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 groxaxo/MOSS-Audio-Tokenizer-backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use groxaxo/MOSS-Audio-Tokenizer-backup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="groxaxo/MOSS-Audio-Tokenizer-backup", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("groxaxo/MOSS-Audio-Tokenizer-backup", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download backup-manifest.json from groxaxo/MOSS-Audio-Tokenizer-backup: direct link, hf CLI and curl.
- Browser
- Download file 2.41 kB
-
https://huggingface.co/groxaxo/MOSS-Audio-Tokenizer-backup/resolve/main/backup-manifest.json
- Command line
-
hf download hf://groxaxo/MOSS-Audio-Tokenizer-backup/backup-manifest.json
-
curl -L -o backup-manifest.json https://huggingface.co/groxaxo/MOSS-Audio-Tokenizer-backup/resolve/main/backup-manifest.json
2.41 kB
| { | |
| "schema_version": 1, | |
| "source_repository": "OpenMOSS-Team/MOSS-Audio-Tokenizer", | |
| "purpose": "Owner-requested backup of original public model and runtime before duplicate consolidation", | |
| "files": [ | |
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| "path": "README.md", | |
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| "sha256": "795c97ec27b38994d4bee033dc3f6f0ad56763510deec3e04a5c1de385139223" | |
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| "path": "__init__.py", | |
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| "sha256": "ed0b4910a5e53b1dfc3234cfcf96e2c6890d339f9134a3dfed8481d01b3768fd" | |
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| "path": "config.json", | |
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| "path": "configuration_moss_audio_tokenizer.py", | |
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