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svc_preprocess_speaker_lora.py
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svc_preprocess_speaker_lora.py
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import os
import argparse
import numpy as np
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.description = 'please enter embed parameter ...'
parser.add_argument("dataset_path", type=str,
help="Path to dataset waves.")
data_svc = parser.parse_args().dataset_path
if os.path.isdir(os.path.join(data_svc, "speaker")):
subfile_num = 0
speaker_ave = 0
for file in os.listdir(os.path.join(data_svc, "speaker")):
if file.endswith(".npy"):
source_embed = np.load(os.path.join(data_svc, "speaker", file))
source_embed = source_embed.astype(np.float32)
speaker_ave = speaker_ave + source_embed
subfile_num = subfile_num + 1
speaker_ave = speaker_ave / subfile_num
print(speaker_ave)
np.save(os.path.join(data_svc, "lora_speaker.npy"),
speaker_ave, allow_pickle=False)
if os.path.isdir(os.path.join(data_svc, "pitch")):
subfile_num = 0
speaker_ave = 0
speaker_max = 0
speaker_min = 1000
for file in os.listdir(os.path.join(data_svc, "pitch")):
if file.endswith(".npy"):
pitch = np.load(os.path.join(data_svc, "pitch", file))
pitch = pitch.astype(np.float32)
pitch = pitch[pitch > 0]
speaker_ave = speaker_ave + pitch.mean()
subfile_num = subfile_num + 1
if (speaker_max < pitch.max()):
speaker_max = pitch.max()
print(f'{file} has {speaker_max}')
if (speaker_min > pitch.min()):
speaker_min = pitch.min()
print(f'{file} has {speaker_min}')
speaker_ave = speaker_ave / subfile_num
pitch_statics = [speaker_ave, speaker_min, speaker_max]
print(pitch_statics)
np.save(os.path.join(data_svc, "lora_pitch_statics.npy"),
pitch_statics, allow_pickle=False)