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 eshmoideas/Ming-audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eshmoideas/Ming-audio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="eshmoideas/Ming-audio", 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("eshmoideas/Ming-audio", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download talker/__init__.py from eshmoideas/Ming-audio: direct link, hf CLI and curl.
- Browser
- Download file 119 Bytes
-
https://huggingface.co/eshmoideas/Ming-audio/resolve/main/talker/__init__.py
- Command line
-
hf download hf://eshmoideas/Ming-audio/talker/__init__.py
-
curl -L -o __init__.py https://huggingface.co/eshmoideas/Ming-audio/resolve/main/talker/__init__.py
119 Bytes
| """Native Ming Talker inference and checkpoint assets.""" | |
| from .inference import MingTalker | |
| __all__ = ["MingTalker"] | |