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generator_paper.py
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generator_paper.py
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import os
import glob
import tqdm
import torch
import random
import librosa
import argparse
import numpy as np
from multiprocessing import Pool, cpu_count
from utils.audio import Audio
from utils.hparams import HParam
import pandas as pd
def formatter(dir_, form, num):
return os.path.join(dir_, form.replace('*', '%06d' % num))
def vad_merge(w):
intervals = librosa.effects.split(w, top_db=20)
temp = list()
for s, e in intervals:
temp.append(w[s:e])
return np.concatenate(temp, axis=None)
def mix(out_dir,hp, args, audio, num, s1_dvec, s1_target, s2):
dir_ = out_dir
srate = hp.audio.sample_rate
d, _ = librosa.load(s1_dvec, sr=srate)
w1, _ = librosa.load(s1_target, sr=srate)
w2, _ = librosa.load(s2, sr=srate)
assert len(d.shape) == len(w1.shape) == len(w2.shape) == 1, \
'wav files must be mono, not stereo'
d, _ = librosa.effects.trim(d, top_db=20)
w1, _ = librosa.effects.trim(w1, top_db=20)
w2, _ = librosa.effects.trim(w2, top_db=20)
# if reference for d-vector is too short, discard it
if d.shape[0] < 1.1 * hp.embedder.window * hp.audio.hop_length:
return
# I think random segment length will be better, but let's follow the paper first
# fit audio to `hp.data.audio_len` seconds.
# if merged audio is shorter than `L`, discard it
L = int(srate * hp.data.audio_len)
if w1.shape[0] < L or w2.shape[0] < L:
return
w1, w2 = w1[:L], w2[:L]
mixed = w1 + w2
norm = np.max(np.abs(mixed)) * 1.1
w1, w2, mixed = w1/norm, w2/norm, mixed/norm
# save vad & normalized wav files
target_wav_path = formatter(dir_, hp.form.target.wav, num)
mixed_wav_path = formatter(dir_, hp.form.mixed.wav, num)
librosa.output.write_wav(target_wav_path, w1, srate)
librosa.output.write_wav(mixed_wav_path, mixed, srate)
# save magnitude spectrograms
target_mag, _ = audio.wav2spec(w1)
mixed_mag, _ = audio.wav2spec(mixed)
target_mag_path = formatter(dir_, hp.form.target.mag, num)
mixed_mag_path = formatter(dir_, hp.form.mixed.mag, num)
torch.save(torch.from_numpy(target_mag), target_mag_path)
torch.save(torch.from_numpy(mixed_mag), mixed_mag_path)
# save selected sample as text file. d-vec will be calculated soon
dvec_text_path = formatter(dir_, hp.form.dvec, num)
with open(dvec_text_path, 'w') as f:
f.write(s1_dvec)
if __name__ == '__main__':
def train_wrapper(num):
clean_utterance_path, embedding_utterance_path, interference_utterance_path = train_data[num]
mix(output_dir_train, hp, args, audio, num, embedding_utterance_path, clean_utterance_path, interference_utterance_path)
#mix_wavfiles(output_dir_train, sample_rate, audio_len, ap, form, num, embedding_utterance_path, interference_utterance_path, clean_utterance_path)
def test_wrapper(num):
clean_utterance_path, embedding_utterance_path, interference_utterance_path = test_data[num]
mix(output_dir_test, hp, args, audio, num, embedding_utterance_path, clean_utterance_path, interference_utterance_path)
#mix_wavfiles(output_dir_test, sample_rate, audio_len, ap, form, num, embedding_utterance_path, interference_utterance_path, clean_utterance_path)
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, required=True,
help="Config json file")
parser.add_argument('-r', '--dataset_root_dir', type=str, required=True,
help="Config yaml file")
parser.add_argument('-d', '--train_data_csv', type=str, required=True,
help="Train Data csv contains rows [clean_utterance,embedding_utterance,interference_utterance] example in datasets/LibriSpeech/train.csv")
parser.add_argument('-t', '--test_data_csv', type=str, required=True,
help="Test Data csv contains rows [clean_utterance,embedding_utterance,interference_utterance] example in datasets/LibriSpeech/dev.csv")
parser.add_argument('-o', '--out_dir', type=str, required=True,
help="Directory of output training triplet")
parser.add_argument('-l', '--librispeech', type=str, required=False, default=False,
help="Librispeech format, if true load with librispeech format")
args = parser.parse_args()
os.makedirs(args.out_dir, exist_ok=True)
os.makedirs(os.path.join(args.out_dir, 'train'), exist_ok=True)
os.makedirs(os.path.join(args.out_dir, 'test'), exist_ok=True)
cpu_num = cpu_count() # num threads = num cpu cores
hp = HParam(args.config)
audio = Audio(hp)
output_dir_train = os.path.join(args.out_dir, 'train')
output_dir_test = os.path.join(args.out_dir, 'test')
dataset_root_dir = args.dataset_root_dir
train_data_csv = pd.read_csv(args.train_data_csv, sep=',').values
test_data_csv = pd.read_csv(args.test_data_csv, sep=',').values
train_data = []
test_data = []
if args.librispeech:
for c, e, i in train_data_csv:
splits = c.split('-')
target_path = os.path.join(dataset_root_dir, splits[0], splits[1], c+'-norm.wav')
splits = e.split('-')
emb_ref_path = os.path.join(dataset_root_dir, splits[0], splits[1], e+'-norm.wav')
splits = i.split('-')
interference_path = os.path.join(dataset_root_dir, splits[0], splits[1], i+'-norm.wav')
train_data.append([target_path, emb_ref_path, interference_path])
for c, e, i in test_data_csv:
splits = c.split('-')
target_path = os.path.join(dataset_root_dir, splits[0], splits[1], c+'-norm.wav')
splits = e.split('-')
emb_ref_path = os.path.join(dataset_root_dir, splits[0], splits[1], e+'-norm.wav')
splits = i.split('-')
interference_path = os.path.join(dataset_root_dir, splits[0], splits[1], i+'-norm.wav')
test_data.append([target_path, emb_ref_path, interference_path])
else:
for c, e, i in train_data_csv:
train_data.append([os.path.join(dataset_root_dir,c), os.path.join(dataset_root_dir,e), os.path.join(dataset_root_dir,i)])
for c, e, i in test_data_csv:
test_data.append([os.path.join(dataset_root_dir,c), os.path.join(dataset_root_dir,e), os.path.join(dataset_root_dir,i)])
train_idx = list(range(len(train_data)))
with Pool(cpu_num) as p:
r = list(tqdm.tqdm(p.imap(train_wrapper, train_idx), total=len(train_idx)))
test_idx = list(range(len(test_data)))
with Pool(cpu_num) as p:
r = list(tqdm.tqdm(p.imap(test_wrapper, test_idx), total=len(test_idx)))