Text-to-Speech
Transformers
ONNX
Safetensors
lingv3_tts
feature-extraction
audio
voice-cloning
multilingual
mixture-of-experts
speech-generation
custom_code
Instructions to use inclusionAI/Ming-3.0-tts-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ming-3.0-tts-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="inclusionAI/Ming-3.0-tts-tiny", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/Ming-3.0-tts-tiny", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __init__.py from inclusionAI/Ming-3.0-tts-tiny: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/resolve/main/__init__.py
- Command line
-
hf download hf://inclusionAI/Ming-3.0-tts-tiny/__init__.py
-
curl -L -o __init__.py https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/resolve/main/__init__.py
563 Bytes
| """Standalone Hugging Face inference helpers with lazy model imports.""" | |
| from typing import Any | |
| __all__ = ["LingV3TTSConfig", "LingV3TTSForConditionalGeneration"] | |
| def __getattr__(name: str) -> Any: | |
| if name == "LingV3TTSConfig": | |
| from .configuration_lingv3_tts import LingV3TTSConfig | |
| return LingV3TTSConfig | |
| if name == "LingV3TTSForConditionalGeneration": | |
| from .modeling_lingv3_tts import ( | |
| LingV3TTSForConditionalGeneration, | |
| ) | |
| return LingV3TTSForConditionalGeneration | |
| raise AttributeError(name) | |