Text-to-Speech
Transformers
Safetensors
Arabic
moss_tts_local
feature-extraction
voice-cloning
custom_code
sglang-omni
moss-tts
moss-tts-local
lora
saudi-arabic
Instructions to use Rabe3/Moss-Saudi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rabe3/Moss-Saudi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Rabe3/Moss-Saudi", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Rabe3/Moss-Saudi", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| *.bin filter=lfs diff=lfs merge=lfs -text | |
| *.safetensors filter=lfs diff=lfs merge=lfs -text | |
| *.pt filter=lfs diff=lfs merge=lfs -text | |
| *.pth filter=lfs diff=lfs merge=lfs -text | |
| *.ckpt filter=lfs diff=lfs merge=lfs -text | |
| *.onnx filter=lfs diff=lfs merge=lfs -text | |
| *.wav filter=lfs diff=lfs merge=lfs -text | |
| *.flac filter=lfs diff=lfs merge=lfs -text | |
| *.mp3 filter=lfs diff=lfs merge=lfs -text | |
| lora_adapter/tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |