import numpy as np import soundfile as sf import glob import argparse import os import utils import configparser as CP LOW_ENERGY_THRESH = -60 def test_snr(clean, noise, expected_snr, snrtolerance=2): '''Test for SNR Note: It is not applicable for Segmental SNR''' rmsclean = (clean**2).mean()**0.5 rmsnoise = (noise**2).mean()**0.5 actual_snr = 20*np.log10(rmsclean/rmsnoise) return actual_snr > (expected_snr-snrtolerance) and actual_snr < (expected_snr+snrtolerance) def test_normalization(audio, expected_rms=-25, normtolerance=2): '''Test for Normalization Note: Set it to False if different target levels are used''' rmsaudio = (audio**2).mean()**0.5 rmsaudiodb = 20*np.log10(rmsaudio) return rmsaudiodb > (expected_rms-normtolerance) and rmsaudiodb < (expected_rms+normtolerance) def test_samplingrate(sr, expected_sr=16000): '''Test to ensure all clips have same sampling rate''' return expected_sr == sr def test_clipping(audio, num_consecutive_samples=3, clipping_threshold=0.01): '''Test to detect clipping''' clipping = False for i in range(0, len(audio)-num_consecutive_samples-1): audioseg = audio[i:i+num_consecutive_samples] if abs(max(audioseg)-min(audioseg)) < clipping_threshold or abs(max(audioseg)) >= 1: clipping = True break return clipping def test_zeros_beg_end(audio, num_zeros=16000, low_energy_thresh=LOW_ENERGY_THRESH): '''Test if there are zeros in the beginning and the end of the signal''' beg_segment_energy = 20*np.log10(audio[:num_zeros]**2).mean()**0.5 end_segment_energy = 20*np.log10(audio[-num_zeros:]**2).mean()**0.5 return beg_segment_energy < low_energy_thresh or end_segment_energy < low_energy_thresh def adsp_filtering_test(adsp, without_adsp): diff = adsp - without_adsp if any(val >0.0001 for val in diff): if __name__=='__main__': parser = argparse.ArgumentParser() parser.add_argument('--cfg', default='noisyspeech_synthesizer.cfg') parser.add_argument('--cfg_str', type=str, default='noisy_speech') args = parser.parse_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) cfg = cfg._sections[args.cfg_str] noisydir = cfg['noisy_train'] cleandir = cfg['clean_train'] noisedir = cfg['noise_train'] audioformat = cfg['audioformat'] # List of noisy speech files noisy_speech_filenames_big = glob.glob(os.path.join(noisydir, audioformat)) noisy_speech_filenames = noisy_speech_filenames_big[0:10] # Initialize the lists noisy_filenames_list = [] clean_filenames_list = [] noise_filenames_list = [] snr_results_list =[] clean_norm_results_list = [] noise_norm_results_list = [] noisy_norm_results_list = [] clean_sr_results_list = [] noise_sr_results_list = [] noisy_sr_results_list = [] clean_clipping_results_list = [] noise_clipping_results_list = [] noisy_clipping_results_list = [] skipped_string = 'Skipped' # Initialize the counters for stats total_clips = len(noisy_speech_filenames) for noisypath in noisy_speech_filenames: # To do: add right paths to clean filename and noise filename noisy_filename = os.path.basename(noisypath) clean_filename = 'clean_fileid_'+os.path.splitext(noisy_filename)[0].split('fileid_')[1]+'.wav' cleanpath = os.path.join(cleandir, clean_filename) noise_filename = 'noise_fileid_'+os.path.splitext(noisy_filename)[0].split('fileid_')[1]+'.wav' noisepath = os.path.join(noisedir, noise_filename) noisy_filenames_list.append(noisy_filename) clean_filenames_list.append(clean_filename) noise_filenames_list.append(noise_filename) # Read clean, noise and noisy signals clean_signal, fs_clean = sf.read(cleanpath) noise_signal, fs_noise = sf.read(noisepath) noisy_signal, fs_noisy = sf.read(noisypath) # SNR Test # To do: add right path split to extract SNR if utils.str2bool(cfg['snr_test']): snr = int(noisy_filename.split('_snr')[1].split('_')[0]) snr_results_list.append(str(test_snr(clean=clean_signal, \ noise=noise_signal, expected_snr=snr))) else: snr_results_list.append(skipped_string) # Normalization test if utils.str2bool(cfg['norm_test']): tl = int(noisy_filename.split('_tl')[1].split('_')[0]) clean_norm_results_list.append(str(test_normalization(clean_signal))) noise_norm_results_list.append(str(test_normalization(noise_signal))) noisy_norm_results_list.append(str(test_normalization(noisy_signal, expected_rms=tl))) else: clean_norm_results_list.append(skipped_string) noise_norm_results_list.append(skipped_string) noisy_norm_results_list.append(skipped_string) # Sampling rate test if utils.str2bool(cfg['sampling_rate_test']): clean_sr_results_list.append(str(test_samplingrate(sr=fs_clean))) noise_sr_results_list.append(str(test_samplingrate(sr=fs_noise))) noisy_sr_results_list.append(str(test_samplingrate(sr=fs_noisy))) else: clean_sr_results_list.append(skipped_string) noise_sr_results_list.append(skipped_string) noisy_sr_results_list.append(skipped_string) # Clipping test if utils.str2bool(cfg['clipping_test']): clean_clipping_results_list.append(str(test_clipping(audio=clean_signal))) noise_clipping_results_list.append(str(test_clipping(audio=noise_signal))) noisy_clipping_results_list.append(str(test_clipping(audio=noisy_signal))) else: clean_clipping_results_list.append(skipped_string) noise_clipping_results_list.append(skipped_string) noisy_clipping_results_list.append(skipped_string) # Stats pc_snr_passed = round(snr_results_list.count('True')/total_clips*100, 1) pc_clean_norm_passed = round(clean_norm_results_list.count('True')/total_clips*100, 1) pc_noise_norm_passed = round(noise_norm_results_list.count('True')/total_clips*100, 1) pc_noisy_norm_passed = round(noisy_norm_results_list.count('True')/total_clips*100, 1) pc_clean_sr_passed = round(clean_sr_results_list.count('True')/total_clips*100, 1) pc_noise_sr_passed = round(noise_sr_results_list.count('True')/total_clips*100, 1) pc_noisy_sr_passed = round(noisy_sr_results_list.count('True')/total_clips*100, 1) pc_clean_clipping_passed = round(clean_clipping_results_list.count('True')/total_clips*100, 1) pc_noise_clipping_passed = round(noise_clipping_results_list.count('True')/total_clips*100, 1) pc_noisy_clipping_passed = round(noisy_clipping_results_list.count('True')/total_clips*100, 1) print('% clips that passed SNR test:', pc_snr_passed) print('% clean clips that passed Normalization tests:', pc_clean_norm_passed) print('% noise clips that passed Normalization tests:', pc_noise_norm_passed) print('% noisy clips that passed Normalization tests:', pc_noisy_norm_passed) print('% clean clips that passed Sampling Rate tests:', pc_clean_sr_passed) print('% noise clips that passed Sampling Rate tests:', pc_noise_sr_passed) print('% noisy clips that passed Sampling Rate tests:', pc_noisy_sr_passed) print('% clean clips that passed Clipping tests:', pc_clean_clipping_passed) print('% noise clips that passed Clipping tests:', pc_noise_clipping_passed) print('% noisy clips that passed Clipping tests:', pc_noisy_clipping_passed) log_dir = utils.get_dir(cfg, 'unit_tests_log_dir', 'Unit_tests_logs') if not os.path.exists(log_dir): log_dir = os.path.join(os.path.dirname(__file__), 'Unit_tests_logs') os.makedirs(log_dir) utils.write_log_file(log_dir, 'unit_test_results.csv', [noisy_filenames_list, clean_filenames_list, \ noise_filenames_list, snr_results_list, clean_norm_results_list, noise_norm_results_list, \ noisy_norm_results_list, clean_sr_results_list, noise_sr_results_list, noisy_sr_results_list, \ clean_clipping_results_list, noise_clipping_results_list, noisy_clipping_results_list])