Added script to create LJSpeech dataset out of Mimic-Recording-Studio recordings.

helperScripts/MRS2LJSpeech.py

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+# This script generates the folder structure for ljspeech-1.1 processing from mimic-recording-studio database
+
+# Changelog
+# v1.0  - Initial release by Thorsten Müller (https://github.com/thorstenMueller/deep-learning-german-tts)
+# v1.1  - Great improvements by Peter Schmalfeldt (https://github.com/manifestinteractive)
+#           - Audio processing with ffmpeg (mono and samplerate of 22.050 Hz)
+#           - Much better Python coding than my original version
+#           - Greater logging output to command line
+#           - See more details here: https://gist.github.com/manifestinteractive/6fd9be62d0ede934d4e1171e5e751aba
+#           - Thanks Peter, it's a great contribution :-)
+# v1.2  - Added choice for choosing which recording session should be exported as LJSpeech
+
+import glob
+import sqlite3
+import ffmpeg
+import os
+
+from shutil import copyfile
+from shutil import rmtree
+
+# Setup Directory Data
+cwd = os.path.dirname(os.path.abspath(__file__))
+mrs_dir = os.path.join(cwd, os.pardir, "mimic-recording-studio")
+output_dir = os.path.join(cwd, "dataset")
+output_dir_audio = ""
+output_dir_audio_temp=""
+output_dir_speech = ""
+
+# Create folders needed for ljspeech
+def create_folders():
+  global output_dir
+  global output_dir_audio
+  global output_dir_audio_temp
+  global output_dir_speech
+
+  print('→ Creating Dataset Folders')
+
+  output_dir_speech = os.path.join(output_dir, "LJSpeech-1.1")
+
+  # Delete existing folder if exists for clean run
+  if os.path.exists(output_dir_speech):
+    rmtree(output_dir_speech)
+
+  output_dir_audio = os.path.join(output_dir_speech, "wavs")
+  output_dir_audio_temp = os.path.join(output_dir_speech, "temp")
+
+  # Create Clean Folders
+  os.makedirs(output_dir_speech)
+  os.makedirs(output_dir_audio)
+  os.makedirs(output_dir_audio_temp)
+
+def convert_audio():
+  global output_dir_audio
+  global output_dir_audio_temp
+
+  recordings = len([name for name in os.listdir(output_dir_audio_temp) if os.path.isfile(os.path.join(output_dir_audio_temp,name))])
+  
+  print('→ Converting %s Audio Files to 22050 Hz, 16 Bit, Mono\n' % "{:,}".format(recordings))
+
+  for idx, wav in enumerate(glob.glob(os.path.join(output_dir_audio_temp, "*.wav"))):
+
+    percent = (idx + 1) / recordings
+
+    print('› \033[96m%s\033[0m \033[2m%s / %s (%s)\033[0m ' % (os.path.basename(wav), "{:,}".format((idx + 1)), "{:,}".format(recordings), "{:.0%}".format(percent)))
+
+    # Convert WAV file to required format
+    (ffmpeg
+      .input(wav)
+      .output(os.path.join(output_dir_audio, os.path.basename(wav)), acodec='pcm_s16le', ac=1, ar=22050, loglevel='error')
+      .overwrite_output()
+      .run(capture_stdout=True)
+    )
+
+    # Delete Temp File
+    os.remove(wav)
+
+  # Remove Temp Folder
+  rmtree(output_dir_audio_temp)
+
+def create_meta_data():
+  print('→ Creating META Data')
+
+  conn = sqlite3.connect(os.path.join(mrs_dir, "backend", "db", "mimicstudio.db"))
+  c = conn.cursor()
+
+  # Create metadata.csv for ljspeech
+  metadata = open(os.path.join(output_dir_speech, "metadata.csv"), mode="w", encoding="utf8")
+
+  # List available recording sessions
+  user_models = c.execute('SELECT uuid, user_name from usermodel ORDER BY created_date DESC').fetchall()
+  user_id = user_models[0][0]
+
+  for row in user_models:
+    print(row[0] + ' -> ' + row[1])
+
+  user_answer = input('Please choose ID of recording session to export (default is newest session) [' + user_id + ']: ')
+
+  if user_answer:
+    user_id = user_answer
+
+
+  for row in c.execute('SELECT audio_id, prompt, lower(prompt) FROM audiomodel WHERE user_id = "' + user_id + '" ORDER BY length(prompt)'):
+    metadata.write(row[0] + "|" + row[1] + "|" + row[2] + "\n")
+    copyfile(os.path.join(mrs_dir, "backend", "audio_files", user_id, row[0] + ".wav"), os.path.join(output_dir_audio_temp, row[0] + ".wav"))
+
+  metadata.close()
+  conn.close()
+
+def main():
+  print('\n\033[48;5;22m  MRS to LJ Speech Processor  \033[0m\n')
+
+  create_folders()
+  create_meta_data()
+  convert_audio()
+
+  print('\n\033[38;5;86;1m✔\033[0m COMPLETE【ツ】\n')
+
+if __name__ == '__main__':
+  main()