o i @s.dZ     d dededed efd d Zd S)aAudio Partitioning Tool for CTC Segmentation. Sometimes, The inference of the model is too slow and uses too much memory. Use RNN-based ASR networks that grow linearly in complexity with longer audio files. Inference complexity increases on long audio files quadratically for Transformer-based architectures. To solve this, it is possible to partition the audio into several parts. This file includes a helper function. 順@>t max_len_sfsoverlapcCsh|d}t||}t||}|||8}|||8}ttd|}|||d|kr;td|d|d|dg}g} d} t||} d} ||kr| ||} ttd| } ||d|d}t| |}|| |fg7}| | 7} t|D]}| | || d|g7} qy||8}| |7} ||ksM| ||} ttd| } || d fg7}||| ||||d }|S) aObtain partitions. Note that this is implemented for frontends that discard trailing data. Note that the partitioning strongly depends on your architecture. A note on audio indices: Based on the ratio of audio sample points to lpz indices (here called frame), the start index of block N is: 0 + N * samples_to_frames_ratio Due to the discarded trailing data, the end is then in the range of: [N * samples_to_frames_ratio - 1 .. (1+N) * samples_to_frames_ratio] g?rz5Pick a larger time value for partitions. time value: z , overlap: z , ratio: .N) partitionsr delete_overlap_listsamples_to_frames_ratio max_length cut_length cut_time_s)intmax ValueErrorrange)rrrrr rrrr duplicate_framescumulative_lpz_lengthcut_length_lpz_framespartition_startstartendipartition_dictrQ/home/ubuntu/.local/lib/python3.10/site-packages/ctc_segmentation/partitioning.pyget_partitionss`           r!N)rrrrr)__doc__rfloatr!rrrr s