Added two scripts for dataset analysis/cleaning.

helperScripts/getDatasetSpeechRate.py

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+# This script gets speech rate per audio recording from a voice dataset (ljspeech file and directory structure)
+# Writte by Thorsten Müller (deep-learning-german@gmx.net) and provided without any warranty.
+# https://github.com/thorstenMueller/deep-learning-german-tts/
+# https://twitter.com/ThorstenVoice
+
+# Changelog:
+# v0.1 - 26.09.2021 - Initial version
+
+import os
+import librosa
+import csv
+
+dataset_dir = "/Users/thorsten/Downloads/thorsten-export-20210909/" # Directory where metadata.csv is in
+out_csv_file = os.path.join(dataset_dir,"speech_rate_report.csv")
+decimal_use_comma = True # False: Splitting decimal value with a dot (.); True: Comma (,)
+
+out_csv = open(out_csv_file,"w")
+out_csv.write("filename;audiolength_sec;number_chars;chars_per_sec;remove_from_dataset\n")
+
+# Open metadata.csv file
+with open(os.path.join(dataset_dir,"metadata.csv")) as csvfile:
+    reader = csv.reader(csvfile, delimiter='|')
+    for row in reader:
+        wav_file = os.path.join(dataset_dir,"wavs",row[0] + ".wav")
+
+        # Gather values for report.csv output
+        phrase_len = len(row[1]) - 1 # Do not count punctuation marks.
+        duration = round(librosa.get_duration(filename=wav_file),2)
+        char_per_sec = round(phrase_len / duration,2)
+
+        if decimal_use_comma:
+            duration = str(duration).replace(".",",")
+            char_per_sec = str(char_per_sec).replace(".",",")
+
+        out_csv.write(row[0] + ".wav;" + str(duration) + ";" + str(phrase_len) + ";" + str(char_per_sec) + ";no\n")
+
+out_csv.close()

helperScripts/removeFilesFromDataset.py

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