Text-to-Speech
Transformers
ONNX
Safetensors
English
Chinese
bailing_moe_v3_audio
feature-extraction
audio
speech
automatic-speech-recognition
audio-understanding
audio-reasoning
speech-to-text
conversational
mixture-of-experts
custom_code
Instructions to use inclusionAI/Ming-3.0-audio-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ming-3.0-audio-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="inclusionAI/Ming-3.0-audio-flash", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/Ming-3.0-audio-flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Use official GitHub logo
Browse files
README.md
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Ming-3.0-audio-flash is a native audio-language model from inclusionAI. It accepts audio together with text instructions and supports speech recognition, spoken question answering, audio understanding, audio reasoning, and optional synthesized-speech responses.
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## Model Summary
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Ming-3.0-audio-flash is a native audio-language model from inclusionAI. It accepts audio together with text instructions and supports speech recognition, spoken question answering, audio understanding, audio reasoning, and optional synthesized-speech responses.
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<p align="center">
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<a href="https://github.com/inclusionAI/Ming-3.0-audio-flash"><img src="assets/github-invertocat-black.png" width="16" alt=""> GitHub</a> 路
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<a href="https://modelscope.cn/models/inclusionAI/Ming-3.0-audio-flash">馃 ModelScope</a> 路
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<a href="https://huggingface.co/inclusionAI/Ming-3.0-audio-flash">馃 Hugging Face</a> 路
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<a href="https://github.com/inclusionAI/Ming-3.0-audio-flash/tree/main/examples">馃捇 Examples</a>
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</p>
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## Model Summary
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