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: 724 Bytes
7ccb33d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | """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",
]
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