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kaldi_form_preprocess.py
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kaldi_form_preprocess.py
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# extract fbanck from wav and save to file
# pre processd an audio in 0.09912s
# spk_ver_20180401_20180630_70_3_reseg_test: 7200 waves,952 speaker Extract audio features and save it as npy file, cost 236.61852288246155 seconds
# spk_ver_20180401_20180630_70_3_reseg_train: 70387 waves,09250 speaker. Extract audio features and save it as npy file, cost 1776.130244731903 seconds
# lls_85_70_libri410_fenbirec713_cmu_clsu_reseg_train: 302871 waves, 15137 speaker
import os
from glob import glob
from python_speech_features import fbank, delta
import librosa
import numpy as np
import pandas as pd
from multiprocessing import Pool
import silence_detector
import constants as c
from constants import SAMPLE_RATE
from time import time
np.set_printoptions(threshold=np.nan)
#pd.set_option('display.height', 1000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('max_colwidth', 100)
def find_files(directory, pattern='**/*.wav'):
"""Recursively finds all files matching the pattern."""
return glob(os.path.join(directory, pattern), recursive=True)
def VAD(audio):
chunk_size = int(SAMPLE_RATE*0.05) # 50ms
index = 0
sil_detector = silence_detector.SilenceDetector(20)
nonsil_audio=[]
while index + chunk_size < len(audio):
if not sil_detector.is_silence(audio[index: index+chunk_size]):
nonsil_audio.extend(audio[index: index + chunk_size])
index += chunk_size
return np.array(nonsil_audio)
def read_audio(filename, sample_rate=SAMPLE_RATE):
audio, sr = librosa.load(filename, sr=sample_rate, mono=True)
start_sec, end_sec = c.TRUNCATE_SOUND_SECONDS
start_frame = int(start_sec * SAMPLE_RATE)
end_frame = int(end_sec * SAMPLE_RATE)
audio = VAD(audio.flatten()[start_frame:]) #去掉前 0.2s 的 bit 提示音
if len(audio) < (end_frame - start_frame):
au = [0] * (end_frame - start_frame)
for i in range(len(audio)):
au[i] = audio[i]
audio = np.array(au)
return audio
def normalize_frames(m,epsilon=1e-12):
return [(v - np.mean(v)) / max(np.std(v),epsilon) for v in m]
def extract_features(signal=np.random.uniform(size=48000), target_sample_rate=SAMPLE_RATE):
filter_banks, energies = fbank(signal, samplerate=target_sample_rate, nfilt=64, winlen=0.025) #filter_bank (num_frames , 64),energies (num_frames ,)
#delta_1 = delta(filter_banks, N=1)
#delta_2 = delta(delta_1, N=1)
filter_banks = normalize_frames(filter_banks)
#delta_1 = normalize_frames(delta_1)
#delta_2 = normalize_frames(delta_2)
#frames_features = np.hstack([filter_banks, delta_1, delta_2]) # (num_frames , 192)
frames_features = filter_banks # (num_frames , 64)
num_frames = len(frames_features)
return np.reshape(np.array(frames_features),(num_frames, 64, 1)) #(num_frames,64, 1)
def prep(spk2utt,utt2path,out_dir=c.DATASET_DIR,name='0'):
start_time = time()
i = 0
for s2u in spk2utt:
speaker = s2u.split()[0]
utts = s2u.split()[1:]
for utt in utts:
i += 1
orig_time = time()
utt_id = utt.split('_')[:-1] #utr2spk 中的 utt id 是'ZEBRA-KIDS0000000_1735129_26445a50743aa75d_00000 去掉后面的 _000
utt_id = '_'.join(utt_id)
filepath = utt2path[utt_id]
#为了统一成和librispeech 格式一致 speaker与utt 用 '-'分割 speaker内部就用'_'
target_filepath = out_dir + speaker.replace('-','_') + '-' + utt_id.replace('-','_') + '.npy'
if os.path.exists(target_filepath):
if i % 10 == 0: print("task:{0} No.:{1} Exist File:{2}".format(name, i, filepath))
continue
raw_audio = read_audio(filepath)
if np.count_nonzero(raw_audio) < 1.0 * SAMPLE_RATE: #如果非静音部分小于 1s 则舍弃掉这个音频
continue
feature = extract_features(raw_audio, target_sample_rate=SAMPLE_RATE)
if feature.ndim != 3 or feature.shape[0] < c.NUM_FRAMES or feature.shape[1] != 64 or feature.shape[2] != 1:
print('there is an error in file:',filepath)
continue
np.save(target_filepath, feature)
if i % 100 == 0:
print("task:{0} cost time per audio: {1:.3f}s No.:{2} File name:{3}".format(name, time() - orig_time, i, filepath))
print("task %s runs %d seconds. %d files" %(name, time()-start_time,i))
def preprocess_and_save(kaldi_dir=c.WAV_DIR,out_dir=c.DATASET_DIR): #kaldi_dir='/Users/walle/PycharmProjects/Speech/coding/my_deep_speaker/audio/spk_ver_20180401_20180630_70_3_reseg_test'
orig_time = time()
with open(kaldi_dir+'/spk2utt','r') as f:
spk2utt = f.readlines()
with open(kaldi_dir+'/wav.scp','r') as f:
wav2path = f.readlines()
utt2path = {}
for wav in wav2path:
utt = wav.split()[0]
path = wav.split()[1]
utt2path[utt] = path
no_spk = min(len(spk2utt), 5000)
spk2utt = spk2utt[:no_spk]
print("extract fbank from audio and save as npy, using multiprocessing pool........ ")
num_proc = 5
p = Pool(num_proc)
patch = int(len(spk2utt)/num_proc)
for i in range(num_proc):
if i < num_proc - 1:
_spk2utt = spk2utt[i*patch: (i+1)*patch]
else:
_spk2utt = spk2utt[i*patch:]
print("task %s speakers num: %d" %(i, len(_spk2utt)))
p.apply_async(prep, args=(_spk2utt,utt2path,out_dir,i))
print('Waiting for all subprocesses done...')
p.close()
p.join()
print("Extract audio features and save it as npy file, cost {0} seconds".format(time()-orig_time))
print("*^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^* *^ˍ^*")
def test():
filename = 'audio/LibriSpeechSamples/train-clean-100/19/227/19-227-0036.wav'
raw_audio = read_audio(filename)
print(filename)
feature = extract_features(raw_audio, target_sample_rate=SAMPLE_RATE)
print(filename)
if __name__ == '__main__':
#test()
preprocess_and_save()