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
| """Sherry and mild RTN weight-quantization helpers.""" | |
| from .pack_stq1 import PackedSTQ1, pack, unpack | |
| from .pack_rtn import PackedRTN, pack_rtn, unpack_rtn | |
| from .rtn_quant import RTNEmbedding, RTNLinear, TiedRTNLMHead, rtn_quantize, tie_rtn_lm_head | |
| from .sherry_quant import ArenasScheduler, SherryLinear, nm_quantize | |
| from .wrap_model import freeze_non_quantized, wrap_rtn, wrap_sherry | |
| __all__ = [ | |
| "ArenasScheduler", | |
| "PackedSTQ1", | |
| "PackedRTN", | |
| "RTNEmbedding", | |
| "RTNLinear", | |
| "TiedRTNLMHead", | |
| "SherryLinear", | |
| "freeze_non_quantized", | |
| "nm_quantize", | |
| "pack", | |
| "pack_rtn", | |
| "rtn_quantize", | |
| "tie_rtn_lm_head", | |
| "unpack", | |
| "unpack_rtn", | |
| "wrap_rtn", | |
| "wrap_sherry", | |
| ] | |