def cut_silence_in_sound(source, target, rmsTreshhold=-40, WndSize=128):
"""
source : fsource audio file
target : output sound
This function cuts the silence at the begining and at the end of an audio file in order.
It's usefull for normalizing the length of the audio stimuli in an experiment.
The default parameters were tested with notmal speech.
"""
NbofWrittendFiles = 1
x, fs, enc = wavread(str(source))
index = 0
#Remove the silence at the begining
while index + WndSize < len(x):
DataArray = x[index:index + WndSize]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
index = 0.5 * WndSize + index
if rms > rmsTreshhold:
end = 0
beginning = index
print beginning / 44100
break
#Remove the silence at the end
x, fs, enc = wavread(str(source))
WndSize = 128
index = 0
x = list(reversed(x))
while index + WndSize < len(x):
DataArray = x[int(index):int(index + WndSize)]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
index = 0.5 * WndSize + index
if rms > rmsTreshhold:
end = 0
final = index
print(len(x) - final) / 44100
break
#write the sound source without silences
x, fs, enc = wavread(str(source))
WndSize = 128
rmsTreshhold = -70
index = 0
name_of_source = str(os.path.basename(source))
name_of_source = os.path.splitext(name_of_source)[0]
path, sourcename = os.path.split(source)
wavwrite(x[beginning:len(x) - final], target, fs, enc='pcm24')
def find_silence(audio_file, threshold = -65, wnd_size = 16384): """ find a segment of silence (<threshold dB)in the sound file return tag in seconds """ try: x, fs, enc = aiffread(str(audio_file)) except: x, fs, enc = wavread(str(audio_file)) index = 0 NbofWrittendFiles = 1 silence_tags = [] while index + wnd_size < len(x): DataArray = x[index: index + wnd_size] rms = np.sqrt(np.mean(np.absolute(DataArray)**2)) rms = lin2db(rms) index = wnd_size + index if rms < threshold: end = 0 begining = index index = wnd_size + index while rms < threshold: if index + wnd_size < len(x): index = wnd_size + index DataArray = x[index: index + wnd_size] rms = np.sqrt(np.mean(np.absolute(DataArray)**2)) rms = lin2db(rms) end = index else: break #if file is over 250 ms long, write it if (end - begining) > (fs / 8) : begining = begining - wnd_size if begining < 0: begining = 0 end = end + wnd_size if end > len(x): end = len(x) #samples to seconds, minutes, hours begining_s = begining/float(fs) end_s = end/float(fs) silence_tags.append([begining_s, end_s]) return silence_tags
def get_sound_without_silence(source, rmsTreshhold=-40, WndSize=128):
"""
source : source audio file
This function returns a begining and end time tags for the begining and the end of audio in a file
"""
x, fs, enc = wavread(str(source))
index = 0
#Remove the silence at the begining
while index + WndSize < len(x):
DataArray = x[index:index + WndSize]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
index = 0.5 * WndSize + index
if rms > rmsTreshhold:
end = 0
beginning = index
break
#Remove the silence at the end
x, fs, enc = wavread(str(source))
WndSize = 128
index = 0
x = list(reversed(x))
while index + WndSize < len(x):
DataArray = x[int(index):int(index + WndSize)]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
index = 0.5 * WndSize + index
if rms > rmsTreshhold:
end = 0
final = index
break
#write the sound source without silences
x, fs, enc = wavread(str(source))
WndSize = 128
rmsTreshhold = -70
index = 0
end = len(x) - final
return beginning / fs, end / fs
def get_rms_from_data(data, in_db = True):
"""
Returns the root-mean-square (power) of the audio buffer
"""
from conversions import lin2db
rms = np.sqrt(np.mean(data**2))
if in_db:
rms = lin2db(rms)
return rms
def get_matrix_values(sdif):
"""
load data from ascii or SDIF file
return time-tagged values and matrix data
return tlist, Matrix_data
This can be used to extract data from lpc or true env .sdif files
"""
inent = eaSDIF.Entity()
res = inent.OpenRead(sdif)
if res == False:
raise RuntimeError("get_lpc:: " + sdif +
" is no sdif file or does not exist")
dlist = []
tlist = []
vec = eaSDIF.Vector()
frame = eaSDIF.Frame()
#fft size
#intypes = inent.GetTypeString() # Very practical line fr printing what is inside
for frame in inent:
has_IGBG = frame.MatrixExists("IGBG")
if has_IGBG:
mat = frame.GetMatrixWithSig("IGBG")
mat.GetRow(vec, 0)
sample_rate = vec[1]
fftsize = vec[3]
fftsize = int(fftsize / 2)
#Extract time tag values
for frame in inent:
mat = frame.GetMatrix(1)
nrow = mat.GetNbRows()
ncol = mat.GetNbCols()
if nrow > 1 and ncol > 0:
tlist.append(frame.GetTime())
#Extract Matrix data values
for frame in inent:
for i in range(0, fftsize + 1):
mat = frame.GetMatrix(1)
nrow = mat.GetNbRows()
ncol = mat.GetNbCols()
if nrow > 1 and ncol >= 0:
mat.GetRow(vec, i)
dlist.append(float(np.array((vec)[0])))
#Convert dlist into a matrix
sample_nb = len(
tlist) - 1 # Because the first value in tlist should be ignored
fftsize_range = fftsize + 1
sample_nb_range = sample_nb + 1
matrix_data = np.zeros((sample_nb_range, fftsize_range))
for row in range(0, sample_nb_range):
for col in range(0, fftsize_range):
matrix_data[row][col] = dlist[row * (fftsize_range) + col]
#when using the flag -OS1 in super vp the amplitude values are in linear, here we transform it to db so the amplitudes are in db
from conversions import lin2db
matrix_data = lin2db(matrix_data)
return tlist, matrix_data
def index_wav_file(source,
rms_threshold=-50,
WndSize=16384,
target_folder="Indexed"):
"""
input:
source : fsource audio file
rms_threshold : this is the threshold
WndSize : window size to compue the RMS on
target_folder : folder to save the extracted sounds in
This function separates all the sentences inside an audiofile.
It takes each sentence and put it into one audio file inside target_folder with the name target_nb
The default parameters were tested with notmal speech.
Only works if file is at least 500 ms long, which can be tuned
You can change the rms threshold to tune the algorithm
"""
try:
x, fs, enc = aiffread(str(source))
except:
x, fs, enc = wavread(str(source))
index = 0
NbofWrittendFiles = 1
while index + WndSize < len(x):
DataArray = x[index:index + WndSize]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
index = WndSize + index
if rms > rms_threshold:
end = 0
begining = index
index = WndSize + index
while rms > rms_threshold:
if index + WndSize < len(x):
index = WndSize + index
DataArray = x[index:index + WndSize]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
end = index
else:
break
#if file is over 500 ms long, write it
if (end - begining) > (fs / 2):
duree = (end - begining) / float(fs)
print "duree : " + str(duree)
begining = begining - WndSize
if begining < 0:
begining = 0
end = end + WndSize
if end > len(x):
end = len(x)
print file
sound_tag = os.path.basename(source)
sound_tag = os.path.splitext(sound_tag)[0]
try:
os.mkdir(target_folder)
except:
pass
#write(filename = target_folder+"/"+ sound_tag + "_" + str(NbofWrittendFiles)+".wav",rate = fs, data= x[begining:end])
wavwrite(x[begining:end],
target_folder + "/" + sound_tag + "_" +
str(NbofWrittendFiles) + ".wav",
fs,
enc='pcm24')
NbofWrittendFiles = NbofWrittendFiles + 1
def extract_sentences_tags(source,
rms_threshold=-50,
WndSize=16384,
overlap=8192):
"""
This function separates all the sentences inside an audiofile.
It takes each sentence and put it into one audio file inside target_folder with the name target_nb
The default parameters were tested with notmal speech.
Only works if file is at least 500 ms long, which can be tuned
You can change the rms threshold to tune the algorithm
input:
source : fsource audio file
rms_threshold : this is the threshold
WndSize : window size to compue the RMS on
overlap : nb of overlap samples
returns:
tags in pairs of [begining end]
"""
try:
x, fs, enc = aiffread(str(source))
except:
x, fs, enc = wavread(str(source))
index = 0
NbofWrittendFiles = 1
tags = []
vid_lengths = []
while index + WndSize < len(x):
DataArray = x[index:index + WndSize]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
index = WndSize + index
#index += overlap
if rms > rms_threshold:
end = 0
begining = index
index = WndSize + index
#index += overlap
while rms > rms_threshold:
if index + WndSize < len(x):
index = WndSize + index
#index += overlap
DataArray = x[index:index + WndSize]
rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
rms = lin2db(rms)
end = index
else:
break
#if file is over 500 ms long, write it
if (end - begining) > (fs / 2):
begining = begining - WndSize
if begining < 0:
begining = 0
end = end + WndSize
if end > len(x):
end = len(x)
#samples to seconds, minutes, hours
begining_s = begining / float(fs)
end_s = end / float(fs)
len_s = (end - begining) / float(fs)
print "duree : " + str(len_s)
from datetime import timedelta, datetime
begining_s = datetime(1, 1, 1) + timedelta(seconds=begining_s)
end_s = datetime(1, 1, 1) + timedelta(seconds=end_s)
len_s = datetime(1, 1, 1) + timedelta(seconds=len_s)
begining_s = "%d:%d:%d.%3d" % (
begining_s.hour, begining_s.minute, begining_s.second,
begining_s.microsecond)
end_s = "%d:%d:%d.%3d" % (end_s.hour, end_s.minute,
end_s.second, end_s.microsecond)
len_s = "%d:%d:%d.%3d" % (len_s.hour, len_s.minute,
len_s.second, len_s.microsecond)
print "la longueur est"
print len_s
tags.append([begining_s, end_s])
vid_lengths.append(len_s)
NbofWrittendFiles = NbofWrittendFiles + 1
return tags, vid_lengths