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 fm_utils.py from inclusionAI/Ming-3.0-tts-tiny: direct link, hf CLI and curl.
- Browser
- Download file 240 Bytes
-
https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/resolve/main/fm_utils.py
- Command line
-
hf download hf://inclusionAI/Ming-3.0-tts-tiny/fm_utils.py
-
curl -L -o fm_utils.py https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/resolve/main/fm_utils.py
240 Bytes
| def is_package_available(package_name: str) -> bool: | |
| try: | |
| import importlib | |
| package_exists = importlib.util.find_spec(package_name) is not None | |
| return package_exists | |
| except Exception: | |
| return False | |