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
| { | |
| "lora": true, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "lora_scope": "all", | |
| "lora_target_modules": "auto", | |
| "lora_target_modules_resolved": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj", | |
| "c_attn", | |
| "c_proj", | |
| "fc_in", | |
| "fc_out" | |
| ], | |
| "trainable_parameters": 33669120, | |
| "learning_rate": 0.0001, | |
| "lr_scheduler_type": "constant", | |
| "num_epochs": 3, | |
| "per_device_batch_size": 4, | |
| "gradient_accumulation_steps": 4, | |
| "mixed_precision": "bf16", | |
| "n_vq": null, | |
| "validation_split_ratio": 0.01, | |
| "best_val_loss": 4.311636394729263, | |
| "saved_as_best": true, | |
| "saved_epoch": 1, | |
| "saved_global_step": 6716, | |
| "saved_at": "2026-06-28 17:05:52", | |
| "base_model": "OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5", | |
| "codec_model": "OpenMOSS-Team/MOSS-Audio-Tokenizer-v2", | |
| "artifact": "LoRA adapter plus merged full model weights" | |
| } | |