forked from extern/Thorsten-Voice
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
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# This script removes recordings from an ljspeech file/directory structured dataset based on CSV file from "getDatasetSpeechRate"
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# Writte by Thorsten Müller (deep-learning-german@gmx.net) and provided without any warranty.
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# https://github.com/thorstenMueller/deep-learning-german-tts/
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# https://twitter.com/ThorstenVoice
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# Changelog:
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# v0.1 - 26.09.2021 - Initial version
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import os
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import csv
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import shutil
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dataset_dir = "/Users/thorsten/Downloads/thorsten-export-20210909/" # Directory where metadata.csv is in
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subfolder_removed = "___removed"
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in_csv_file = os.path.join(dataset_dir,"speech_rate_report.csv")
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to_remove = []
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# Open metadata.csv file
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with open(os.path.join(dataset_dir,in_csv_file)) as csvfile:
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reader = csv.reader(csvfile, delimiter=';')
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for row in reader:
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if row[4] == "yes":
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# Recording in that row should be removed from dataset
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to_remove.append(row[0])
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print("Recording " + row[0] + " will be removed from dataset.")
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print("\n" + str(len(to_remove)) + " recordings has been marked for deletion.")
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if len(to_remove) > 0:
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metadata_cleaned = open(os.path.join(dataset_dir,"metadata_cleaned.csv"),"w")
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# Create new subdirectory for removed wav files
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removed_dir = os.path.join(dataset_dir,subfolder_removed)
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if not os.path.exists(removed_dir):
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os.makedirs(removed_dir)
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# Remove lines from metadata.csv and move wav files to new subdirectory
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with open(os.path.join(dataset_dir,"metadata.csv")) as csvfile:
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reader = csv.reader(csvfile, delimiter='|')
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for row in reader:
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if (row[0] + ".wav") not in to_remove:
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metadata_cleaned.write(row[0] + "|" + row[1] + "|" + row[2] + "\n")
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else:
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# Move recording to new subfolder
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shutil.move(os.path.join(dataset_dir,"wavs",row[0] + ".wav"),removed_dir)
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metadata_cleaned.close()
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