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 ml-resources/Ming-3.0-audio-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml-resources/Ming-3.0-audio-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ml-resources/Ming-3.0-audio-tiny", 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("ml-resources/Ming-3.0-audio-tiny", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ml-resources/Ming-3.0-audio-tiny: direct link, hf CLI and curl.
- Browser
- Download file 12.2 MB
-
https://huggingface.co/ml-resources/Ming-3.0-audio-tiny/resolve/main/tokenizer.json
- Command line
-
hf download hf://ml-resources/Ming-3.0-audio-tiny/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ml-resources/Ming-3.0-audio-tiny/resolve/main/tokenizer.json
12.2 MB
- Xet hash:
- 0520451c5d1ed04b320d35be96a5b83fa93ab74ff2078858025fe5d58cafe6dc
- Size of remote file:
- 12.2 MB
- SHA256:
- 2207eca933084436c15bb85ee648e28b2957934bc1da9506e10ea6c243bad869
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