import config
import utils
import glob

# split details in config.py
# > `max_audio_length_ms`, `format_long_audio_split_name`
# > `raw_audio_path`, `train_format_audio_path`
for i in glob.glob("./public_voice/*"):
    utils.split_long_wav(i)

from audio_analysis import multiple_speaker
import config
import utils
import glob
import os

for i in glob.glob(os.path.join(config.train_format_audio_path, "__split*")):
    multiple_speaker.get_speaker_dict(i)

single, overlap = utils.classify_overlap_dicts(dicts)

import config
import utils
import glob
import os

single_dicts, overlap_dicts = list(), list()

for i in glob.glob(os.path.join(config.audio_analysis_save_path, "analysis_*split*.json")):
    dicts = utils.load_json(i)
    if len(dicts) > 0:
        single, overlap = utils.classify_overlap_dicts(dicts)
        single_dicts.extend(single), overlap_dicts.extend(overlap)

utils.save_json(single_dicts, os.path.join(config.audio_analysis_save_path, "all_analysis_single.json"))
utils.save_json(overlap_dicts, os.path.join(config.audio_analysis_save_path, "all_analysis_overlap.json"))

import config
import utils
import os

dicts = utils.load_json(os.path.join(config.audio_analysis_save_path, "all_analysis_single.json"))

utils.split_audio_clips(dicts)

from audio_analysis import emotion
import config
import utils
import os

dicts = utils.load_json(os.path.join(config.audio_analysis_save_path, "all_analysis_single.json"))

pad_dicts = emotion.get_pad_dicts(dicts)