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
Add icons to model card navigation links
Browse files
README.md
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<a href="https://github.com/inclusionAI/Ming-3.0-tts">GitHub</a> |
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<a href="https://nomorecoding.github.io/Ming-3.0-tts/">Blog & Demos</a> |
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<a href="https://modelscope.cn/models/inclusionAI/Ming-3.0-tts-tiny">ModelScope</a>
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This repository contains **Ming-3.0-tts-tiny**, the Ming-3.0-tts mixture-of-experts checkpoint built on Ling V3. Its Ling V3 backbone has 7.9B total parameters and activates about 1.3B parameters per token. The repository includes sharded weights, supporting model code, audio tokenizer, speaker encoder and text tokenizer.
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💻 <a href="https://github.com/inclusionAI/Ming-3.0-tts">GitHub</a> |
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🌐 <a href="https://nomorecoding.github.io/Ming-3.0-tts/">Blog & Demos</a> |
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🤖 <a href="https://modelscope.cn/models/inclusionAI/Ming-3.0-tts-tiny">ModelScope</a>
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</p>
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This repository contains **Ming-3.0-tts-tiny**, the Ming-3.0-tts mixture-of-experts checkpoint built on Ling V3. Its Ling V3 backbone has 7.9B total parameters and activates about 1.3B parameters per token. The repository includes sharded weights, supporting model code, audio tokenizer, speaker encoder and text tokenizer.
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