noisyspeech_synthesizer_multiprocessing.py

"""
@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:
        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['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()