Datasets:

narad
/

ravdess

	# 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']