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 generation_config.json from inclusionAI/Ming-3.0-tts-tiny: direct link, hf CLI and curl.
- Browser
- Download file 133 Bytes
-
https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/resolve/main/generation_config.json
- Command line
-
hf download hf://inclusionAI/Ming-3.0-tts-tiny/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/resolve/main/generation_config.json
133 Bytes
| { | |
| "temperature": 1.5, | |
| "lingv3_conditioning": "speaker_only", | |
| "lingv3_min_decode_steps": 13, | |
| "lingv3_max_decode_steps": 250 | |
| } | |