""" @author: chkarada """ # Note: This single process audio synthesizer will attempt to use each clean # speech sourcefile once, as it does not randomly sample from these files import os import glob import argparse import ast import configparser as CP import random from random import shuffle import librosa import numpy as np from audiolib import audioread, audiowrite, segmental_snr_mixer, snr_mixer, \ activitydetector, is_clipped, add_clipping import utils import pandas as pd MAXTRIES = 50 MAXFILELEN = 100 np.random.seed(2) random.seed(3) def build_audio(is_clean, params, index, 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 = [] if is_clean: source_files = params['cleanfilenames'] idx = index else: if 'noisefilenames' in params.keys(): source_files = params['noisefilenames'] idx = index # if noise files are organized into individual subdirectories, pick a directory randomly else: noisedirs = params['noisedirs'] # pick a noise category randomly idx_n_dir = np.random.randint(0, np.size(noisedirs)) source_files = glob.glob(os.path.join(noisedirs[idx_n_dir], params['audioformat'])) shuffle(source_files) # pick a noise source file index randomly idx = np.random.randint(0, np.size(source_files)) # 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 idx = (idx + 1) % 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 and not is_clean and 'noisedirs' in params.keys(): print("There are not enough non-clipped files in the " + noisedirs[idx_n_dir] + \ " directory to complete the audio build") return [], [], clipped_files, idx return output_audio, files_used, clipped_files, idx def gen_audio(is_clean, params, index, 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, index = \ build_audio(is_clean, params, index, 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, index def main_gen(params): '''Calls gen_audio() to generate the audio signals, verifies that they meet the requirements, and writes the files to storage''' clean_source_files = [] clean_clipped_files = [] clean_low_activity_files = [] noise_source_files = [] noise_clipped_files = [] noise_low_activity_files = [] clean_index = 0 noise_index = 0 file_num = params['fileindex_start'] while file_num <= params['fileindex_end']: # generate clean speech clean, clean_sf, clean_cf, clean_laf, clean_index = \ gen_audio(True, params, clean_index) # generate noise noise, noise_sf, noise_cf, noise_laf, noise_index = \ gen_audio(False, params, noise_index, 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): print("Warning: File #" + str(file_num) + " has unexpected clipping, " + \ "returning without writing audio to disk") continue clean_source_files += clean_sf noise_source_files += noise_sf # write resultant audio streams to files hyphen = '-' clean_source_filenamesonly = [i[:-4].split(os.path.sep)[-1] for i in clean_sf] clean_files_joined = hyphen.join(clean_source_filenamesonly)[:MAXFILELEN] noise_source_filenamesonly = [i[:-4].split(os.path.sep)[-1] for i in noise_sf] noise_files_joined = hyphen.join(noise_source_filenamesonly)[:MAXFILELEN] noisyfilename = clean_files_joined + '_' + noise_files_joined + '_snr' + \ str(snr) + '_tl' + str(target_level) + '_fileid_' + str(file_num) + '.wav' cleanfilename = 'clean_fileid_'+str(file_num)+'.wav' noisefilename = 'noise_fileid_'+str(file_num)+'.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] file_num += 1 for i in range(len(audio_signals)): try: audiowrite(file_paths[i], audio_signals[i], params['fs']) except Exception as e: print(str(e)) return clean_source_files, clean_clipped_files, clean_low_activity_files, \ noise_source_files, noise_clipped_files, noise_low_activity_files 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: 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['num_files'] = int(cfg['fileindex_end'])-int(cfg['fileindex_start']) params['fileindex_start'] = int(cfg['fileindex_start']) params['fileindex_end'] = int(cfg['fileindex_end']) else: params['num_files'] = int((params['total_hours']*60*60)/params['audio_length']) params['fileindex_start'] = 0 params['fileindex_end'] = params['num_files'] 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']) # If there are .wav files in noise_dir directory, use those # If not, that implies that the noise files are organized into subdirectories by type, # so get the names of the non-excluded subdirectories if 'noise_csv' in cfg.keys() and cfg['noise_csv'] != 'None': noisefilenames = pd.read_csv(cfg['noise_csv']) noisefilenames = noisefilenames['filename'] else: noisefilenames = glob.glob(os.path.join(noise_dir, params['audioformat'])) if len(noisefilenames)!=0: shuffle(noisefilenames) params['noisefilenames'] = noisefilenames else: noisedirs = glob.glob(os.path.join(noise_dir, '*')) if cfg['noise_types_excluded'] != 'None': dirstoexclude = cfg['noise_types_excluded'].split(',') for dirs in dirstoexclude: noisedirs.remove(dirs) shuffle(noisedirs) params['noisedirs'] = noisedirs # Call main_gen() to generate audio clean_source_files, clean_clipped_files, clean_low_activity_files, \ noise_source_files, noise_clipped_files, noise_low_activity_files = main_gen(params) # 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', clean_source_files + noise_source_files) utils.write_log_file(log_dir, 'clipped_files.csv', clean_clipped_files + noise_clipped_files) utils.write_log_file(log_dir, 'low_activity_files.csv', \ clean_low_activity_files + noise_low_activity_files) # Compute and print stats about percentange of clipped and low activity files total_clean = len(clean_source_files) + len(clean_clipped_files) + len(clean_low_activity_files) total_noise = len(noise_source_files) + len(noise_clipped_files) + len(noise_low_activity_files) pct_clean_clipped = round(len(clean_clipped_files)/total_clean*100, 1) pct_noise_clipped = round(len(noise_clipped_files)/total_noise*100, 1) pct_clean_low_activity = round(len(clean_low_activity_files)/total_clean*100, 1) pct_noise_low_activity = round(len(noise_low_activity_files)/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()