Automatic Speech Recognition
Transformers
asr
speaker-diarization
timestamps
quantization
low-bit
arm
on-device
Instructions to use yongyizang/TinyMOSS-Diarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yongyizang/TinyMOSS-Diarize with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yongyizang/TinyMOSS-Diarize")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yongyizang/TinyMOSS-Diarize", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 519 Bytes
7ccb33d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | """Deployment export and reload helpers for the fixed MOSS quantized topology."""
from importlib import import_module
from typing import Any
__all__ = [
"build_export_plan",
"export_model",
"load_export",
"rebuild_cpu_model",
"verify_export",
]
def __getattr__(name: str) -> Any:
"""Load public helpers lazily so ``python -m export.export_model`` is clean."""
if name not in __all__:
raise AttributeError(name)
return getattr(import_module(".export_model", __name__), name)
|