ravdess_preprocessor.py

# from pathlib import Path 

# import pandas as pd
# import regex as re
# import os

# import torchaudio
# import argparse
# from tqdm import tqdm  

# from collections import OrderedDict


# feat_dict = OrderedDict()
# od['Modality'] = ['full-AV', 'video-only', 'audio-only']
# od['Vocal channel'] = ['speech', 'song']
# od['Emotion'] = ['neutral', 'calm', 'happy', 'sad', 'angry', 'fearful', 'disgust', 'surprised']
# od['Emotion intensity'] = ['normal', 'strong']
# od['Statement'] = ["Kids are talking by the door", "Dogs are sitting by the door"]
# od['Repetition'] = ["1st repetition", "2nd repetition"]


# # def filename2feats(filename):
# #     codes = filename.stem.split('-')
# #     for i, k in enumerate(od.keys()):
# #         d = {}
# #         d[k] = od[k][int(codes[i])-1]
# #         d['Actor'] = codes[-1]
# #         d['Gender'] = 'female' if int(codes[-1]) % 2 == 0 else 'male'
# #     return d

# def preprocess(data_root_path):
#     output_dir = data_root_path / "RAVDESS_ser"
#     for f in data_root_path.iterdir():
#         print(f)
#         filename2feats(filename)
#         print("\n\n")



# # Filename identifiers 

# # Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
# # Vocal channel (01 = speech, 02 = song).
# # Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
# # Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
# # Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
# # Repetition (01 = 1st repetition, 02 = 2nd repetition).
# # Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).

# # Filename example: 02-01-06-01-02-01-12.mp4 

# # Video-only (02)
# # Speech (01)
# # Fearful (06)
# # Normal intensity (01)
# # Statement "dogs" (02)
# # 1st Repetition (01)
# # 12th Actor (12)
# # Female, as the actor ID number is even.







# #         self.data_root_path = Path(data_root_path)
# #         df = pd.DataFrame()
# #         for session in range(1,5):
# #             print(f"Processing session {session}")
# #             df = pd.concat([df, self.read_session_data(session)])

# #         # Write the sliced wavs
# #         print("Writing wav slices to file...")
# #         sample_rate = 16000
# #         for index, row in df.iterrows():
# #             old_filename = str(self.data_root_path / Path(row['Path_to_Wav']))
# #             new_filename = str(output_dir / (index + ".wav"))
# #             waveform = self.read_audio(old_filename,
# #                                        start=row['Time_Start'],
# #                                        end=row['Time_End'])
# #             torchaudio.save(os.path.join(new_filename), 
# #                                          src=waveform, 
# #                                          sample_rate=sample_rate)
# #             df.at[index, 'Path_to_Wav'] = new_filename
            

# #         # Write out the combined data information
# #         try:
# #             df.to_csv(output_filename, index=False, header=True)
# #         except:
# #             print("Error writing dataframe to csv.")



# #     def read_session_data(self, session_id):
# #         d1 = self.read_emotion_labels(session_id)
# #         d2 = self.read_transcriptions(session_id)
# #         return d1.join(d2)


# #     def read_emotion_labels(self, session_id):        
# #         emo_path = Path(self.data_root_path / Path(f"Session{session_id}") / Path("dialog") / Path("EmoEvaluation"))    
# #         emo_files = [f for f in list(emo_path.iterdir()) if f.suffix == ".txt"]
# #         df = pd.DataFrame()
# #         for ef in emo_files:
# #             df2 = self.read_emotion_file(ef)
# #             for ri, row in df2.iterrows():
# #                 df2.loc[ri, 'Path_to_Wav'] = os.path.join(f"Session{session_id}", 
# #                                                          "dialog", "wav", 
# #                                                          row['Session_ID'] +".wav")
# #             df = pd.concat([df, df2])
# #         df = df.set_index('ID')
# #         return df


# #     def slice_audio(self, session_id):
# #         for i, row in df.iterrows():
# #             filename = row['Session_ID'] + ".wav"
# #             wav_path = Path(self.data_root_path / Path(f"Session{session_id}") / Path("dialog") / Path("wav") / Path(filename))
# #             print("wav path = ", wav_path)
# #             self.read_audio(wav_path, row['Time_Start'], row['Time_End'], row['Annotations'])


# #     def read_emotion_file(self, filename):
# #         time_extract_pattern = "\[([0-9\.]+) - ([0-9\.]+)\] +([^ ]+) +([^ ]+) \[([^\]]+)\]"
# #         df = pd.DataFrame() #columns=columns)
# #         i = 0
# #         with open(filename) as file:
# #             lines = file.readlines()
# #             lines = lines[2:] #:10]

# #             while i < len(lines):
# #                 # Remove header
# #                 if match := re.search(time_extract_pattern, lines[i].replace("\t", " ")):
# #                     time_start = float(match.group(1))
# #                     time_end = float(match.group(2))
# #                     filename = match.group(3)
# #                     mys_id = match.group(4)
# #                     digits = [float(x) for x in match.group(5).split(", ")]
# #                     annotations = []
# #                     while lines[i] != "\n":
# #                         i += 1
# #                         if lines[i].startswith("C-"):
# #                             aid, anns, _ = lines[i].split("\t")
# #                             for an in anns.split(";")[:-1]:
# #                                 annotations.append(an.strip())
# #                         elif lines[i].startswith("A-"):
# #                             pass

# #                     annotations = list(set(annotations))
# #                     annotations = ','.join(annotations)

# #                     session_id = filename[:filename.rindex("_")] 
# #                     utt_id = filename[filename.rindex("_")+1:]

# #                     df2 = pd.DataFrame([{
# #                                   'ID': filename, # ID for join between dataframes is the filename
# #                                   'Session_ID': session_id,
# #                                   'Utterance_ID': utt_id,
# #                                   'Time_Start': time_start,
# #                                   'Time_End': time_end,
# #                                   'Labels': annotations}])
# #                     df = pd.concat([df, df2], ignore_index=True)
# #                 else:
# #                     i += 1
# #         return df


# #     def read_transcriptions(self, session_id):
# #         df = pd.DataFrame()
# #         transcripts_path = Path(self.data_root_path / Path(f"Session{session_id}") / Path("dialog") / Path("transcriptions"))    
# #         transcript_files = [f for f in list(transcripts_path.iterdir()) if f.suffix == ".txt"]
# #         for f in transcript_files:
# #             df = pd.concat([df, self.read_transcript(f)], ignore_index=True)  
# #         df = df.set_index('ID')
# #         return df          


# #     def read_transcript(self, filename):
# #         df = pd.DataFrame()
# #         with open(filename, "r") as f:
# #             for l in f.readlines():
# #                 cols = l.strip().split(" ")
# #                 if l[1] != ":" and len(cols) > 2: # There are some lines like "F:Mmhmm." that get ignored here
# #                     df2 = pd.DataFrame([{
# #                         'ID': cols[0],
# #                         'Transcription': ' '.join(cols[2:])
# #                         }])
# #                     df = pd.concat([df, df2])
# #         return df


# #     def read_audio(self, filename, start, end, sample_rate=16000):
# #         waveform, sample_rate = torchaudio.load(filename,
# #                                                 frame_offset=int(start * sample_rate),
# #                                                 num_frames=int((end-start) * sample_rate))
# #         return waveform


# # if __name__ == '__main__':
# # #    osx_path = '/Users/narad/Downloads/RAVDESS_full_release'
# # #    windows_path = r'C:\Users\jasonn\Desktop\ser\data\RAVDESS_full_release'

# #     parser = argparse.ArgumentParser(description='Process some integers.')
# #     parser.add_argument('--data_dir', type=Path, required=True,
# #                        help='Path to IEOMCAP release directory.')
# #     parser.add_argument('--output_file', type=Path, default="data.csv", 
# #                         help='Filename for Huggingface-compatible dataset csv file.')
# #     parser.add_argument('--output_dir', type=Path, default="processed",
# #                        help='Directory for processed wav files')
# #     args = parser.parse_args()

# #     print(args)

# #     reader = RAVDESS(data_root_path=args.data_dir,
# #                      output_filename=args.output_file)











#     # columns = ['Utterance_ID',
#     #            'Time_Start',
#     #            'Time-End',
#     #            'Annotations']