Feature Extraction
Transformers
Safetensors
moss-audio-tokenizer
audio
audio-tokenizer
neural-codec
moss-tts-family
MOSS Audio Tokenizer
speech-tokenizer
trust-remote-code
custom_code
Instructions to use niobures/MOSS-Audio-Tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niobures/MOSS-Audio-Tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="niobures/MOSS-Audio-Tokenizer", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("niobures/MOSS-Audio-Tokenizer", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __init__.py from niobures/MOSS-Audio-Tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 52 Bytes
-
https://huggingface.co/niobures/MOSS-Audio-Tokenizer/resolve/main/__init__.py
- Command line
-
hf download hf://niobures/MOSS-Audio-Tokenizer/__init__.py
-
curl -L -o __init__.py https://huggingface.co/niobures/MOSS-Audio-Tokenizer/resolve/main/__init__.py
52 Bytes
| """Remote code package for Moss audio tokenizer.""" | |