Plachta
41 lines
1.7 KiB
Python
41 lines
1.7 KiB
Python
import os
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if __name__ == "__main__":
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# load sampled_audio4ft
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with open("sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
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old_annos = f.readlines()
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num_old_voices = len(old_annos)
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# load user text
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with open("./user_voice/user_voice.txt.cleaned", 'r', encoding='utf-8') as f:
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user_annos = f.readlines()
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# check how many voices are recorded
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wavfiles = [file for file in list(os.walk("./user_voice"))[0][2] if file.endswith(".wav")]
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num_user_voices = len(wavfiles)
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# user voices need to occupy 1/4 of the total dataset
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if num_user_voices:
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user_duplicate = num_old_voices // num_user_voices // 3
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else:
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user_duplicate = 0
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# find corresponding existing annotation lines
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actual_user_annos = ["./user_voice/" + line for line in user_annos if line.split("|")[0] in wavfiles]
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final_annos = old_annos + actual_user_annos * user_duplicate
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# load custom characters
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with open("custom_character_anno.txt", 'r', encoding='utf-8') as f:
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custom_character_anno = f.readlines()
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# custom character voices need to be at least equal to number of sample_audio4ft
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num_character_voices = len(custom_character_anno)
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cc_duplicate = num_old_voices // num_character_voices
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final_annos = final_annos + custom_character_anno * cc_duplicate
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# save annotation file
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with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
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for line in final_annos:
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f.write(line)
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# save annotation file for validation
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with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
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for line in actual_user_annos:
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f.write(line)
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for line in custom_character_anno:
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f.write(line)
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