""" @author: chkarada """ # Note that this file picks the clean speech files randomly, so it does not guarantee that all # source files will be used import os import glob import argparse import ast import configparser as CP from itertools import repeat import multiprocessing from multiprocessing import Pool import random from random import shuffle import librosa import numpy as np from audiolib import is_clipped, audioread, audiowrite, snr_mixer, activitydetector import utils PROCESSES = multiprocessing.cpu_count() MAXTRIES = 50 MAXFILELEN = 100 np.random.seed(2) random.seed(3) clean_counter = None noise_counter = None def init(args1, args2): ''' store the counter for later use ''' global clean_counter, noise_counter clean_counter = args1 noise_counter = args2 def build_audio(is_clean, params, filenum, audio_samples_length=-1): '''Construct an audio signal from source files''' fs_output = params['fs'] silence_length = params['silence_length'] if audio_samples_length == -1: audio_samples_length = int(params['audio_length']*params['fs']) output_audio = np.zeros(0) remaining_length = audio_samples_length files_used = [] clipped_files = [] global clean_counter, noise_counter if is_clean: source_files = params['cleanfilenames'] idx_counter = clean_counter else: source_files = params['noisefilenames'] idx_counter = noise_counter # initialize silence silence = np.zeros(int(fs_output*silence_length)) # iterate through multiple clips until we have a long enough signal tries_left = MAXTRIES while remaining_length > 0 and tries_left > 0: # read next audio file and resample if necessary with idx_counter.get_lock(): idx_counter.value += 1 idx = idx_counter.value % np.size(source_files) input_audio, fs_input = audioread(source_files[idx]) if fs_input != fs_output: input_audio = librosa.resample(input_audio, fs_input, fs_output) # if current file is longer than remaining desired length, and this is # noise generation or this is training set, subsample it randomly if len(input_audio) > remaining_length and (not is_clean or not params['is_test_set']): idx_seg = np.random.randint(0, len(input_audio)-remaining_length) input_audio = input_audio[idx_seg:idx_seg+remaining_length] # check for clipping, and if found move onto next file if is_clipped(input_audio): clipped_files.append(source_files[idx]) tries_left -= 1 continue # concatenate current input audio to output audio stream files_used.append(source_files[idx]) output_audio = np.append(output_audio, input_audio) remaining_length -= len(input_audio) # add some silence if we have not reached desired audio length if remaining_length > 0: silence_len = min(remaining_length, len(silence)) output_audio = np.append(output_audio, silence[:silence_len]) remaining_length -= silence_len if tries_left == 0: print("Audio generation failed for filenum " + str(filenum)) return [], [], clipped_files return output_audio, files_used, clipped_files def gen_audio(is_clean, params, filenum, audio_samples_length=-1): '''Calls build_audio() to get an audio signal, and verify that it meets the activity threshold''' clipped_files = [] low_activity_files = [] if audio_samples_length == -1: audio_samples_length = int(params['audio_length']*params['fs']) if is_clean: activity_threshold = params['clean_activity_threshold'] else: activity_threshold = params['noise_activity_threshold'] while True: audio, source_files, new_clipped_files = \ build_audio(is_clean, params, filenum, audio_samples_length) clipped_files += new_clipped_files if len(audio) < audio_samples_length: continue if activity_threshold == 0.0: break percactive = activitydetector(audio=audio) if percactive > activity_threshold: break else: low_activity_files += source_files return audio, source_files, clipped_files, low_activity_files def main_gen(params, filenum): '''Calls gen_audio() to generate the audio signals, verifies that they meet the requirements, and writes the files to storage''' print("Generating file #" + str(filenum)) clean_clipped_files = [] clean_low_activity_files = [] noise_clipped_files = [] noise_low_activity_files = [] while True: # generate clean speech clean, clean_source_files, clean_cf, clean_laf = \ gen_audio(True, params, filenum) # generate noise noise, noise_source_files, noise_cf, noise_laf = \ gen_audio(False, params, filenum, len(clean)) clean_clipped_files += clean_cf clean_low_activity_files += clean_laf noise_clipped_files += noise_cf noise_low_activity_files += noise_laf # mix clean speech and noise # if specified, use specified SNR value if not params['randomize_snr']: snr = params['snr'] # use a randomly sampled SNR value between the specified bounds else: snr = np.random.randint(params['snr_lower'], params['snr_upper']) clean_snr, noise_snr, noisy_snr, target_level = snr_mixer(params=params, clean=clean, noise=noise, snr=snr) # Uncomment the below lines if you need segmental SNR and comment the above lines using snr_mixer #clean_snr, noise_snr, noisy_snr, target_level = segmental_snr_mixer(params=params, # clean=clean, # noise=noise, # snr=snr) # unexpected clipping if is_clipped(clean_snr) or is_clipped(noise_snr) or is_clipped(noisy_snr): continue else: break # write resultant audio streams to files hyphen = '-' clean_source_filenamesonly = [i[:-4].split(os.path.sep)[-1] for i in clean_source_files] clean_files_joined = hyphen.join(clean_source_filenamesonly)[:MAXFILELEN] noise_source_filenamesonly = [i[:-4].split(os.path.sep)[-1] for i in noise_source_files] noise_files_joined = hyphen.join(noise_source_filenamesonly)[:MAXFILELEN] noisyfilename = clean_files_joined + '_' + noise_files_joined + '_snr' + \ str(snr) + '_fileid_' + str(filenum) + '.wav' cleanfilename = 'clean_fileid_'+str(filenum)+'.wav' noisefilename = 'noise_fileid_'+str(filenum)+'.wav' noisypath = os.path.join(params['noisyspeech_dir'], noisyfilename) cleanpath = os.path.join(params['clean_proc_dir'], cleanfilename) noisepath = os.path.join(params['noise_proc_dir'], noisefilename) audio_signals = [noisy_snr, clean_snr, noise_snr] file_paths = [noisypath, cleanpath, noisepath] for i in range(len(audio_signals)): try: audiowrite(file_paths[i], audio_signals[i], params['fs']) except Exception as e: print(str(e)) pass return clean_source_files, clean_clipped_files, clean_low_activity_files, \ noise_source_files, noise_clipped_files, noise_low_activity_files def extract_list(input_list, index): output_list = [i[index] for i in input_list] flat_output_list = [item for sublist in output_list for item in sublist] flat_output_list = sorted(set(flat_output_list)) return flat_output_list def main_body(): '''Main body of this file''' parser = argparse.ArgumentParser() # Configurations: read noisyspeech_synthesizer.cfg and gather inputs parser.add_argument('--cfg', default='noisyspeech_synthesizer.cfg', help='Read noisyspeech_synthesizer.cfg for all the details') parser.add_argument('--cfg_str', type=str, default='noisy_speech') args = parser.parse_args() params = dict() params['args'] = args cfgpath = os.path.join(os.path.dirname(__file__), args.cfg) assert os.path.exists(cfgpath), f'No configuration file as [{cfgpath}]' cfg = CP.ConfigParser() cfg._interpolation = CP.ExtendedInterpolation() cfg.read(cfgpath) params['cfg'] = cfg._sections[args.cfg_str] cfg = params['cfg'] clean_dir = os.path.join(os.path.dirname(__file__), 'CleanSpeech') if cfg['speech_dir'] != 'None': clean_dir = cfg['speech_dir'] if not os.path.exists(clean_dir): assert False, ('Clean speech data is required') noise_dir = os.path.join(os.path.dirname(__file__), 'Noise') if cfg['noise_dir'] != 'None': noise_dir = cfg['noise_dir'] if not os.path.exists(noise_dir): assert False, ('Noise data is required') params['fs'] = int(cfg['sampling_rate']) params['audioformat'] = cfg['audioformat'] params['audio_length'] = float(cfg['audio_length']) params['silence_length'] = float(cfg['silence_length']) params['total_hours'] = float(cfg['total_hours']) if cfg['fileindex_start'] != 'None' and cfg['fileindex_start'] != 'None': params['fileindex_start'] = int(cfg['fileindex_start']) params['fileindex_end'] = int(cfg['fileindex_end']) params['num_files'] = int(params['fileindex_end'])-int(params['fileindex_start']) else: params['num_files'] = int((params['total_hours']*60*60)/params['audio_length']) print('Number of files to be synthesized:', params['num_files']) params['is_test_set'] = utils.str2bool(cfg['is_test_set']) params['clean_activity_threshold'] = float(cfg['clean_activity_threshold']) params['noise_activity_threshold'] = float(cfg['noise_activity_threshold']) params['snr_lower'] = int(cfg['snr_lower']) params['snr_upper'] = int(cfg['snr_upper']) params['randomize_snr'] = utils.str2bool(cfg['randomize_snr']) params['target_level_lower'] = int(cfg['target_level_lower']) params['target_level_upper'] = int(cfg['target_level_upper']) if 'snr' in cfg.keys(): params['snr'] = int(cfg['snr']) else: params['snr'] = int((params['snr_lower'] + params['snr_upper'])/2) params['noisyspeech_dir'] = utils.get_dir(cfg, 'noisy_destination', 'noisy') params['clean_proc_dir'] = utils.get_dir(cfg, 'clean_destination', 'clean') params['noise_proc_dir'] = utils.get_dir(cfg, 'noise_destination', 'noise') if 'speech_csv' in cfg.keys() and cfg['speech_csv'] != 'None': cleanfilenames = pd.read_csv(cfg['speech_csv']) cleanfilenames = cleanfilenames['filename'] else: cleanfilenames = glob.glob(os.path.join(clean_dir, params['audioformat'])) params['cleanfilenames'] = cleanfilenames shuffle(params['cleanfilenames']) params['num_cleanfiles'] = len(params['cleanfilenames']) params['noisefilenames'] = glob.glob(os.path.join(noise_dir, params['audioformat'])) shuffle(params['noisefilenames']) # Invoke multiple processes and fan out calls to main_gen() to these processes global clean_counter, noise_counter clean_counter = multiprocessing.Value('i', 0) noise_counter = multiprocessing.Value('i', 0) multi_pool = multiprocessing.Pool(processes=PROCESSES, initializer = init, initargs = (clean_counter, noise_counter, )) fileindices = range(params['num_files']) output_lists = multi_pool.starmap(main_gen, zip(repeat(params), fileindices)) flat_output_lists = [] num_lists = 6 for i in range(num_lists): flat_output_lists.append(extract_list(output_lists, i)) # Create log directory if needed, and write log files of clipped and low activity files log_dir = utils.get_dir(cfg, 'log_dir', 'Logs') utils.write_log_file(log_dir, 'source_files.csv', flat_output_lists[0] + flat_output_lists[3]) utils.write_log_file(log_dir, 'clipped_files.csv', flat_output_lists[1] + flat_output_lists[4]) utils.write_log_file(log_dir, 'low_activity_files.csv', flat_output_lists[2] + flat_output_lists[5]) # Compute and print stats about percentange of clipped and low activity files total_clean = len(flat_output_lists[0]) + len(flat_output_lists[1]) + len(flat_output_lists[2]) total_noise = len(flat_output_lists[3]) + len(flat_output_lists[4]) + len(flat_output_lists[5]) pct_clean_clipped = round(len(flat_output_lists[1])/total_clean*100, 1) pct_noise_clipped = round(len(flat_output_lists[4])/total_noise*100, 1) pct_clean_low_activity = round(len(flat_output_lists[2])/total_clean*100, 1) pct_noise_low_activity = round(len(flat_output_lists[5])/total_noise*100, 1) print("Of the " + str(total_clean) + " clean speech files analyzed, " + str(pct_clean_clipped) + \ "% had clipping, and " + str(pct_clean_low_activity) + "% had low activity " + \ "(below " + str(params['clean_activity_threshold']*100) + "% active percentage)") print("Of the " + str(total_noise) + " noise files analyzed, " + str(pct_noise_clipped) + \ "% had clipping, and " + str(pct_noise_low_activity) + "% had low activity " + \ "(below " + str(params['noise_activity_threshold']*100) + "% active percentage)") if __name__ == '__main__': main_body()