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.yaml` is the project-level training config. | |
| - `finetune_mms_khm.json` is 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. | |