.jsonis the generated VITS fine-tuning config with dataset paths replaced by placeholders.
The raw training audio, internal automation scripts, and local intermediate training states are intentionally excluded from the Hugging Face release folder. Exact reproduction requires a Khmer audio/text dataset with the same format.
To fine-tune further, replace the placeholder dataset paths with your own Hugging Face AudioFolder dataset or a compatible local dataset.