├── README.md ├── constants.py ├── create_filenames.py ├── create_filenames_parallel.py ├── create_wham_from_scratch.py ├── create_wham_from_scratch_parallel.py ├── data ├── .DS_Store ├── mix_2_spk_filenames_librilight_tr.csv └── reverb_params_librilight_tr.csv ├── requirements.txt ├── run_sample_reverb.py ├── run_sample_reverb_parallel.py ├── sample_reverb.py ├── utils.py └── wham_room.py /README.md: -------------------------------------------------------------------------------- 1 | # Generating the LibriLight-Mix dataset 2 | 3 | This script supports generating noisy and reverberant 2-speaker mixture audio for training with the Libri-Light dataset, which can be served as training materials for large-scale robust speech separation. 4 | 5 | If you want to mix more speakers, please refer to [LibriLightMix-WHAM](https://github.com/WangHelin1997/LibriLightMix-WHAM). 6 | 7 | ## Python requirements 8 | 9 | Requires python 3.8, and the numpy, scipy, pandas, pyroomacoustics, and pysoundfile packages 10 | ```sh 11 | $ pip install -r requirements.txt 12 | ``` 13 | 14 | If you cannot install the ```pyroomacoustics``` successfully, you can try: 15 | ```sh 16 | $ conda install -c conda-forge gcc 17 | $ conda install -c conda-forge cxx-compiler 18 | $ conda install -c conda-forge libstdcxx-ng 19 | $ export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH 20 | $ pip install pyroomacoustics 21 | $ pip install -r requirements.txt 22 | ``` 23 | 24 | ## Prerequisites 25 | 26 | This requires the [Libri-Light](https://github.com/facebookresearch/libri-light) dataset, 27 | and the [WHAM](http://wham.whisper.ai/) noise corpus. 28 | 29 | ## Creating LibriLight-Mix 30 | 31 | ### Creating meta files 32 | 33 | ```sh 34 | $ python create_filenames.py 35 | ``` 36 | Change the following arguments in the script: 37 | * **wham_path**: Folder where the unzipped wham_noise was downloaded (training set). 38 | * **librilight_path**: Folder where the unzipped Libri-Light data was downloaded. 39 | * **debug**: Whether to process a dummy dataset. 40 | * **SOT**: Whether to process speakers in order (speaker1 speaks earlier than speaker2) for [serialized output training](https://arxiv.org/pdf/2003.12687.pdf). 41 | 42 | ### Creating reverberation meta files 43 | 44 | ```sh 45 | $ python run_sample_reverb.py 46 | ``` 47 | 48 | ### Creating mixture files 49 | 50 | ```sh 51 | $ python create_wham_from_scratch.py --mono \ 52 | --output-dir ./librilight_whamr/ \ 53 | --mode fix \ 54 | --sr 16000 \ 55 | --fixed-len 5 56 | 57 | ``` 58 | 59 | The arguments for the script are: 60 | * **output-dir**: Where to write the new dataset. 61 | * **mode**: Length of the simulated speech: "fix" for a fixed length, "min" for the minimum length of the two utterences, and "max" for the maximum length of the two utterences. 62 | * **sr**: Sampling rate. 63 | * **fixed-len**: Fixed length in mode "fix". 64 | 65 | ## Creating LibriLight-Mix parallelly with mulitple CPUs 66 | 67 | ### Creating meta files 68 | 69 | ```sh 70 | $ python create_filenames_parallel.py 71 | ``` 72 | Change the following arguments in the script: 73 | * **wham_path**: Folder where the unzipped wham_noise was downloaded (training set). 74 | * **librilight_path**: Folder where the unzipped Libri-Light data was downloaded. 75 | * **savename**: Name of the meta .csv file to save. 76 | * **tag**: Name of the meta .csv folder to save. 77 | * **debug**: Whether to process a dummy dataset. 78 | * **SOT**: Whether to process speakers in order (speaker1 speaks earlier than speaker2) for [serialized output training](https://arxiv.org/pdf/2003.12687.pdf). 79 | 80 | ### Creating reverberation meta files 81 | 82 | ```sh 83 | $ python run_sample_reverb_parallel.py 84 | ``` 85 | Change the **filelists** according to the **tag**. 86 | 87 | ### Creating mixture files 88 | 89 | ``` 90 | for i in $(seq 0 49) 91 | do 92 | python create_wham_from_scratch_parallel.py --mono \ 93 | --output-dir "./LibrilightMix-medium/$i/" \ 94 | --filepath "data/medium/mix_2_spk_filenames_librilight_tr_medium$i.csv" \ 95 | --mode fix \ 96 | --sr 16000 \ 97 | --fixed-len 5 98 | done 99 | ``` 100 | 101 | The arguments for the script are: 102 | * **output-dir**: Where to write the new dataset. 103 | * **filepath**: Name of the saved meta .csv folder. 104 | * **mode**: Length of the simulated speech: "fix" for a fixed length, "min" for the minimum length of the two utterences, and "max" for the maximum length of the two utterences. 105 | * **sr**: Sampling rate. 106 | * **fixed-len**: Fixed length in mode "fix". 107 | 108 | 109 | 110 | ## Output data organization 111 | 112 | For each utterance in the training (tr) set folder, the following wav files are written: 113 | 114 | 1. noise: contains the isolated background noise from WHAM! 115 | 116 | 2. s1_anechoic: isolated data from speaker 1 without reverb, but with appropriate delays to align with s1_reverb 117 | 118 | 3. s2_anechoic: isolated data from speaker 2 without reverb, but with appropriate delays to align with s2_reverb 119 | 120 | 4. s1_reverb: isolated data from speaker 1 with reverberation 121 | 122 | 5. s2_reverb: isolated data from speaker 2 with reverberation 123 | 124 | 6. mix_single_anechoic: for speech enhancement, contains mixture of s1_anechoic and noise 125 | 126 | 7. mix_clean_anechoic: clean speech separation for two speakers, contains mixture of s1_anechoic and s2_anechoic. The relative levels between speakers should match the original libri-light dataset, but the overall level of the mix will be different. 127 | 128 | 8. mix_both_anechoic: contains mixtures of s1_anechoic, s2_anechoic, and noise 129 | 130 | 9. mix_single_reverb: for speech enhancement, contains mixture of s1_reverb and noise 131 | 132 | 10. mix_clean_reverb: clean speech separation for two reverberant speakers, contains a mixture of s1_reverb and s2_reverb. The relative levels between speakers should match the original libri-light dataset, but the overall level of the mix will be different. 133 | 134 | 11. mix_both_reverb: contains mixtures of s1_reverb, s2_reverb, and noise 135 | 136 | 137 | ## Reference 138 | 139 | https://wham.whisper.ai/WHAMR_README.html 140 | -------------------------------------------------------------------------------- /constants.py: -------------------------------------------------------------------------------- 1 | NUM_BANDS = 4 2 | SNR_THRESH = -6. 3 | PRE_NOISE_SECONDS = 2.0 4 | SAMPLERATE = 16000 5 | MAX_SAMPLE_AMP = 0.95 6 | MIN_SNR_DB = -3. 7 | MAX_SNR_DB = 6. 8 | PRE_NOISE_SAMPLES = PRE_NOISE_SECONDS * SAMPLERATE -------------------------------------------------------------------------------- /create_filenames.py: -------------------------------------------------------------------------------- 1 | import csv 2 | import os 3 | import random 4 | import glob 5 | import json 6 | from tqdm import tqdm 7 | 8 | wham_path = './wham_noise/tr' 9 | librilight_path = './librilight/small' 10 | debug=False 11 | SOT=True 12 | 13 | count=0 14 | SEED=17 15 | random.seed(SEED) 16 | 17 | csvdata = [ 18 | ["output_filename","noise_path","noise_snr","s1_path","s1_start","s1_end","s1_tag","s1_snr","s2_path","s2_start","s2_end","s2_tag","s2_snr"] 19 | ] 20 | file_path = os.path.join('data', 'mix_2_spk_filenames_librilight_tr.csv') 21 | 22 | # Reading all noise files 23 | noise_files = [os.path.abspath(os.path.join(wham_path, file)) for file in os.listdir(wham_path) if file.endswith('.wav')] 24 | spks = [name for name in os.listdir(librilight_path) if os.path.isdir(os.path.join(librilight_path, name)) and not os.path.isfile(os.path.join(librilight_path, name))] 25 | 26 | for spk in spks: 27 | audiofiles = glob.glob(os.path.join(librilight_path, spk, '**/*.flac'), recursive=True) 28 | 29 | for audiofile in audiofiles: 30 | with open(audiofile.replace('.flac','.json'), 'r') as file: 31 | audiodata = json.load(file) 32 | vads = audiodata["voice_activity"] 33 | if debug: 34 | if count > 50: 35 | break 36 | for vad in tqdm(vads): 37 | if float(vad[1]) - float(vad[0]) > 2.: 38 | count+=1 39 | another_spk = random.choice([x for x in spks if x != spk]) 40 | another_audiofiles = glob.glob(os.path.join(librilight_path, another_spk, '**/*.flac'), recursive=True) 41 | another_audiofile = random.choice(another_audiofiles) 42 | with open(another_audiofile.replace('.flac','.json'), 'r') as file: 43 | another_audiodata = json.load(file) 44 | another_vads = another_audiodata["voice_activity"] 45 | another_vad = random.choice(another_vads) 46 | noisefile = random.choice(noise_files) 47 | s1_start = random.uniform(0, 0.3) if SOT else random.uniform(0, 0.5) 48 | s2_start = random.uniform(0.6, 1.3) if SOT else random.uniform(0, 0.5) 49 | s1_snr = random.uniform(-3, 6) 50 | s2_snr = random.uniform(-3, 6) 51 | noise_snr = random.uniform(-6, 3) 52 | csvdata.append([ 53 | str(count)+'.wav', 54 | noisefile, 55 | noise_snr, 56 | audiofile, 57 | vad[0], 58 | vad[1], 59 | s1_start, 60 | s1_snr, 61 | another_audiofile, 62 | another_vad[0], 63 | another_vad[1], 64 | s2_start, 65 | s2_snr 66 | ]) 67 | 68 | 69 | # Writing to the CSV file 70 | with open(file_path, mode='w', newline='') as file: 71 | writer = csv.writer(file) 72 | writer.writerows(csvdata) 73 | print(f'Data has been written to {file_path}') 74 | -------------------------------------------------------------------------------- /create_filenames_parallel.py: -------------------------------------------------------------------------------- 1 | import csv 2 | import os 3 | import random 4 | import glob 5 | import json 6 | from tqdm import tqdm 7 | import multiprocessing 8 | 9 | wham_path = './wham_noise/tr' 10 | librilight_path = './librilight/small' 11 | savename = 'mix_2_spk_filenames_librilight_tr_small' 12 | tag='small' 13 | debug=False 14 | SOT=True 15 | 16 | SEED=17 17 | random.seed(SEED) 18 | 19 | def process_one(i, tag, savename, spks, librilight_path, noise_files, debug, SOT): 20 | count=0 21 | os.makedirs(os.path.join('data', tag), exist_ok=True) 22 | file_path = os.path.join('data', tag, savename+ str(i)+'.csv') 23 | csvdata = [ 24 | ["output_filename","noise_path","noise_snr","s1_path","s1_start","s1_end","s1_tag","s1_snr","s2_path","s2_start","s2_end","s2_tag","s2_snr"] 25 | ] 26 | for spk in spks: 27 | audiofiles = glob.glob(os.path.join(librilight_path, spk, '**/*.flac'), recursive=True) 28 | 29 | for audiofile in audiofiles: 30 | with open(audiofile.replace('.flac','.json'), 'r') as file: 31 | audiodata = json.load(file) 32 | vads = audiodata["voice_activity"] 33 | if debug: 34 | if count > 50: 35 | break 36 | for vad in tqdm(vads): 37 | if float(vad[1]) - float(vad[0]) > 2.: 38 | count+=1 39 | another_spk = random.choice([x for x in spks if x != spk]) 40 | another_audiofiles = glob.glob(os.path.join(librilight_path, another_spk, '**/*.flac'), recursive=True) 41 | another_audiofile = random.choice(another_audiofiles) 42 | with open(another_audiofile.replace('.flac','.json'), 'r') as file: 43 | another_audiodata = json.load(file) 44 | another_vads = another_audiodata["voice_activity"] 45 | another_vad = random.choice(another_vads) 46 | noisefile = random.choice(noise_files) 47 | s1_start = random.uniform(0, 0.3) if SOT else random.uniform(0, 0.5) 48 | s2_start = random.uniform(0.6, 1.3) if SOT else random.uniform(0, 0.5) 49 | s1_snr = random.uniform(-3, 6) 50 | s2_snr = random.uniform(-3, 6) 51 | noise_snr = random.uniform(-6, 3) 52 | csvdata.append([ 53 | str(count)+'.wav', 54 | noisefile, 55 | noise_snr, 56 | audiofile, 57 | vad[0], 58 | vad[1], 59 | s1_start, 60 | s1_snr, 61 | another_audiofile, 62 | another_vad[0], 63 | another_vad[1], 64 | s2_start, 65 | s2_snr 66 | ]) 67 | 68 | 69 | # Writing to the CSV file 70 | with open(file_path, mode='w', newline='') as file: 71 | writer = csv.writer(file) 72 | writer.writerows(csvdata) 73 | print(f'Data has been written to {file_path}') 74 | 75 | 76 | # Reading all noise files 77 | noise_files = [os.path.abspath(os.path.join(wham_path, file)) for file in os.listdir(wham_path) if file.endswith('.wav')] 78 | spks = [name for name in os.listdir(librilight_path) if os.path.isdir(os.path.join(librilight_path, name)) and not os.path.isfile(os.path.join(librilight_path, name))] 79 | num_subspks = 50 80 | subspks_size = len(spks) // num_subspks 81 | 82 | random.shuffle(spks) 83 | # Split the list into subspks 84 | subspks = [spks[i*subspks_size:(i+1)*subspks_size] for i in range(num_subspks)] 85 | 86 | # If there are remaining elements, distribute them among the subspks 87 | if len(spks) % num_subspks != 0: 88 | remainder = len(spks) % num_subspks 89 | for i in range(remainder): 90 | subspks[i].append(spks[-(i+1)]) 91 | spks.pop() 92 | 93 | cmds = [] 94 | 95 | for i in range(len(subspks)): 96 | cmds.append((i, tag, savename, subspks[i], librilight_path, noise_files, debug, SOT)) 97 | with multiprocessing.Pool(processes=50) as pool: 98 | pool.starmap(process_one, cmds) 99 | -------------------------------------------------------------------------------- /create_wham_from_scratch.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import soundfile as sf 4 | import pandas as pd 5 | from constants import SAMPLERATE 6 | import argparse 7 | from utils import read_scaled_wav, quantize, fix_length, create_wham_mixes 8 | from wham_room import WhamRoom 9 | import multiprocessing 10 | import random 11 | 12 | FILELIST_STUB = os.path.join('data', 'mix_2_spk_filenames_librilight_{}.csv') 13 | 14 | SINGLE_DIR = 'mix_single' 15 | BOTH_DIR = 'mix_both' 16 | CLEAN_DIR = 'mix_clean' 17 | S1_DIR = 's1' 18 | S2_DIR = 's2' 19 | NOISE_DIR = 'noise' 20 | SUFFIXES = ['_anechoic', '_reverb'] 21 | SPLITS = ['tr'] 22 | 23 | def create_one(i_utt, output_name, wsjmix_df, reverb_param_df, SAMPLE_RATES, output_root, MODE, splt, FIXED_LEN, ch_ind, LEFT_CH_IND): 24 | wav_dir = 'wav' + str(SAMPLE_RATES) 25 | output_path = os.path.join(output_root, wav_dir, MODE, splt) 26 | if not os.path.exists(os.path.join(output_path, NOISE_DIR, output_name)): 27 | utt_row = reverb_param_df[reverb_param_df['output_filename'] == output_name] 28 | room = WhamRoom([utt_row['room_x'].iloc[0], utt_row['room_y'].iloc[0], utt_row['room_z'].iloc[0]], 29 | [[utt_row['micL_x'].iloc[0], utt_row['micL_y'].iloc[0], utt_row['mic_z'].iloc[0]], 30 | [utt_row['micR_x'].iloc[0], utt_row['micR_y'].iloc[0], utt_row['mic_z'].iloc[0]]], 31 | [utt_row['s1_x'].iloc[0], utt_row['s1_y'].iloc[0], utt_row['s1_z'].iloc[0]], 32 | [utt_row['s2_x'].iloc[0], utt_row['s2_y'].iloc[0], utt_row['s2_z'].iloc[0]], 33 | utt_row['T60'].iloc[0]) 34 | room.generate_rirs() 35 | 36 | utt_row = wsjmix_df[wsjmix_df['output_filename'] == output_name] 37 | s1_path = utt_row['s1_path'].iloc[0] 38 | s2_path = utt_row['s2_path'].iloc[0] 39 | s1_start = float(utt_row["s1_start"].iloc[0]) 40 | s1_end = float(utt_row["s1_end"].iloc[0]) 41 | s1_tag = float(utt_row["s1_tag"].iloc[0]) 42 | s1_snr = 10**(float(utt_row["s1_snr"].iloc[0]) / 20) 43 | s2_start = float(utt_row["s2_start"].iloc[0]) 44 | s2_end = float(utt_row["s2_end"].iloc[0]) 45 | s2_tag = float(utt_row["s2_tag"].iloc[0]) 46 | s2_snr = 10**(float(utt_row["s2_snr"].iloc[0]) / 20) 47 | noise_path = utt_row['noise_path'].iloc[0] 48 | noise_snr = 10**(float(utt_row["noise_snr"].iloc[0]) / 20) 49 | snr_ratio = 0.9/(s1_snr + s2_snr + noise_snr) 50 | s1 = quantize(read_scaled_wav(s1_path, s1_snr*snr_ratio, s1_start, s1_end, SAMPLE_RATES)) 51 | s2 = quantize(read_scaled_wav(s2_path, s2_snr*snr_ratio, s2_start, s2_end, SAMPLE_RATES)) 52 | s1, s2 = fix_length(s1, s2, s1_tag, s2_tag, MODE, FIXED_LEN, SAMPLE_RATES) 53 | 54 | room.add_audio(s1, s2) 55 | anechoic = room.generate_audio(anechoic=True, fs=SAMPLE_RATES) 56 | reverberant = room.generate_audio(fs=SAMPLE_RATES) 57 | 58 | # Make relative source energy of anechoic sources same with original in mono (left channel) case 59 | s1_spatial_scaling = np.sqrt(np.sum(s1 ** 2) / np.sum(anechoic[0, LEFT_CH_IND, :] ** 2)) 60 | s2_spatial_scaling = np.sqrt(np.sum(s2 ** 2) / np.sum(anechoic[1, LEFT_CH_IND, :] ** 2)) 61 | 62 | noise_samples_full = read_scaled_wav(noise_path, noise_snr*snr_ratio, 0, None, SAMPLE_RATES) 63 | 64 | if noise_samples_full.shape[0] < s1.shape[0]: 65 | repeat_factor = (s1.shape[0] // noise_samples_full.shape[0]) + 1 66 | noise_samples_full = np.tile(noise_samples_full, repeat_factor) 67 | noise_samples_full = noise_samples_full[:s1.shape[0]] 68 | 69 | out_len = s1.shape[0] 70 | 71 | s1_anechoic = anechoic[0, ch_ind, :out_len].T * s1_spatial_scaling 72 | s2_anechoic = anechoic[1, ch_ind, :out_len].T * s2_spatial_scaling 73 | s1_reverb = reverberant[0, ch_ind, :out_len].T * s1_spatial_scaling 74 | s2_reverb = reverberant[1, ch_ind, :out_len].T * s2_spatial_scaling 75 | 76 | sources = [(s1_anechoic, s2_anechoic), (s1_reverb, s2_reverb)] 77 | for i_sfx, (sfx, source_pair) in enumerate(zip(SUFFIXES, sources)): 78 | s1_samples, s2_samples, noise_samples = source_pair[0], source_pair[1], noise_samples_full 79 | mix_clean, mix_single, mix_both = create_wham_mixes(s1_samples, s2_samples, noise_samples) 80 | 81 | # write audio 82 | samps = [mix_clean, mix_single, mix_both, s1_samples, s2_samples] 83 | dirs = [CLEAN_DIR, SINGLE_DIR, BOTH_DIR, S1_DIR, S2_DIR] 84 | for dir, samp in zip(dirs, samps): 85 | sf.write(os.path.join(output_path, dir+sfx, output_name), samp, 86 | SAMPLE_RATES, subtype='FLOAT') 87 | 88 | if i_sfx == 0: # only write noise once as it doesn't change between anechoic and reverberant 89 | sf.write(os.path.join(output_path, NOISE_DIR, output_name), noise_samples, 90 | SAMPLE_RATES, subtype='FLOAT') 91 | 92 | if (i_utt + 1) % 500 == 0: 93 | print('Completed {} of {} utterances'.format(i_utt + 1, len(wsjmix_df))) 94 | 95 | 96 | def create_wham(args, output_root): 97 | MONO = args.mono 98 | MODE = args.mode 99 | FIXED_LEN = args.fixed_len 100 | SAMPLE_RATES = args.sr 101 | 102 | LEFT_CH_IND = 0 103 | if MONO: 104 | ch_ind = LEFT_CH_IND 105 | else: 106 | ch_ind = [0, 1] 107 | 108 | reverb_param_stub = os.path.join('data', 'reverb_params_librilight_{}.csv') 109 | 110 | for splt in SPLITS: 111 | 112 | wsjmix_path = FILELIST_STUB.format(splt) 113 | wsjmix_df = pd.read_csv(wsjmix_path) 114 | 115 | reverb_param_path = reverb_param_stub.format(splt) 116 | reverb_param_df = pd.read_csv(reverb_param_path) 117 | 118 | wav_dir = 'wav' + str(SAMPLE_RATES) 119 | output_path = os.path.join(output_root, wav_dir, MODE, splt) 120 | for sfx in SUFFIXES: 121 | os.makedirs(os.path.join(output_path, CLEAN_DIR+sfx), exist_ok=True) 122 | os.makedirs(os.path.join(output_path, SINGLE_DIR+sfx), exist_ok=True) 123 | os.makedirs(os.path.join(output_path, BOTH_DIR+sfx), exist_ok=True) 124 | os.makedirs(os.path.join(output_path, S1_DIR+sfx), exist_ok=True) 125 | os.makedirs(os.path.join(output_path, S2_DIR+sfx), exist_ok=True) 126 | os.makedirs(os.path.join(output_path, NOISE_DIR), exist_ok=True) 127 | 128 | utt_ids = wsjmix_df['output_filename'] 129 | 130 | cmds = [] 131 | for i_utt, output_name in enumerate(utt_ids): 132 | cmds.append((i_utt, output_name, wsjmix_df, reverb_param_df, SAMPLE_RATES, output_root, MODE, splt, FIXED_LEN, ch_ind, LEFT_CH_IND)) 133 | print('Totally {} utterances'.format(len(cmds))) 134 | random.shuffle(cmds) # For parallel CPU processing, which can run several scripts at the same time. 135 | with multiprocessing.Pool(processes=20) as pool: 136 | pool.starmap(create_one, cmds) 137 | 138 | 139 | if __name__ == '__main__': 140 | parser = argparse.ArgumentParser() 141 | parser.add_argument('--output-dir', type=str, required=True, 142 | help='Output directory for writing wsj0-2mix 8 k Hz and 16 kHz datasets.') 143 | parser.add_argument('--mode', type=str, required=True, 144 | help='Simulated mode of data: fix, max or min') 145 | parser.add_argument('--sr', type=int, default=16000, 146 | help='Sampling rate') 147 | parser.add_argument('--fixed-len', type=float, default=5, 148 | help='Fixed length of simulated speech') 149 | parser.add_argument('--mono', action='store_true', help='Generate mono audio, set false for stereo audio, not test yet') 150 | 151 | args = parser.parse_args() 152 | print('All arguments:', args) 153 | os.makedirs(args.output_dir, exist_ok=True) 154 | create_wham(args, args.output_dir) 155 | -------------------------------------------------------------------------------- /create_wham_from_scratch_parallel.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import soundfile as sf 4 | import pandas as pd 5 | from constants import SAMPLERATE 6 | import argparse 7 | from utils import read_scaled_wav, quantize, fix_length, create_wham_mixes 8 | from wham_room import WhamRoom 9 | import multiprocessing 10 | import random 11 | 12 | # FILELIST_STUB = os.path.join('data', 'mix_2_spk_filenames_librilight_{}.csv') 13 | 14 | SINGLE_DIR = 'mix_single' 15 | BOTH_DIR = 'mix_both' 16 | CLEAN_DIR = 'mix_clean' 17 | S1_DIR = 's1' 18 | S2_DIR = 's2' 19 | NOISE_DIR = 'noise' 20 | SUFFIXES = ['_anechoic', '_reverb'] 21 | SPLITS = ['tr'] 22 | 23 | def create_one(i_utt, output_name, wsjmix_df, reverb_param_df, SAMPLE_RATES, output_root, MODE, splt, FIXED_LEN, ch_ind, LEFT_CH_IND): 24 | wav_dir = 'wav' + str(SAMPLE_RATES) 25 | output_path = os.path.join(output_root, wav_dir, MODE, splt) 26 | if not os.path.exists(os.path.join(output_path, NOISE_DIR, output_name)): 27 | utt_row = reverb_param_df[reverb_param_df['output_filename'] == output_name] 28 | room = WhamRoom([utt_row['room_x'].iloc[0], utt_row['room_y'].iloc[0], utt_row['room_z'].iloc[0]], 29 | [[utt_row['micL_x'].iloc[0], utt_row['micL_y'].iloc[0], utt_row['mic_z'].iloc[0]], 30 | [utt_row['micR_x'].iloc[0], utt_row['micR_y'].iloc[0], utt_row['mic_z'].iloc[0]]], 31 | [utt_row['s1_x'].iloc[0], utt_row['s1_y'].iloc[0], utt_row['s1_z'].iloc[0]], 32 | [utt_row['s2_x'].iloc[0], utt_row['s2_y'].iloc[0], utt_row['s2_z'].iloc[0]], 33 | utt_row['T60'].iloc[0]) 34 | room.generate_rirs() 35 | 36 | utt_row = wsjmix_df[wsjmix_df['output_filename'] == output_name] 37 | s1_path = utt_row['s1_path'].iloc[0] 38 | s2_path = utt_row['s2_path'].iloc[0] 39 | s1_start = float(utt_row["s1_start"].iloc[0]) 40 | s1_end = float(utt_row["s1_end"].iloc[0]) 41 | s1_tag = float(utt_row["s1_tag"].iloc[0]) 42 | s1_snr = 10**(float(utt_row["s1_snr"].iloc[0]) / 20) 43 | s2_start = float(utt_row["s2_start"].iloc[0]) 44 | s2_end = float(utt_row["s2_end"].iloc[0]) 45 | s2_tag = float(utt_row["s2_tag"].iloc[0]) 46 | s2_snr = 10**(float(utt_row["s2_snr"].iloc[0]) / 20) 47 | noise_path = utt_row['noise_path'].iloc[0] 48 | noise_snr = 10**(float(utt_row["noise_snr"].iloc[0]) / 20) 49 | snr_ratio = 0.9/(s1_snr + s2_snr + noise_snr) 50 | s1 = quantize(read_scaled_wav(s1_path, s1_snr*snr_ratio, s1_start, s1_end, SAMPLE_RATES)) 51 | s2 = quantize(read_scaled_wav(s2_path, s2_snr*snr_ratio, s2_start, s2_end, SAMPLE_RATES)) 52 | s1, s2 = fix_length(s1, s2, s1_tag, s2_tag, MODE, FIXED_LEN, SAMPLE_RATES) 53 | 54 | room.add_audio(s1, s2) 55 | anechoic = room.generate_audio(anechoic=True, fs=SAMPLE_RATES) 56 | reverberant = room.generate_audio(fs=SAMPLE_RATES) 57 | 58 | # Make relative source energy of anechoic sources same with original in mono (left channel) case 59 | s1_spatial_scaling = np.sqrt(np.sum(s1 ** 2) / np.sum(anechoic[0, LEFT_CH_IND, :] ** 2)) 60 | s2_spatial_scaling = np.sqrt(np.sum(s2 ** 2) / np.sum(anechoic[1, LEFT_CH_IND, :] ** 2)) 61 | 62 | noise_samples_full = read_scaled_wav(noise_path, noise_snr*snr_ratio, 0, None, SAMPLE_RATES) 63 | 64 | if noise_samples_full.shape[0] < s1.shape[0]: 65 | repeat_factor = (s1.shape[0] // noise_samples_full.shape[0]) + 1 66 | noise_samples_full = np.tile(noise_samples_full, repeat_factor) 67 | noise_samples_full = noise_samples_full[:s1.shape[0]] 68 | 69 | out_len = s1.shape[0] 70 | 71 | s1_anechoic = anechoic[0, ch_ind, :out_len].T * s1_spatial_scaling 72 | s2_anechoic = anechoic[1, ch_ind, :out_len].T * s2_spatial_scaling 73 | s1_reverb = reverberant[0, ch_ind, :out_len].T * s1_spatial_scaling 74 | s2_reverb = reverberant[1, ch_ind, :out_len].T * s2_spatial_scaling 75 | 76 | sources = [(s1_anechoic, s2_anechoic), (s1_reverb, s2_reverb)] 77 | for i_sfx, (sfx, source_pair) in enumerate(zip(SUFFIXES, sources)): 78 | s1_samples, s2_samples, noise_samples = source_pair[0], source_pair[1], noise_samples_full 79 | mix_clean, mix_single, mix_both = create_wham_mixes(s1_samples, s2_samples, noise_samples) 80 | 81 | # write audio 82 | samps = [mix_clean, mix_single, mix_both, s1_samples, s2_samples] 83 | dirs = [CLEAN_DIR, SINGLE_DIR, BOTH_DIR, S1_DIR, S2_DIR] 84 | for dir, samp in zip(dirs, samps): 85 | sf.write(os.path.join(output_path, dir+sfx, output_name), samp, 86 | SAMPLE_RATES, subtype='FLOAT') 87 | 88 | if i_sfx == 0: # only write noise once as it doesn't change between anechoic and reverberant 89 | sf.write(os.path.join(output_path, NOISE_DIR, output_name), noise_samples, 90 | SAMPLE_RATES, subtype='FLOAT') 91 | 92 | if (i_utt + 1) % 500 == 0: 93 | print('Completed {} of {} utterances'.format(i_utt + 1, len(wsjmix_df))) 94 | 95 | 96 | def create_wham(args, output_root): 97 | MONO = args.mono 98 | MODE = args.mode 99 | FIXED_LEN = args.fixed_len 100 | SAMPLE_RATES = args.sr 101 | 102 | LEFT_CH_IND = 0 103 | if MONO: 104 | ch_ind = LEFT_CH_IND 105 | else: 106 | ch_ind = [0, 1] 107 | 108 | for splt in SPLITS: 109 | 110 | wsjmix_path = args.filepath 111 | wsjmix_df = pd.read_csv(wsjmix_path) 112 | 113 | reverb_param_stub = args.filepath.replace('mix_2_spk_filenames', 'reverb_params') 114 | 115 | reverb_param_path = reverb_param_stub.format(splt) 116 | reverb_param_df = pd.read_csv(reverb_param_path) 117 | 118 | wav_dir = 'wav' + str(SAMPLE_RATES) 119 | output_path = os.path.join(output_root, wav_dir, MODE, splt) 120 | for sfx in SUFFIXES: 121 | os.makedirs(os.path.join(output_path, CLEAN_DIR+sfx), exist_ok=True) 122 | os.makedirs(os.path.join(output_path, SINGLE_DIR+sfx), exist_ok=True) 123 | os.makedirs(os.path.join(output_path, BOTH_DIR+sfx), exist_ok=True) 124 | os.makedirs(os.path.join(output_path, S1_DIR+sfx), exist_ok=True) 125 | os.makedirs(os.path.join(output_path, S2_DIR+sfx), exist_ok=True) 126 | os.makedirs(os.path.join(output_path, NOISE_DIR), exist_ok=True) 127 | 128 | utt_ids = wsjmix_df['output_filename'] 129 | 130 | cmds = [] 131 | for i_utt, output_name in enumerate(utt_ids): 132 | cmds.append((i_utt, output_name, wsjmix_df, reverb_param_df, SAMPLE_RATES, output_root, MODE, splt, FIXED_LEN, ch_ind, LEFT_CH_IND)) 133 | print('Totally {} utterances'.format(len(cmds))) 134 | random.shuffle(cmds) # For parallel CPU processing, which can run several scripts at the same time. 135 | with multiprocessing.Pool(processes=20) as pool: 136 | pool.starmap(create_one, cmds) 137 | 138 | 139 | if __name__ == '__main__': 140 | parser = argparse.ArgumentParser() 141 | parser.add_argument('--output-dir', type=str, required=True, 142 | help='Output directory for writing wsj0-2mix 8 k Hz and 16 kHz datasets.') 143 | parser.add_argument('--filepath', type=str, required=True, 144 | help='Meta file path: .csv') 145 | parser.add_argument('--mode', type=str, required=True, 146 | help='Simulated mode of data: fix, max or min') 147 | parser.add_argument('--sr', type=int, default=16000, 148 | help='Sampling rate') 149 | parser.add_argument('--fixed-len', type=float, default=5, 150 | help='Fixed length of simulated speech') 151 | parser.add_argument('--mono', action='store_true', help='Generate mono audio, set false for stereo audio, not test yet') 152 | 153 | args = parser.parse_args() 154 | print('All arguments:', args) 155 | os.makedirs(args.output_dir, exist_ok=True) 156 | create_wham(args, args.output_dir) 157 | -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WangHelin1997/LibriLightMix-WHAMR/6af0ab6f4a00a82f746c8689006f8c72dedf5539/data/.DS_Store -------------------------------------------------------------------------------- /data/reverb_params_librilight_tr.csv: -------------------------------------------------------------------------------- 1 | output_filename,room_x,room_y,room_z,micL_x,micL_y,micR_x,micR_y,mic_z,s1_x,s1_y,s1_z,s2_x,s2_y,s2_z,T60 2 | 1.wav,6.473325013435549,7.652933778026471,3.191520786947495,3.0086280524665066,3.8766193186763487,3.119017247522075,4.005902827350052,1.49070016959827,4.4522351147929005,4.2890441493048295,1.222032244035194,4.85550303821435,4.651219420786221,1.6776378931915898,0.550916210459212 3 | 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551.wav,9.895536719095299,6.157041464420436,3.156948254658837,4.890539427846818,3.078557952111451,4.806627827533738,3.22640531709608,1.308928443636046,4.19317523232278,2.7594796847596994,1.6161088308954006,5.249755707702601,1.9948183061237175,1.6634863827513597,0.6039902027715702 553 | 552.wav,9.368458425553442,6.559259701622739,3.1214752299419457,4.654178507465726,3.156960881562896,4.534920512384174,3.065979946147126,1.5387999138940245,5.146682427048735,4.9697276279515075,0.9704146637712523,4.927862809970887,1.542993121530715,0.9291874144751857,0.5029479502038847 554 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tqdm 2 | numpy==1.23.0 3 | pyroomacoustics==0.3.1 4 | SoundFile==0.10.3.post1 5 | pandas==1.1.2 6 | scipy==1.5.2 7 | pyloudnorm==0.1.0 8 | -------------------------------------------------------------------------------- /run_sample_reverb.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import os 4 | from sample_reverb import draw_params 5 | import random 6 | 7 | SPLIT_NAMES = {'Train': 'tr'} 8 | 9 | SEED = 17 10 | np.random.seed(SEED) 11 | random.seed(SEED) 12 | 13 | FILELIST_STUB = os.path.join('data', 'mix_2_spk_filenames_librilight_{}.csv') 14 | REVERB_STUB = os.path.join('data', 'reverb_params_librilight_{}.csv') 15 | 16 | for split_long, split_short in SPLIT_NAMES.items(): 17 | print('Running {} Set'.format(split_long)) 18 | filelist_path = FILELIST_STUB.format(split_short) 19 | filelist_df = pd.read_csv(filelist_path) 20 | utt_ids = list(filelist_df['output_filename']) 21 | 22 | utt_list, param_list = [], [] 23 | for utt in utt_ids: 24 | mic_sp = random.randint(15, 17) 25 | rev_lvl = random.choice(["high","medium","low"]) 26 | room_params = draw_params(mic_sp/100., rev_lvl) 27 | 28 | room_dim = room_params[0] 29 | mics = room_params[1] 30 | s1 = room_params[2] 31 | s2 = room_params[3] 32 | T60 = room_params[4] 33 | 34 | param_dict = { 'room_x' : room_dim[0], 35 | 'room_y' : room_dim[1], 36 | 'room_z' : room_dim[2], 37 | 'micL_x' : mics[0][0], 38 | 'micL_y' : mics[0][1], 39 | 'micR_x' : mics[1][0], 40 | 'micR_y' : mics[1][1], 41 | 'mic_z' : mics[0][2], 42 | 's1_x' : s1[0], 43 | 's1_y' : s1[1], 44 | 's1_z' : s1[2], 45 | 's2_x' : s2[0], 46 | 's2_y' : s2[1], 47 | 's2_z' : s2[2], 48 | 'T60' : T60 } 49 | 50 | utt_list.append(utt) 51 | param_list.append(param_dict) 52 | 53 | reverb_param_df = pd.DataFrame(data=param_list, index=utt_list, 54 | columns=['room_x', 'room_y', 'room_z', 'micL_x', 'micL_y', 'micR_x', 'micR_y', 'mic_z', 's1_x', 's1_y', 's1_z', 's2_x', 's2_y', 's2_z', 'T60']) 55 | reverb_param_path = REVERB_STUB.format(split_short) 56 | reverb_param_df.to_csv(reverb_param_path, index=True, index_label='output_filename') 57 | -------------------------------------------------------------------------------- /run_sample_reverb_parallel.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import os 4 | from sample_reverb import draw_params 5 | import random 6 | import glob 7 | 8 | SPLIT_NAMES = {'Train': 'tr'} 9 | 10 | SEED = 17 11 | np.random.seed(SEED) 12 | random.seed(SEED) 13 | 14 | filelists = glob.glob('data/small/*.csv') 15 | for filelist in filelists: 16 | FILELIST_STUB = filelist 17 | REVERB_STUB = FILELIST_STUB.replace('mix_2_spk_filenames', 'reverb_params') 18 | 19 | for split_long, split_short in SPLIT_NAMES.items(): 20 | filelist_path = FILELIST_STUB 21 | filelist_df = pd.read_csv(filelist_path) 22 | utt_ids = list(filelist_df['output_filename']) 23 | 24 | utt_list, param_list = [], [] 25 | for utt in utt_ids: 26 | mic_sp = random.randint(15, 17) 27 | rev_lvl = random.choice(["high","medium","low"]) 28 | room_params = draw_params(mic_sp/100., rev_lvl) 29 | 30 | room_dim = room_params[0] 31 | mics = room_params[1] 32 | s1 = room_params[2] 33 | s2 = room_params[3] 34 | T60 = room_params[4] 35 | 36 | param_dict = { 'room_x' : room_dim[0], 37 | 'room_y' : room_dim[1], 38 | 'room_z' : room_dim[2], 39 | 'micL_x' : mics[0][0], 40 | 'micL_y' : mics[0][1], 41 | 'micR_x' : mics[1][0], 42 | 'micR_y' : mics[1][1], 43 | 'mic_z' : mics[0][2], 44 | 's1_x' : s1[0], 45 | 's1_y' : s1[1], 46 | 's1_z' : s1[2], 47 | 's2_x' : s2[0], 48 | 's2_y' : s2[1], 49 | 's2_z' : s2[2], 50 | 'T60' : T60 } 51 | 52 | utt_list.append(utt) 53 | param_list.append(param_dict) 54 | 55 | reverb_param_df = pd.DataFrame(data=param_list, index=utt_list, 56 | columns=['room_x', 'room_y', 'room_z', 'micL_x', 'micL_y', 'micR_x', 'micR_y', 'mic_z', 's1_x', 's1_y', 's1_z', 's2_x', 's2_y', 's2_z', 'T60']) 57 | reverb_param_path = REVERB_STUB.format(split_short) 58 | reverb_param_df.to_csv(reverb_param_path, index=True, index_label='output_filename') 59 | -------------------------------------------------------------------------------- /sample_reverb.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from numpy.random import uniform 3 | 4 | def draw_params(mic_width, reverb_level): 5 | 6 | room_dim = np.array([uniform(5, 10), 7 | uniform(5, 10), 8 | uniform(3, 4)]) 9 | 10 | center = np.array([room_dim[0]/2 + uniform(-0.2, 0.2), 11 | room_dim[1]/2 + uniform(-0.2, 0.2), 12 | uniform(0.9, 1.8)]) 13 | 14 | mic_theta = uniform(0, 2*np.pi) 15 | mic_offset = np.array([np.cos(mic_theta) * mic_width/2, 16 | np.sin(mic_theta) * mic_width/2, 17 | 0]) 18 | mics = np.array([center + mic_offset, 19 | center - mic_offset]) 20 | 21 | s1_dist = uniform(0.66, 2) 22 | s1_theta = uniform(0, 2*np.pi) 23 | s1_height = uniform(0.9, 1.8) 24 | s1_offset = np.array([np.cos(s1_theta) * s1_dist, 25 | np.sin(s1_theta) * s1_dist, 26 | s1_height - center[2]]) 27 | s1 = center + s1_offset 28 | 29 | s2_dist = uniform(0.66, 2) 30 | s2_theta = uniform(0, 2*np.pi) 31 | s2_height = uniform(0.9, 1.8) 32 | s2_offset = np.array([np.cos(s2_theta) * s2_dist, 33 | np.sin(s2_theta) * s2_dist, 34 | s2_height - center[2]]) 35 | s2 = center + s2_offset 36 | 37 | if reverb_level == "high": 38 | T60 = uniform(0.4, 1.0) 39 | elif reverb_level == "medium": 40 | T60 = uniform(0.2, 0.6) 41 | elif reverb_level == "low": 42 | T60 = uniform(0.1, 0.3) 43 | 44 | return [room_dim, mics, s1, s2, T60] 45 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import soundfile as sf 3 | from scipy.signal import resample_poly 4 | 5 | 6 | def read_scaled_wav(path, scaling_factor, start=0, end=None, sr=16000, mono=True): 7 | if end is not None: 8 | samples, sr_orig = sf.read(path, start=int(start*sr), stop=int(end*sr)) 9 | else: 10 | samples, sr_orig = sf.read(path) 11 | 12 | if len(samples.shape) > 1 and mono: 13 | samples = samples[:, 0] 14 | 15 | if sr != sr_orig: 16 | samples = resample_poly(samples, sr, sr_orig) 17 | 18 | samples /= np.max(np.abs(samples)) + 1e-8 19 | samples *= scaling_factor 20 | return samples 21 | 22 | 23 | def wavwrite_quantize(samples): 24 | return np.int16(np.round((2 ** 15) * samples)) 25 | 26 | 27 | def quantize(samples): 28 | int_samples = wavwrite_quantize(samples) 29 | return np.float64(int_samples) / (2 ** 15) 30 | 31 | 32 | def wavwrite(file, samples, sr): 33 | """This is how the old Matlab function wavwrite() quantized to 16 bit. 34 | We match it here to maintain parity with the original dataset""" 35 | int_samples = wavwrite_quantize(samples) 36 | sf.write(file, int_samples, sr, subtype='PCM_16') 37 | 38 | 39 | def fix_length(s1, s2, tag1, tag2, mode='fix', fixed_len=5, sr=16000): 40 | # mode: {'fix', 'min', 'max'} 41 | # tag: start time 42 | if mode == 'fix': 43 | # Fix length 44 | s1_out, s2_out = np.zeros(int(sr*fixed_len)), np.zeros(int(sr*fixed_len)) 45 | if s1.shape[0] < int(fixed_len*sr) - int(sr*tag1): # avoid out of shape 46 | s1_out[int(sr*tag1):s1.shape[0]+int(sr*tag1)] = s1 47 | else: 48 | s1_out[int(sr*tag1):] = s1[:(int(sr*fixed_len)-int(sr*tag1))] 49 | if s2.shape[0] < int(fixed_len*sr) - int(sr*tag2): # avoid out of shape 50 | s2_out[int(sr*tag2):s2.shape[0]+int(sr*tag2)] = s2 51 | else: 52 | s2_out[int(sr*tag2):] = s2[:(int(sr*fixed_len)-int(sr*tag2))] 53 | elif mode == 'min': 54 | utt_len = np.minimum(s1.shape[0]+int(sr*tag1), s2.shape[0]+int(sr*tag2)) 55 | s1_out, s2_out = np.zeros(utt_len), np.zeros(utt_len) 56 | s1_out[int(sr*tag1):] = s1[:(utt_len-int(sr*tag1))] 57 | s2_out[int(sr*tag2):] = s2[:(utt_len-int(sr*tag2))] 58 | else: # max 59 | utt_len = np.maximum(s1.shape[0]+int(sr*tag1), s2.shape[0]+int(sr*tag2)) 60 | s1_out, s2_out = np.zeros(utt_len), np.zeros(utt_len) 61 | s1_out[int(sr*tag1):s1.shape[0]+int(sr*tag1)] = s1 62 | s2_out[int(sr*tag2):s2.shape[0]+int(sr*tag2)] = s2 63 | return s1_out, s2_out 64 | 65 | 66 | def create_wham_mixes(s1_samples, s2_samples, noise_samples): 67 | mix_clean = s1_samples + s2_samples 68 | mix_single = noise_samples + s1_samples 69 | mix_both = noise_samples + s1_samples + s2_samples 70 | return mix_clean, mix_single, mix_both 71 | -------------------------------------------------------------------------------- /wham_room.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pyroomacoustics as pra 3 | from pyroomacoustics.parameters import constants 4 | from scipy.signal import resample_poly 5 | 6 | class WhamRoom(pra.room.ShoeBox): 7 | 8 | def __init__(self, p, mics, s1, s2, T60, fs=16000, 9 | t0=0., sigma2_awgn=None): 10 | 11 | self.T60 = T60 12 | self.max_rir_len = np.ceil(T60*fs).astype(int) 13 | 14 | volume = p[0]*p[1]*p[2] 15 | surface_area = 2*(p[0]*p[1] + p[0]*p[2] + p[1]*p[2]) 16 | absorption = 24 * volume * np.log(10.0) / (constants.get('c') * surface_area * T60) 17 | 18 | # minimum max order to guarantee complete filter of length T60 19 | max_order = np.ceil(T60 * constants.get('c') / min(p)).astype(int) 20 | 21 | super().__init__(p, fs=fs, t0=t0, absorption=absorption, 22 | max_order=max_order, sigma2_awgn=sigma2_awgn, 23 | sources=None, mics=None) 24 | 25 | self.add_source(s1) 26 | self.add_source(s2) 27 | 28 | self.add_microphone_array(pra.MicrophoneArray(np.array(mics).T, fs)) 29 | 30 | def add_audio(self, s1, s2): 31 | self.sources[0].add_signal(s1) 32 | self.sources[1].add_signal(s2) 33 | 34 | def compute_rir(self): 35 | 36 | self.rir = [] 37 | self.visibility = None 38 | 39 | self.image_source_model() 40 | 41 | for m, mic in enumerate(self.mic_array.R.T): 42 | h = [] 43 | for s, source in enumerate(self.sources): 44 | h.append(source.get_rir(mic, self.visibility[s][m], self.fs, self.t0)[:self.max_rir_len]) 45 | self.rir.append(h) 46 | 47 | def generate_rirs(self): 48 | 49 | original_max_order = self.max_order 50 | self.max_order = 0 51 | 52 | self.compute_rir() 53 | 54 | self.rir_anechoic = self.rir 55 | 56 | self.max_order = original_max_order 57 | 58 | self.compute_rir() 59 | 60 | self.rir_reverberant = self.rir 61 | 62 | def generate_audio(self, anechoic=False, fs=16000): 63 | 64 | if not self.rir: 65 | self.generate_rirs() 66 | if anechoic: 67 | self.rir = self.rir_anechoic 68 | else: 69 | self.rir = self.rir_reverberant 70 | audio_array = self.simulate(return_premix=True, recompute_rir=False) 71 | 72 | if type(fs) is not list: 73 | fs_array = [fs] 74 | else: 75 | fs_array = fs 76 | audio_out = [] 77 | for elem in fs_array: 78 | if type(elem) is str: 79 | elem = int(elem.replace('k','000')) 80 | if elem != self.fs: 81 | assert(self.fs % elem == 0) 82 | audio_out.append(resample_poly(audio_array, elem, self.fs, axis=2)) 83 | else: 84 | audio_out.append(audio_array) 85 | if type(fs) is not list: 86 | return audio_out[0] # array of shape (n_sources, n_mics, n_samples) 87 | else: 88 | return audio_out 89 | --------------------------------------------------------------------------------