def read_sound(fp):
"""
create a normalized float array and datarate from any audo file
"""
if fp.endswith('mp3'):
try:
oname = 'temp.wav'
#cmd = 'lame --decode "{0}" {1}'.format( fp ,oname )
result = subprocess.call(['lame', '--decode', fp, oname])
assert(result is 0)
samplerate, data = wav.read(oname)
except:
print "couldn't run lame"
try:
import moviepy.editor as mpy
aud_clip = mpy.AudioFileClip(fp)
samplerate = aud_clip.fps
data = aud_clip.to_soundarray()
except:
print "moviepy not installed?"
if fp.endswith('aif'):
#sf = aifc.open(fp)
oname = fp
sf = Sndfile(fp, 'r')
sf.seek(0)
data = sf.read_frames(sf.nframes)
samplerate = sf.samplerate
if fp.endswith('wav'):
samplerate, data = wav.read(fp)
if len(data.shape)>1: data = data[:,0]
data = data.astype('float64')
data /= data.max()
return data, samplerate
def test_energy_denois(self):
original_file = self.auxiliary_files_url + "/nai_sample.wav"
denoised_file = self.auxiliary_files_url + "/nai_sample_energy_denoised.wav"
result = self.energy_denoise_module.energyDenoise(\
original_file,\
0.2,\
denoised_file,\
False)
# The function thinks the denoising succeded
self.assertEqual(result, True)
# Check for the denoised file
denoised_exists = os.path.isfile(denoised_file)
self.assertEqual(denoised_exists, True)
# Check if denoised energy is lower than the initial one
samp_freq, signal_orig = wavfile.read(original_file)
energy_orig = 0.0
for i in signal_orig:
energy_orig += i * 1.0 * i
samp_freq, signal_denoised = wavfile.read(denoised_file)
energy_denoised = 0.0
for i in signal_denoised:
energy_denoised += i * 1.0 * i
self.assertGreater(energy_orig, energy_denoised)
# erase the denoised file
os.remove(denoised_file)
def loadReferenceWords(self, word1_path, word2_path):
fs, self.word1 = wavfile.read(word1_path)
fs, self.word2 = wavfile.read(word2_path)
self.word1 = self.scaler(self.word1)
self.word2 = self.scaler(self.word2)
self.word1 = self.word1[self.get_startingpoint(self.word1):self.get_endingpoint(self.word1),:]
self.word2 = self.word2[self.get_startingpoint(self.word2):self.get_endingpoint(self.word2),:]
def remove_silence(filename):
(rate,sig) = wav.read(filename)
framelength = int(round(FRAMELENGTH * rate))
frameamount = int(math.ceil(len(sig) / framelength))
newsig = np.array([])
for i in xrange(0,frameamount+1):
start = i * framelength
end = start + framelength
print end
if (end > len(sig)):
end = len(sig)
if (start >= len(sig)):
start = len(sig) - 1
length = end - start
energy = 0.0
for j in xrange(start,end):
energy = energy + pow(float(sig[j]), 2)
energy = energy / length
if (energy >= TRESHOLD):
newsig = np.concatenate((newsig,sig[start:end]))
newsig = newsig.astype(sig.dtype)
print "silence removed, saving: "+ filename+".sr"
wav.write(filename+".sr", rate, newsig)
(rate,sig) = wav.read(filename+".sr")
def cut_video(recording_path, datapack_dir):
# Read the start/end pattern
sr1, pattern_wav = wav.read('pattern.wav')
workingdir = tempfile.mkdtemp()
# Open the video file
clip = VideoFileClip(recording_path)
# Save its audio track temporarily on disk
clip.audio.write_audiofile(os.path.join(workingdir,"temp_audio.wav"))
# Read the audio samples, mix down to mono (if necessary), and delete the temporary audio track
sr2, recording_wav = wav.read(os.path.join(workingdir,"temp_audio.wav"))
if recording_wav.shape[1]>1:
recording_wav = numpy.mean(recording_wav,1)
shutil.rmtree(workingdir)
# Detect the start and end audio pattern
start, end = detect_start_end_times(pattern_wav, recording_wav, sr2, 4)
# Cut the video and write it into two separate video and audio files
clip.subclip(start+0.4, end).write_videofile(os.path.join(datapack_dir, 'video.mp4'), codec='libx264')
clip.subclip(start+0.4, end).audio.write_audiofile(os.path.join(datapack_dir,'audio.wav'))
def extract_features(recording_files, nr_ceps=12):
print("skipping features")
return Ceps()(range(100))
nr_utt_in_ubm = 300
win_length_ms = 25 # The window length of the cepstral analysis in milliseconds
win_shift_ms = 10 # The window shift of the cepstral analysis in milliseconds
nr_filters = 24 # NOTSURE The number of filter bands
nr_ceps = nr_ceps # The number of cepstral coefficients
f_min = 0. # NOTSURE The minimal frequency of the filter bank
f_max = 4000. # NOTSURE The maximal frequency of the filter bank
delta_win = 2 # NOTSURE The integer delta value used for computing the first and second order derivatives
pre_emphasis_coef = 0.97 # NOTSURE The coefficient used for the pre-emphasis
dct_norm = True # NOTSURE A factor by which the cepstral coefficients are multiplied
mel_scale = True # Tell whether cepstral features are extracted on a linear (LFCC) or Mel (MFCC) scale
# TODO add feature wrapping
if glob.has_magic(recording_files):
recording_files = glob.glob(recording_files)
rate, ubm_wav = wavfile.read(recording_files.pop())
for recording_file in recording_files:
rate, signal = wavfile.read(recording_file)
ubm_wav = np.append(ubm_wav, signal)
c = Ceps(rate, win_length_ms, win_shift_ms, nr_filters, nr_ceps, f_min, f_max, delta_win, pre_emphasis_coef,
mel_scale, dct_norm)
ubm_wav = np.cast['float'](ubm_wav) # vector should be in **float**
mfcc = c(ubm_wav)
return mfcc
def get_data(path):
"""
Gets the data associated with an audio file, converting to wav when necessary.
:param path: path to audio file
:return: sample rate, audio data
"""
if path.endswith(".wav"):
bee_rate, bee_data = read(path)
else:
temp = tempfile.NamedTemporaryFile(suffix=".wav")
temp.close()
if path.endswith(".flac"):
sound = AudioSegment.from_file(path, "flac")
sound.export(temp.name, format="wav")
elif path.endswith(".mp3"):
sound = AudioSegment.from_file(path, "mp3")
sound.export(temp.name, format="wav")
bee_rate, bee_data = read(temp.name)
os.remove(temp.name)
data_type = np.iinfo(bee_data.dtype)
dmin = data_type.min
dmax = data_type.max
bee_data = bee_data.astype(np.float64)
bee_data = 2.0 * ((bee_data - dmin) / (dmax - dmin)) - 1.0
bee_data = bee_data.astype(np.float32)
return bee_rate, bee_data
def main(argv):
if len(argv) == 6:
window_size = int(argv[1])
a_fname = argv[2]
b_fname = argv[3]
a_phase_b_mag_fname = argv[4]
b_phase_a_mag_fname = argv[5]
else:
print(
'Usage: %s ' % argv[0] +
'<WINDOW_WIDTH> <FILE_1> <FILE_2> <OUTFILE_1> <OUTFILE_2>' + (
'\n\nSwap magnitude and phase of WAV files FILE_1 and FILE_2.'
'\n\nWINDOW_WIDTH: STFT frame length (integer # of samples)'
'\nOUTFILE_1: phase of FILE_1, magnitude of FILE_2'
'\nOUTFILE_2: phase of FILE_2, magnitude of FILE_1'))
return 1
a_rate, a = wavfile.read(a_fname)
b_rate, b = wavfile.read(b_fname)
assert a_rate == b_rate
assert a.dtype == b.dtype
print('Window width: %d samples = %.3f ms' %
(window_size, 1e3 * window_size / a_rate))
a_phase_b_mag, b_phase_a_mag = swap_wav_magnitude(a, b, window_size)
wavfile.write(a_phase_b_mag_fname, a_rate, a_phase_b_mag)
wavfile.write(b_phase_a_mag_fname, a_rate, b_phase_a_mag)
return 0
def mainfn():
print "In comb4.py"
#Read the file
f = wavfile.read('/home/akshay/anaconda/ModulesPython/MUSICGEVD/sep_0.wav')
rdata1 = f[1]
fs = f[0]
print "Sampling frequency: " + str(fs)
print "shape of rdata1: ", str(rdata1.shape)
#rdata1 = np.matrix(rdata1)
#print "shape of rdata1: ", str(rdata1.shape)
g = wavfile.read('/home/akshay/anaconda/ModulesPython/MUSICGEVD/sep_1.wav')
rdata2 = g[1]
#rdata2 = np.matrix(rdata2)
h = wavfile.read('/home/akshay/anaconda/ModulesPython/MUSICGEVD/sep_2.wav')
rdata3 = h[1]
#rdata3 = np.matrix(rdata3)
i = wavfile.read('/home/akshay/anaconda/ModulesPython/MUSICGEVD/sep_3.wav')
rdata4 = i[1]
#rdata4 = np.matrix(rdata4)
rdata = np.array([[rdata1],[rdata2],[rdata3],[rdata4]])
rdatat = rdata[:,0,:]
print "Shape of rdatat: ", str(rdatat.shape)
return(rdatat)
def output(partIdx):
"""Uses the student code to compute the output for test cases."""
outputString = ''
if partIdx == 0: # This is ScaledFFTdB
from assignment1 import scaled_fft_db
r,x = wavfile.read('data/a1_submissionInput.wav')
X = scaled_fft_db(x)
for val in X:
outputString += '%.5f ' % (val)
elif partIdx == 1: # This is PrototypeFilter
from assignment2 import prototype_filter
h = prototype_filter()
# test signal
s = np.loadtxt('data/a2_submissionInput.txt')
r = np.convolve(h, s)[4*512:5*512]/2
for val in r:
outputString += '%.5f ' % val
elif partIdx == 2: # This is SubbandFiltering
from assignment3 import subband_filtering
r,x = wavfile.read('data/a3_submissionInput.wav')
h = np.hanning(512)
X = subband_filtering(x, h)
for val in X:
outputString += '%.5f ' % (val)
elif partIdx == 3: # This is Quantization
from assignment4 import quantization
from parameters import EncoderParameters
params = EncoderParameters(44100, 2, 64)
val_in = np.loadtxt('data/a4_submissionInput.txt')
for r,row in enumerate(val_in):
val = row[0]
scf = row[1]
ba = int(row[2])
QCa = params.table.qca[ba-2]
QCb = params.table.qcb[ba-2]
val = quantization(val, scf, ba, QCa, QCb)
outputString += '%d ' % (val)
return outputString.strip()
def processing():
"""post-processing of MLSbuf and recBuf, using the matched filter functions"""
# -- start recording and playback in async. mode
play_while_recording()
global SAMPLE_RATE
# -- latency for input and output devs, obtained using portaudio pa_devs script
inputLatency = 0.0087
outputLatency = 0.0087
# -- convert latencies to num. of samples
latencySamples = math.ceil((inputLatency+outputLatency)*SAMPLE_RATE)
# -- calibration samples (uncomment for debugging)
calSamp = 52
# -- load recording buffer into numpy array
recData = read("recBuf.wav")
recBuf = np.array(recData[1],dtype =float)
# -- index of internal delays & calibritation samples to subtract
interDelaySamp = np.s_[0:(latencySamples + calSamp)]
recBuf = np.delete(recBuf,interDelaySamp)
# -- remove excess samples from the recording buffer
removeExcessSamples = np.s_[6000:]
recBuf = np.delete(recBuf,removeExcessSamples)
# -- load playback buffer
MLSdata = read("MLS.wav")
MLSbuf = np.array(MLSdata[1],dtype =float)
# -- compute delay using Matched Filters & normalize
xcorr = matched_filter(MLSbuf,recBuf)/50000000000.0
# -- get gain
gain = get_gain(MLSbuf,recBuf)
# -- peak detector
prop_delay = peak_detector(xcorr)
# -- plot recorded seq, Tx MLS seq. (uncomment for debugging)
plt.figure(1)
plt.plot(MLSbuf)
plt.title("MLS sequence")
plt.xlabel("samples")
plt.grid(True)
plt.figure(2)
plt.plot(recBuf)
plt.title("Recorded MLS sequence")
plt.xlabel("samples")
plt.ylabel("Amplitude")
plt.grid(True)
plt.figure(3)
plt.plot(abs(xcorr))
plt.title("Matched Filter Output")
plt.xlabel("delay (samples)")
plt.ylabel("Rxy")
plt.grid(True)
plt.show()
def plot_from_wavfile(file1, file2):
'''
Given two wav files, plot their frequency spectrums
'''
rate1, data1 = wavefile.read(file1)
rate2, data2 = wavefile.read(file2)
plot_from_rawdata(data1, data2, rate1)
def test_write_edge_values(self):
# Write edge values 1.0
samples = numpy.ones((441, 1), dtype=numpy.float32)
dest_file = NamedTemporaryFile(delete=True)
wfile, infos = wav.open_write_mode(dest_file.name, 44100, 1)
wav.write_block(wfile, samples)
wfile._file.flush() # To force the file to be written to the disk
frame_rate, samples_written = sp_wavfile.read(dest_file.name)
numpy.testing.assert_array_equal(samples_written, numpy.array([2**15 - 1] * 441, dtype=numpy.int16))
dest_file.close()
# Write value 2.0, clipped to 1.0
samples = numpy.ones((441, 1), dtype=numpy.float32) * 2.0
dest_file = NamedTemporaryFile(delete=True)
wfile, infos = wav.open_write_mode(dest_file.name, 44100, 1)
wav.write_block(wfile, samples)
wfile._file.flush() # To force the file to be written to the disk
frame_rate, samples_written = sp_wavfile.read(dest_file.name)
numpy.testing.assert_array_equal(samples_written, numpy.array([2**15 - 1] * 441, dtype=numpy.int16))
dest_file.close()
# Write edge values -1.0
samples = numpy.ones((441, 1), dtype=numpy.float32) * -1
dest_file = NamedTemporaryFile(delete=True)
wfile, infos = wav.open_write_mode(dest_file.name, 44100, 1)
wav.write_block(wfile, samples)
wfile._file.flush() # To force the file to be written to the disk
frame_rate, samples_written = sp_wavfile.read(dest_file.name)
numpy.testing.assert_array_equal(samples_written, numpy.array([-2**15] * 441, dtype=numpy.int16))
dest_file.close()
def __init__(self, snd, fps=None, bitrate=3000):
Clip.__init__(self)
if isinstance(snd, str):
if not snd.endswith('.wav'):
temp = 'temp.wav'
ffmpeg.extract_sound(snd, temp, fps, bitrate)
fps, arr = wavfile.read(temp)
# os.remove(temp)
else:
fps, arr = wavfile.read(snd)
self.array = arr
self.fps = fps
else:
self.array = snd
self.fps = fps
self.duration = 1.0 * len(self.array) / self.fps
def gf(t):
i = int(self.fps * t)
if i < 0 or i >= len(self.array):
return 0
else:
return self.array[i]
self.get_frame = gf
def test_realFile(self):
original_file = self.auxiliary_files_url + "/nai_sample.wav"
denoised_file = self.auxiliary_files_url + "/nai_sample_sox_denoised.wav"
user = '******'
audio_type = 'nao_wav_1_ch'
scale = 0.2
result = self.sox_denoise_module.soxDenoise(\
user,\
audio_type,\
original_file,\
denoised_file,\
scale)
# The function thinks the denoising succeded
self.assertEqual(result, "true")
# Check for the denoised file
denoised_exists = os.path.isfile(denoised_file)
self.assertEqual(denoised_exists, True)
# Check if denoised energy is lower than the initial one
samp_freq, signal_orig = wavfile.read(original_file)
energy_orig = 0.0
for i in signal_orig:
energy_orig += i * 1.0 * i
samp_freq, signal_denoised = wavfile.read(denoised_file)
energy_denoised = 0.0
for i in signal_denoised:
energy_denoised += i * 1.0 * i
self.assertGreater(energy_orig, energy_denoised)
# erase the denoised file
os.remove(denoised_file)
def get_offset_wav(wav_filename1, wav_filename2, time_limit=300):
"""Return offset in seconds between wav_filename1 and
wav_filename2, which are recordings of the same event
with potentially different starting times. Returns the
number of seconds that wav_filename2 starts after wav_filename1
(possibly negative).
If time_limit is provided, clip files
to first time_limit seconds. This can substantially speed up
offset detection"""
rate1, data1 = sp_wav.read(wav_filename1)
rate2, data2 = sp_wav.read(wav_filename2)
# the two files must have the same sampling rate
assert(rate1==rate2)
if time_limit is not None:
data1 = data1[0:rate1 * time_limit]
data2 = data2[0:rate2 * time_limit]
offset_samples = get_offset_xcorr(data1, data2)
offset_seconds = offset_samples / float(rate1)
return offset_seconds
def perf_eval(param):
# wrtitten in 3000 basis, finding the nperseg value from param
nperseg=param % 3000
# wrtitten in 3000 basis, finding the number of music segments
num_of_seg_idx=(param-nperseg)/3000
num_of_seg=num_of_segs[num_of_seg_idx]
input_rate,input_sig=wavfile.read(input_dir+song_name+'.wav')
output_rate,output_sig=wavfile.read(output_dir+song_name+'.wav')
#the +1 in denominator is because we exclude the last piece of music to only
# consider music pieces of the same size.
input_seg_len=input_sig.shape[0]/(num_of_seg+1)
output_seg_len=output_sig.shape[0]/(num_of_seg+1)
if input_rate!=output_rate:
print ("Rate Mistmatch!")
sys.exit(0)
#print (nperseg,nperseg_step,input_seg_len,output_seg_len)
if np.min((input_seg_len,output_seg_len))*0.7 < nperseg * nperseg_step:
print ("Nothing to do!")
sys.exit(0)
res=estim_diff(input_sig, input_seg_len, output_sig, output_seg_len, nperseg, num_of_seg, nperseg_step)
f=open('/agbs/cluster/naji/Linear Filters/Echo/out/Winter/Room/'+str(num_of_seg)+'/'+str(nperseg)+'.txt','w')
print (nperseg,file=f)
print (np.mean(res>0),file=f)
def test_ubm_var_channel():
ubm = GMM.load('model/ubm.mixture-32.person-20.immature.model')
train_duration = 8.
nr_test = 5
test_duration = 3.
audio_files = ['xinyu.vad.wav', 'wyx.wav']
X_train, y_train, X_test, y_test = [], [], [], []
for audio_file in audio_files:
fs, signal = wavfile.read(audio_file)
signal = monotize_signal(signal)
train_len = int(fs * train_duration)
test_len = int(fs * test_duration)
X_train.append(mix_feature((fs, signal[:train_len])))
y_train.append(audio_file)
for i in range(nr_test):
start = random.randint(train_len, len(signal) - test_len)
X_test.append(mix_feature((fs, signal[start:start+train_len])))
y_test.append(audio_file)
gmmset = GMMSet(32, ubm=ubm)
gmmset.fit(X_train, y_train)
y_pred = gmmset.predict_with_reject(X_test)
for i in xrange(len(y_pred)):
print y_test[i], y_pred[i], '' if y_test[i] == y_pred[i] else 'wrong'
for imposter_audio_file in map(
lambda x: 'test-{}.wav'.format(x), range(5)):
fs, signal = wavfile.read(imposter_audio_file)
signal = monotize_signal(signal)
imposter_x = mix_feature((fs, signal))
print gmmset.predict_one_with_rejection(imposter_x)
def find_offset(file1, file2, fs=8000, trim=60*15, correl_nframes=1000):
tmp1 = convert_and_trim(file1, fs, trim)
tmp2 = convert_and_trim(file2, fs, trim)
# Removing warnings because of 18 bits block size
# outputted by ffmpeg
# https://trac.ffmpeg.org/ticket/1843
warnings.simplefilter("ignore", wavfile.WavFileWarning)
a1 = wavfile.read(tmp1, mmap=True)[1] / (2.0 ** 15)
a2 = wavfile.read(tmp2, mmap=True)[1] / (2.0 ** 15)
# We truncate zeroes off the beginning of each signals
# (only seems to happen in ffmpeg, not in sox)
a1 = ensure_non_zero(a1)
a2 = ensure_non_zero(a2)
mfcc1 = mfcc(a1, nwin=256, nfft=512, fs=fs, nceps=13)[0]
mfcc2 = mfcc(a2, nwin=256, nfft=512, fs=fs, nceps=13)[0]
mfcc1 = std_mfcc(mfcc1)
mfcc2 = std_mfcc(mfcc2)
c = cross_correlation(mfcc1, mfcc2, nframes=correl_nframes)
max_k_index = np.argmax(c)
# The MFCC window overlap is hardcoded in scikits.talkbox
offset = max_k_index * 160.0 / float(fs) # * over / sample rate
score = (c[max_k_index] - np.mean(c)) / np.std(c) # standard score of peak
os.remove(tmp1)
os.remove(tmp2)
return offset, score
def load_data(syllable, N, used_samples, snr, sample_order = None):
"""Function that goes through all N samples of syllable and loads its wave data.
:param syllable: complete path name of syllable (string)
:param N: number of samples to load
:param used_samples: number of samples to skip in the beginning
:param snr: the strength of the noise
:param sample_order: if not None should be vector of indices of samples to be loaded (default = None)
:returns syllable_waves: list of N sample waves of syllable
"""
samples = [files for files in os.listdir(syllable)]
syllable_waves = []
if sample_order is None:
for i in range(int(N)):
rate, wave = wav.read(syllable + '/' + samples[i + used_samples])
if (snr != 0.0):
noiseLvl = np.sqrt(np.var(wave) / snr)
else:
noiseLvl = 0.0
wave = wave + noiseLvl * np.random.randn(len(wave))
syllable_waves.append([wave,rate])
else:
for i in sample_order:
rate, wave = wav.read(syllable + '/' + samples[i])
if(snr != 0.0):
noiseLvl = np.sqrt(np.var(wave) / snr)
else:
noiseLvl = 0.0
wave = wave + noiseLvl * np.random.randn(len(wave))
syllable_waves.append([wave,rate])
return syllable_waves
def estim_diff(percent=256):
sound_counter=0
res=np.empty(len(input_file_names))
for i in range(res.shape[0]):
input_rate,input_sig=wavfile.read(input_dir+'Segments/'+input_file_names[i])
output_rate,output_sig=wavfile.read(output_dir+'Segments/'+output_file_names[i])
input_sig=pcm2float(input_sig,'float32')
output_sig=pcm2float(output_sig,'float32')
min_size=np.min((input_sig[:,0].shape[0],output_sig[:,0].shape[0]))
#print min_size,min_size*percent
#S_inp=np.absolute(fft(input_sig[:min_size,0]-np.mean(input_sig[:min_size,0])))
#S_out=np.absolute(fft(output_sig[:min_size,0]-np.mean(output_sig[:min_size,0])))
t=time()
nperseg=int(min_size*percent)-np.mod(int(min_size*percent),10)
real_perc=float(float(nperseg)/int(min_size*percent))
S_inp=signal.welch(input_sig[:min_size,0],nperseg=nperseg)[1]
S_out=signal.welch(output_sig[:min_size,0],nperseg=nperseg)[1]
#S_inp=ndim_welch(input_sig[:min_size,0][None,...],nperseg=int(min_size*percent))[1]
#S_out=ndim_welch(output_sig[:min_size,0][None,...],nperseg=int(min_size*percent))[1]
#print time()-t
#print S_inp_1,S_inp_2
res[sound_counter]=delta_estimator_3(S_out/S_inp,S_inp)-delta_estimator_3(S_inp/S_out,S_out)
#out=float2pcm(output_sig,'int16')
sound_counter+=1
return real_perc,int(min_size*percent),res
def generate_mixture(src1, src2, fname, attn1, attn2):
"""
mixes 10 seconds of two sources of the same sample rate and saves them as fname
Args:
src1: filename for the first source
src2: filename for the second source
fname: output filename to save as
attn1: relative attenuation for the first source
attn2: relative attenuation for the second source
Returns:
"""
sr1, data1 = wav.read(src1)
if data1.dtype == np.dtype("int16"):
data1 = data1 / float(np.iinfo(data1.dtype).max)
sr2, data2 = wav.read(src2)
if data2.dtype == np.dtype("int16"):
data2 = data2 / float(np.iinfo(data2.dtype).max)
if sr1 != sr2:
raise ValueError("Both sources muse have same sample rate")
attn1 = float(attn1 + 1) / 2
attn2 = float(attn2 + 1) / 2
sample1 = data1[0:10 * sr1]
sample2 = data2[0:10 * sr1]
left = attenuate(sample1, attn1) + attenuate(sample2, attn2)
right = attenuate(sample1, 1-attn1) + attenuate(sample2, 1-attn2)
signal = np.vstack((left, right))
scipy.io.wavfile.write(fname, sr1, signal.T)
def simple_noise_filter(target, files, method=median_by_intensity, combination=flatten, section_length=4096):
# load all .mp3 files into an arrays
# bin each to a certain length
#print time()
feeds = [section_by_length(wavfile.read(file)[1], section_length) for file in files]
samplerate = wavfile.read(files[0])[0]
#print time()
# perform fft on each bin, select median of each
max_len = len(max(feeds, key=len))
sections = []
for i in range(max_len):
begin = time()
freqs = [fft.fft(feed[i], axis=0) for feed in feeds]
#print "Fourier per ~.1s feed: ",
#print (time()-begin)/3.
begin = time()
#filtered_freqs = [median_by_intensity(freqs, j) for j in range(len(freqs[0]))] # traverse the arrays in parallel
filtered_freqs = [method(freqs, j) for j in range(len(freqs[0]))]
#print "Filtering: ",
#print (time()-begin)
begin = time()
sections += [real(fft.ifft(filtered_freqs, axis=0)).astype(feeds[0][0].dtype)]
#print "Inversing per ~.1s feed: ",
#print (time() - begin)
# output
#print time()
samples = combination(sections)
wavfile.write(target, samplerate, samples)
def generate_reverb(signal, reverb, fname, iter_range):
"""
Adds reverb from the path reverb to the data in the path signal and saves it as fname. Applies reverb iteratively over
iter_range
:param signal: the filename for the stereo input signal
:param reverb: the filename for the stereo impulse response
:param fname: the output filename to save as
:param iter_range: the max number of iterations to convolve with the signal
:return:
"""
sr, data = wav.read(signal)
if data.dtype == np.dtype("int16"):
data = data / float(np.iinfo(data.dtype).max)
sr_ir, data_ir = wav.read(reverb)
if data_ir.dtype == np.dtype("int16"):
data_ir = data_ir / float(np.iinfo(data_ir.dtype).max)
if sr_ir != sr:
raise ValueError("Impulse Response must have same sample rate as signal")
prev_data = data
for i in xrange(0, iter_range+1):
if i > 0:
mix = add_reverb(prev_data.T, data_ir.T)
prev_data = np.copy(mix).T
else:
mix = data.T
if not os.path.exists(os.path.splitext(fname)[0]+'-'+str(i)+'.wav'):
scipy.io.wavfile.write(os.path.splitext(fname)[0]+'-'+str(i)+'.wav', sr, mix.T)
def mix_files(f1,f2):
base1 = f1.split('/')[-1].split('.wav')[0]
base2 = f2.split('/')[-1].split('.wav')[0]
(fs,sig) = wav.read(f1)
s1 = sig.reshape((len(sig),1))
del sig
(fs,sig) = wav.read(f2)
s2 = sig.reshape((len(sig),1))
del sig
block_length = 5*fs
s1_blocks = enframe(s1,block_length,block_length)
s2_blocks = enframe(s2,block_length,block_length)
del s1, s2
nrg1 = 0.707*np.sqrt(np.sum(np.power(s1_blocks,2),axis=1))
nrg2 = 0.707*np.sqrt(np.sum(np.power(s2_blocks,2),axis=1))
for i in range(len(nrg1)):
db1 = np.log(nrg1[i])
db2 = np.log(nrg2[i])
if (db1 >= 9) and (db2 >= 9) and (0.1 < abs(db1 - db2) < 5):
sir = '%.2f' % (db1 - db2)
ovl_name = '/erasable/nxs113020/wav_ovl/'+base1+'_'+base2+'_sir'+sir+'_'+str(i)+'.wav'
overlapped = s1_blocks[i,:] + s2_blocks[i,:]
nrg_ovl = 0.707*np.sqrt(np.sum(np.power(overlapped,2)))
scikits.audiolab.wavwrite(overlapped/nrg_ovl, ovl_name, fs, 'pcm16')
def generate(self, fileList, inputParam1, inputParam2, inputParam3, inputParam4):
for path in fileList: # add Music objects to pl (playlist). Pass in filename, full L/R data, and mean-ed data
self.pl.append(Music(path.split("/")[-1], wav.read(path)[1], wav.read(path)[1].mean(axis=1)))
print(self.pl[1].title)
print(self.pl[1].data)
print(self.pl[1].avgData)
print(inputParam1, inputParam2, inputParam3, inputParam4)
def wait_for_wav(filename):
# Super ugly hack! Since Csound might not be finished writing to the file, we try to read it, and upon fail (i.e. it was not closed) we wait .05 seconds.
while True:
try:
wavfile.read(filename)
break
except:
time.sleep(.05)
return filename
def main():
fs, bg_signal = wavfile.read(sys.argv[1])
ltsd = LTSD_VAD()
ltsd.init_params_by_noise(fs, bg_signal)
fs, signal = wavfile.read(sys.argv[2])
vaded_signal = ltsd.filter(signal)
wavfile.write('vaded.wav', fs, vaded_signal)
def training(nfiltbank, orderLPC):
nSpeaker = 8
nCentroid = 16
codebooks_mfcc = np.empty((nSpeaker,nfiltbank,nCentroid))
codebooks_lpc = np.empty((nSpeaker, orderLPC, nCentroid))
directory = os.getcwd() + '/train';
fname = str()
for i in range(nSpeaker):
fname = '/s' + str(i+1) + '.wav'
print('Now speaker ', str(i+1), 'features are being trained' )
(fs,s) = read(directory + fname)
mel_coeff = mfcc(s, fs, nfiltbank)
lpc_coeff = lpc(s, fs, orderLPC)
codebooks_mfcc[i,:,:] = lbg(mel_coeff, nCentroid)
codebooks_lpc[i,:,:] = lbg(lpc_coeff, nCentroid)
plt.figure(i)
plt.title('Codebook for speaker ' + str(i+1) + ' with ' + str(nCentroid) + ' centroids')
for j in range(nCentroid):
plt.subplot(211)
plt.stem(codebooks_mfcc[i,:,j])
plt.ylabel('MFCC')
plt.subplot(212)
markerline, stemlines, baseline = plt.stem(codebooks_lpc[i,:,j])
plt.setp(markerline,'markerfacecolor','r')
plt.setp(baseline,'color', 'k')
plt.ylabel('LPC')
plt.axis(ymin = -1, ymax = 1)
plt.xlabel('Number of features')
plt.show()
print('Training complete')
#plotting 5th and 6th dimension MFCC features on a 2D plane
#comment lines 54 to 71 if you don't want to see codebook
codebooks = np.empty((2, nfiltbank, nCentroid))
mel_coeff = np.empty((2, nfiltbank, 68))
for i in range(2):
fname = '/s' + str(i+2) + '.wav'
(fs,s) = read(directory + fname)
mel_coeff[i,:,:] = mfcc(s, fs, nfiltbank)[:,0:68]
codebooks[i,:,:] = lbg(mel_coeff[i,:,:], nCentroid)
plt.figure(nSpeaker + 1)
s1 = plt.scatter(mel_coeff[0,6,:], mel_coeff[0,4,:],s = 100, color = 'r', marker = 'o')
c1 = plt.scatter(codebooks[0,6,:], codebooks[0,4,:], s = 100, color = 'r', marker = '+')
s2 = plt.scatter(mel_coeff[1,6,:], mel_coeff[1,4,:],s = 100, color = 'b', marker = 'o')
c2 = plt.scatter(codebooks[1,6,:], codebooks[1,4,:], s = 100, color = 'b', marker = '+')
plt.grid()
plt.legend((s1, s2, c1, c2), ('Sp1','Sp2','Sp1 centroids', 'Sp2 centroids'), scatterpoints = 1, loc = 'upper left')
plt.show()
return (codebooks_mfcc, codebooks_lpc)
def run():
print "Creating file voiceCepstrums.arff..."
files = []
output = open("voiceCepstrums.arff", 'w')
for x in os.walk("AudioRecordings"):
files.append(x)
females = files[1][2]
males = files[2][2]
output.write("@relation voiceCepstrums\n\n")
output.write("@attribute coefficient1 Continuous\n")
output.write("@attribute coefficient2 Continuous\n")
output.write("@attribute coefficient3 Continuous\n")
output.write("@attribute coefficient4 Continuous\n")
output.write("@attribute coefficient5 Continuous\n")
output.write("@attribute gender {male, female}\n\n")
output.write("@data\n")
for filename in males:
if filename.endswith(".wav") or filename.endswith(".WAV"):
sampFreq, data = wavfile.read('AudioRecordings/Male/' + filename)
cepstrum = getCepstrum(data)
frequencies = {}
for i in range(0, len(data)):
coefficient = data[i]
if coefficient[0] != float('Inf') and coefficient[0] != 0.0:
frequency = (i*sampFreq)/len(data)
frequencies[frequency] = coefficient[0];
sortedFrequencies = sorted(frequencies.items(), key=operator.itemgetter(1), reverse=True);
sortedFrequencies = sortedFrequencies
for i in range(0, 5):
output.write(str(sortedFrequencies[i][0]))
output.write(", ")
output.write("male\n")
for filename in females:
if filename.endswith(".wav") or filename.endswith(".WAV"):
sampFreq, data = wavfile.read('AudioRecordings/Female/' + filename)
# time =float(len(data))/float(sampFreq)
# frequency =
cepstrum = getCepstrum(data)
frequencies = {}
for i in range(0, len(data)):
coefficient = data[i]
if coefficient[0] != float('Inf') and coefficient[0] != 0.0:
frequency = (i*44100)/len(data)
frequencies[frequency] = coefficient[0];
sortedFrequencies = sorted(frequencies.items(), key=operator.itemgetter(1), reverse=True);
sortedFrequencies = sortedFrequencies
for i in range(0, 5):
output.write(str(sortedFrequencies[i][0]))
output.write(", ")
output.write("female\n")
from features import ssc
import scipy.io.wavfile as wav
file_name = "splateroyyo.wav"
(rate, sig) = wav.read(file_name) #or whatever the filename is
f = ssc(rate, sig)
print f.shape
fd = open("x_train.npy", "a+b")
np.save(fd, f)
fd.close()
if file_name[0] == 's':
y_train = np.ones(f.shape[0], 1)
elif file_name[0] == 'e':
y_train = np.zeros(f.shape[0], 1)
fdd = open("y_train.npy", "a+b")
np.save(fdd, y_train)
fdd.close()
def load_metadata_from_wavs():
global background_noise
background = [
f for f in os.listdir(join(TRAIN_AUDIO_PATH, '_background_noise_'))
if f.endswith('.wav')
]
for wav in background:
samples, sample_rate = librosa.load(join(
join(TRAIN_AUDIO_PATH, '_background_noise_'), wav),
sr=INPUT_SAMPLES)
background_noise.append(samples)
dirs = [
f for f in os.listdir(TRAIN_AUDIO_PATH)
if isdir(join(TRAIN_AUDIO_PATH, f))
]
dirs.sort()
wavs = []
labels = []
unknown_wavs = []
unknown_list = [
d for d in dirs if d not in TARGET_LIST and d != '_background_noise_'
]
print('target_list : ', end='')
print(TARGET_LIST)
print('unknowns_list : ', end='')
print(unknown_list)
print('silence : _background_noise_')
i = 0
for directory in dirs[1:]:
waves = [
f for f in os.listdir(join(TRAIN_AUDIO_PATH, directory))
if f.endswith('.wav')
]
for j, wav in enumerate(waves):
# samples, sample_rate = librosa.load(join(join(TRAIN_AUDIO_PATH, directory), wav), sr=16000)
sample_rate, samples = wavfile.read(
join(join(TRAIN_AUDIO_PATH, directory), wav))
samples = np.concatenate((np.zeros((INPUT_SAMPLES - 8000) // 2,
dtype="float32"), samples[::2],
np.zeros((INPUT_SAMPLES - 8000) // 2,
dtype="float32")))
if len(samples) != INPUT_SAMPLES:
continue
if directory in unknown_list:
unknown_wavs.append((wav, directory))
else:
wavs.append((TYPE_REGULAR, wav))
labels.append(directory)
wavc = len(wavs)
for n in range(NOISE_MULTIPLIER):
for i in range(wavc):
wavs.append((TYPE_NOISED, wavs[i][1]))
labels.append(labels[i])
for i in range(UNKNOWN_COUNT):
wavs.append((TYPE_UNKNOWN, random.choice(unknown_wavs)))
labels.append("unknown")
for i in range(SILENCE_COUNT):
wavs.append((TYPE_SILENCE, random.randrange(0, len(background_noise))))
labels.append("silence")
return wavs, labels
room_dim = [8, 9]
# source location
source = np.array([1, 4.5])
# create an anechoic room with sources and mics
room = pra.ShoeBox(room_dim,
fs=16000,
max_order=15,
absorption=0.35,
sigma2_awgn=1e-8)
# get signals
signals = [
np.concatenate(
[wavfile.read(f)[1].astype(np.float32) for f in source_files])
for source_files in wav_files
]
delays = [1., 0.]
locations = [[2.5, 3], [2.5, 6]]
# add mic and good source to room
# Add silent signals to all sources
for sig, d, loc in zip(signals, delays, locations):
room.add_source(loc, signal=np.zeros_like(sig), delay=d)
# add microphone array
room.add_microphone_array(
pra.MicrophoneArray(np.c_[[6.5, 4.49], [6.5, 4.51]], fs=room.fs))
# compute RIRs
def audiofile_to_input_vector(audio_filename, numcep, numcontext):
# Load wav files
fs, audio = wav.read(audio_filename)
# Get mfcc coefficients
orig_inputs = mfcc(audio, samplerate=fs, numcep=numcep)
# For each time slice of the training set, we need to copy the context this makes
# the numcep dimensions vector into a numcep + 2*numcep*numcontext dimensions
# because of:
# - numcep dimensions for the current mfcc feature set
# - numcontext*numcep dimensions for each of the past and future (x2) mfcc feature set
# => so numcep + 2*numcontext*numcep
train_inputs = np.array([], np.float32)
train_inputs.resize((orig_inputs.shape[0], numcep + 2*numcep*numcontext))
# Prepare pre-fix post fix context (TODO: Fill empty_mfcc with MCFF of silence)
empty_mfcc = np.array([])
empty_mfcc.resize((numcep))
# Prepare train_inputs with past and future contexts
time_slices = range(train_inputs.shape[0])
context_past_min = time_slices[0] + numcontext
context_future_max = time_slices[-1] - numcontext
for time_slice in time_slices:
### Reminder: array[start:stop:step]
### slices from indice |start| up to |stop| (not included), every |step|
# Pick up to numcontext time slices in the past, and complete with empty
# mfcc features
need_empty_past = max(0, (context_past_min - time_slice))
empty_source_past = list(empty_mfcc for empty_slots in range(need_empty_past))
data_source_past = orig_inputs[max(0, time_slice - numcontext):time_slice]
assert(len(empty_source_past) + len(data_source_past) == numcontext)
# Pick up to numcontext time slices in the future, and complete with empty
# mfcc features
need_empty_future = max(0, (time_slice - context_future_max))
empty_source_future = list(empty_mfcc for empty_slots in range(need_empty_future))
data_source_future = orig_inputs[time_slice + 1:time_slice + numcontext + 1]
assert(len(empty_source_future) + len(data_source_future) == numcontext)
if need_empty_past:
past = np.concatenate((empty_source_past, data_source_past))
else:
past = data_source_past
if need_empty_future:
future = np.concatenate((data_source_future, empty_source_future))
else:
future = data_source_future
past = np.reshape(past, numcontext*numcep)
now = orig_inputs[time_slice]
future = np.reshape(future, numcontext*numcep)
train_inputs[time_slice] = np.concatenate((past, now, future))
assert(len(train_inputs[time_slice]) == numcep + 2*numcep*numcontext)
# Whiten inputs (TODO: Should we whiten)
train_inputs = (train_inputs - np.mean(train_inputs))/np.std(train_inputs)
# Return results
return train_inputs
def mainfn():
#Read the file
wavfil = raw_input("Wav file to be read: ")
f = wavfile.read(
'/home/akshay/anaconda/ModulesPython/MUSICGEVD/{}'.format(wavfil))
rdata = f[1]
nc = rdata.shape[1]
print "Number of channels: " + str(nc)
fs = f[0]
print "Sampling frequency: " + str(fs)
(Pxx, freqs, bins, im) = plt.specgram(rdata[:, 0],
NFFT=512,
Fs=fs,
noverlap=160)
plt.title('Spectrogram')
plt.xlabel('Time')
plt.ylabel('Frequency in Hz')
plt.show()
tf, az, Nd = readingdat.mainfn()
#print tf
#print str(tf.shape)
# INPUTS:
N = 512 # Block Size & N point FFT
M = 160 # Block Increment
WINDOW = 50 # Time averaging of the CM, WINDOW_TYPE is FUTURE
## Plot the audio signal - Channel 1
##xaxis = np.arange(len(rdata))
##plt.plot(xaxis,rdata[:,0])
##plt.title('Channel 1: First microphone')
#plt.show()
# Divide into frames with overlap - Use blockd
## Assumption: rdata has channels on columns, and samples on rows
rdata = np.transpose(
rdata) #Comment out if samples on col. and channels on rows
num_blocks = nblocks(rdata[0, :], N, M)
blockeddata = np.zeros((nc, num_blocks, N)) # blockeddata is a 3D matrix
for i in range(nc):
blockeddata[i, :, :] = blockd(rdata[i, :], N, M)
print "Shape of blocked data: " + str(blockeddata.shape)
#EACH FRAME COMPUTATION:
#Each frame has to be taken for FFT
## fftmat = mfft(blockeddata[:,0,:])
fnum = 0 #fnum is the frame index
#The correlation matrix is calculated once every PERIOD number of frames.
# Time averaging is done with WINDOW_TYPE = FUTURE
#print "Calculating the time-averaged correlation matrices"
N_avg = noisecormat.mainfn()
for t in range(int(math.ceil(num_blocks / WINDOW))):
#fnum is Frame index
i = 0
R_tot = np.zeros((nc, nc, N / 2 + 1), dtype=complex)
print "Localization for frames: ", str(
t * 50), " to ", str((t + 1) * 50 - 1)
while i < WINDOW:
if fnum >= num_blocks:
break
else:
fftmat = mfft(blockeddata[:, fnum, :])
#print fftmat
#print "Shape of fftmat: " + str(fftmat.shape)
R_tot = R_tot + Rmatrix(fftmat)
fnum = fnum + 1
i = i + 1
## print i
## print fnum
R_avg = R_tot / (i)
print "R_avg matrix for freq bin 20: "
print R_avg[:, :, 20]
## print "Shape of R_avg is: " + str(R_avg.shape)
print "N_avg shape: ", str(N_avg.shape)
for fy in range(N / 2 + 1):
N_avg2 = sl.inv(np.matrix(N_avg[:, :, fy]))
N_avg2 = sl.sqrtm(N_avg2)
R_avg[:, :, fy] = N_avg2 * R_avg[:, :, fy]
R_avg[:, :, fy] = R_avg[:, :, fy] * N_avg2
#Eigen value decomposition is done for each frequency
w = np.zeros((nc, N / 2 + 1), dtype=complex)
v = np.zeros((nc, nc, N / 2 + 1), dtype=complex)
for fy in range(N / 2 + 1):
w[:, fy], v[:, :, fy] = np.linalg.eig(R_avg[:, :, fy])
#print "Shape of w: " + str(w.shape)
#print "Shape of v: " + str(v.shape)
print "values: ", w[:,
5] #printing the 3rd freq bin's eigen values and eigen vectors
#print w[:,2].shape
print "vectors: ", v[:, :, 5]
## print "v shape: ", str(v[:,1,2].shape)
## Calculation of MUSIC spectrum
print "Calculating MUSIC spectrum"
powerspec.mainfn(tf, v, t, az, Nd)
def load_model():
# learning loop
model = FunctionSet(l1=F.Linear(2 * (dim * 2 + 1),
n_units,
initialW=initializer),
l2=F.Linear(n_units, n_units,
initialW=initializer),
l3=F.Linear(n_units, 1, initialW=initializer))
# Setup optimizer
optimizer = optimizers.Adam()
optimizer.setup(model)
model.to_gpu()
# Neural net architecture
def forward(x_data, y_data, ratio=0.5, train=True):
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.sigmoid(model.l1(x)), ratio=ratio, train=train)
h2 = F.dropout(F.sigmoid(model.l2(h1)), ratio=ratio, train=train)
y = model.l3(h2)
return F.mean_squared_error(y, t), y
with open('selectfrq_estimated_data/pretrain_write1.csv',
'r') as csvfile:
readfile = csv.reader(csvfile)
for row in readfile:
print len(row)
# print row
l1_W.append(row)
with open('selectfrq_estimated_data/pretrain_write2.csv',
"r") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
# print row
l2_W.append(row)
with open('selectfrq_estimated_data/pretrain_write3.csv',
"r") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
# print row
l3_W.append(row)
#test loop
SNRList = ["-10dB", "-5dB", "0dB", "5dB", "10dB", "-20dB"]
for SNRnum, SNR in enumerate(SNRList): #-10,-5,0,5,10,-20dB
loss_sum = np.zeros(testsize)
for idx in np.arange(learnsize + SNRnum * testsize,
learnsize + (SNRnum + 1) * testsize):
fs, signal_data = read(estimated_signal[idx], "r")
fs, noise_data = read(estimated_noise[idx], "r")
fs, teacher_data = read(
teacher_signal[idx - testsize * SNRnum], "r")
signal_data = signal_data / np.sqrt(np.mean(signal_data**2))
noise_data = noise_data / np.sqrt(np.mean(noise_data**2))
teacher_data = teacher_data / np.sqrt(np.mean(teacher_data**2))
Sspectrum_, synparam = FFTanalysis.FFTanalysis(signal_data)
Nspectrum_, synparam = FFTanalysis.FFTanalysis(noise_data)
Tspectrum_, synparam = FFTanalysis.FFTanalysis(teacher_data)
N_FRAMES = np.shape(Sspectrum_)[0]
HFFTL = np.shape(Sspectrum_)[1]
x_data = np.zeros((N_FRAMES, HFFTL * 2))
y_data = np.zeros((N_FRAMES, HFFTL))
for nframe in xrange(N_FRAMES):
spectrum = np.append(Sspectrum_[nframe],
Nspectrum_[nframe])
x_data[nframe] = [
np.sqrt(c.real**2 + c.imag**2) for c in spectrum
] #DNN indata
#phaseSpectrum = [np.arctan2(c.imag, c.real) for c in spectrum]
Spower = np.array([
np.sqrt(c.real**2 + c.imag**2)
for c in Sspectrum_[nframe]
])
Tpower = np.array([
np.sqrt(c.real**2 + c.imag**2)
for c in Tspectrum_[nframe]
])
for i, x in enumerate(Spower):
if x == 0:
Spower[i] = 1e-10
y_data[nframe] = Tpower / Spower
calcSNR = np.empty((N_FRAMES, 0), float)
totalloss = np.zeros(HFFTL, float)
# testing
for frq in xrange(HFFTL):
model.l1.W.data = cuda.to_gpu(l1_W[frq])
model.l2.W.data = cuda.to_gpu(l2_W[frq])
model.l3.W.data = cuda.to_gpu(l3_W[frq])
# testing
x_frqdata = np.zeros(
(np.shape(x_data)[0], 2 * (dim * 2 + 1)), float)
x_frqdata[:, dim] = x_data[:, frq]
x_frqdata[:, dim * 3 + 1] = x_data[:, frq + HFFTL]
for j in np.arange(1, dim + 1):
if (frq - j) >= 0:
x_frqdata[:, dim - j] = x_data[:, frq - j]
x_frqdata[:, dim * 3 + 1 - j] = x_data[:, frq +
HFFTL - j]
if ((HFFTL - 1) - (j + frq)) >= 0:
x_frqdata[:, dim + j] = x_data[:, frq + j]
x_frqdata[:, dim * 3 + 1 + j] = x_data[:, frq +
HFFTL + j]
y_frqdata = np.zeros((np.shape(y_data)[0], 1), float)
y_frqdata = y_data[:, frq].reshape(np.shape(y_data)[0], 1)
x_frqdata = x_frqdata.astype(np.float32)
y_frqdata = y_frqdata.astype(np.float32)
if args.gpu >= 0:
x_frqdata = cuda.to_gpu(x_frqdata)
y_frqdata = cuda.to_gpu(y_frqdata)
loss, pred = forward(x_frqdata, y_frqdata, train=False)
totalloss[frq] = cuda.to_cpu(loss.data)
pred = np.reshape(cuda.to_cpu(pred.data), (N_FRAMES, 1))
calcSNR = np.append(calcSNR, pred, axis=1)
fs, teacher_data = read(
teacher_signal[idx - testsize * SNRnum], "r")
if teacher_data.dtype == "int16":
teacher_data = teacher_data / norm
y_out = Sspectrum_ * calcSNR
wf_signal = FFTanalysis.Synth(y_out, synparam, BPFon=0)
wf_signal = wf_signal * np.sqrt(
np.mean(teacher_data**2) / np.mean(wf_signal**2))
write(
dir + SNR + "/dim{}_DNNbased_No{}.wav".format(
dim, idx - testsize * SNRnum), Fs, wf_signal)
def DNNbasedWienerfilter():
#pretrain loop
startexec = time.time()
for epoch in xrange(pretrain_epoch):
print "now proc: pretraining epoch{}".format(epoch)
startepoch = time.time()
perm = np.random.permutation(learnsize)
for idx in np.arange(0, pretrainsize,
3): #utterance Number training dataset
# start = time.time()
x_batch = np.empty((0, HFFTL * 2), float)
for iter in xrange(3):
fs, signal_data = read(estimated_signal[perm[idx + iter]],
"r")
fs, noise_data = read(estimated_noise[perm[idx + iter]],
"r")
signal_data = signal_data / np.sqrt(np.mean(signal_data**
2))
noise_data = noise_data / np.sqrt(np.mean(noise_data**2))
#FFT
Sspectrum_, synparam = FFTanalysis.FFTanalysis(signal_data)
Nspectrum_, synparam = FFTanalysis.FFTanalysis(noise_data)
N_FRAMES = np.shape(Sspectrum_)[0]
HFFTL = np.shape(Sspectrum_)[1]
x_data = np.zeros((N_FRAMES, HFFTL * 2))
for nframe in xrange(N_FRAMES):
spectrum = np.append(Sspectrum_[nframe],
Nspectrum_[nframe])
x_data[nframe] = [
np.sqrt(c.real**2 + c.imag**2) for c in spectrum
] #DNN indata
if iter == 0:
x_batch = np.append(x_batch, x_data, axis=0)
else:
x_batch = np.vstack((x_batch, x_data))
for frq in xrange(HFFTL):
x_frqbatch = np.zeros(
(np.shape(x_batch)[0], 2 * (dim * 2 + 1)), float)
x_frqbatch[:, dim] = x_batch[:, frq]
x_frqbatch[:, dim * 3 + 1] = x_batch[:, frq + HFFTL]
for j in np.arange(1, dim + 1):
if (frq - j) >= 0:
x_frqbatch[:, dim - j] = x_batch[:, frq - j]
x_frqbatch[:, dim * 3 + 1 - j] = x_batch[:, frq +
HFFTL - j]
if ((HFFTL - 1) - (j + frq)) >= 0:
x_frqbatch[:, dim + j] = x_batch[:, frq + j]
x_frqbatch[:, dim * 3 + 1 + j] = x_batch[:, frq +
HFFTL + j]
x_frqbatch = x_frqbatch.astype(np.float32)
if epoch != 0 or idx != 0: #except first batch
modelL1.l1.W.data = cuda.to_gpu(l1_W.pop(0))
modelL2.l1.W.data = cuda.to_gpu(l2_W.pop(0))
modelL1.l2.W.data = cuda.to_gpu(l1b_W.pop(0))
modelL2.l2.W.data = cuda.to_gpu(l2b_W.pop(0))
# training
if args.gpu >= 0:
x_frqbatch = cuda.to_gpu(x_frqbatch)
optL1.zero_grads()
loss, hidden = pretrain_L1(x_frqbatch, ratio=0.5)
loss.backward()
optL1.update()
optL2.zero_grads()
loss, hidden = pretrain_L2(hidden, ratio=0.5)
loss.backward()
optL2.update()
#model parameter saving
l1_W.append(cuda.to_cpu(modelL1.l1.W.data))
l2_W.append(cuda.to_cpu(modelL2.l1.W.data))
l1b_W.append(cuda.to_cpu(modelL1.l2.W.data))
l2b_W.append(cuda.to_cpu(modelL2.l2.W.data))
print 'pretrain epoch time:{0}sec'.format(
np.round(time.time() - startepoch, decimals=2))
# learning loop
model = FunctionSet(l1=F.Linear(2 * (dim * 2 + 1),
n_units,
initialW=initializer),
l2=F.Linear(n_units, n_units,
initialW=initializer),
l3=F.Linear(n_units, 1, initialW=initializer))
# Setup optimizer
optimizer = optimizers.Adam()
optimizer.setup(model)
model.to_gpu()
# Neural net architecture
def forward(x_data, y_data, ratio=0.5, train=True):
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.sigmoid(model.l1(x)), ratio=ratio, train=train)
h2 = F.dropout(F.sigmoid(model.l2(h1)), ratio=ratio, train=train)
y = model.l3(h2)
return F.mean_squared_error(y, t), y
startexec = time.time()
for epoch in xrange(n_epoch):
print "now proc: learning epoch{}".format(epoch)
startepoch = time.time()
perm = np.random.permutation(learnsize)
for idx in np.arange(0, learnsize,
3): #utterance Number training dataset
# start = time.time()
x_batch = np.empty((0, HFFTL * 2), float)
y_batch = np.empty((0, HFFTL), float)
for iter in xrange(3):
fs, signal_data = read(estimated_signal[perm[idx + iter]],
"r")
fs, noise_data = read(estimated_noise[perm[idx + iter]],
"r")
fs, teacher_data = read(teacher_signal[perm[idx + iter]],
"r")
signal_data = signal_data / np.sqrt(np.mean(signal_data**
2))
noise_data = noise_data / np.sqrt(np.mean(noise_data**2))
teacher_data = teacher_data / np.sqrt(
np.mean(teacher_data**2))
#FFT
Sspectrum_, synparam = FFTanalysis.FFTanalysis(signal_data)
Nspectrum_, synparam = FFTanalysis.FFTanalysis(noise_data)
Tspectrum_, synparam = FFTanalysis.FFTanalysis(
teacher_data)
N_FRAMES = np.shape(Sspectrum_)[0]
HFFTL = np.shape(Sspectrum_)[1]
x_data = np.zeros((N_FRAMES, HFFTL * 2))
y_data = np.zeros((N_FRAMES, HFFTL))
if epoch == 0:
learned_data += N_FRAMES
for nframe in xrange(N_FRAMES):
spectrum = np.append(Sspectrum_[nframe],
Nspectrum_[nframe])
x_data[nframe] = [
np.sqrt(c.real**2 + c.imag**2) for c in spectrum
] #DNN indata
#phaseSpectrum = [np.arctan2(c.imag, c.real) for c in spectrum]
Spower = np.array([
np.sqrt(c.real**2 + c.imag**2)
for c in Sspectrum_[nframe]
])
Tpower = np.array([
np.sqrt(c.real**2 + c.imag**2)
for c in Tspectrum_[nframe]
])
for i, x in enumerate(Spower):
if x == 0:
Spower[i] = 1e-10
y_data[nframe] = Tpower / Spower
if iter == 0:
x_batch = np.append(x_batch, x_data, axis=0)
y_batch = np.append(y_batch, y_data, axis=0)
else:
x_batch = np.vstack((x_batch, x_data))
y_batch = np.vstack((y_batch, y_data))
for frq in xrange(HFFTL):
x_frqbatch = np.zeros(
(np.shape(x_batch)[0], 2 * (dim * 2 + 1)), float)
x_frqbatch[:, dim] = x_batch[:, frq]
x_frqbatch[:, dim * 3 + 1] = x_batch[:, frq + HFFTL]
for j in np.arange(1, dim + 1):
if (frq - j) >= 0:
x_frqbatch[:, dim - j] = x_batch[:, frq - j]
x_frqbatch[:, dim * 3 + 1 - j] = x_batch[:, frq +
HFFTL - j]
if ((HFFTL - 1) - (j + frq)) >= 0:
x_frqbatch[:, dim + j] = x_batch[:, frq + j]
x_frqbatch[:, dim * 3 + 1 + j] = x_batch[:, frq +
HFFTL + j]
y_frqbatch = np.zeros((np.shape(y_batch)[0], 1), float)
y_frqbatch = y_batch[:,
frq].reshape(np.shape(y_batch)[0], 1)
x_frqbatch = x_frqbatch.astype(np.float32)
y_frqbatch = y_frqbatch.astype(np.float32)
model.l1.W.data = cuda.to_gpu(l1_W.pop(0))
model.l2.W.data = cuda.to_gpu(l2_W.pop(0))
if epoch != 0 or idx != 0: #except first batch
model.l3.W.data = cuda.to_gpu(l3_W.pop(0))
# training
if args.gpu >= 0:
x_frqbatch = cuda.to_gpu(x_frqbatch)
y_frqbatch = cuda.to_gpu(y_frqbatch)
optimizer.zero_grads()
loss, pred = forward(x_frqbatch, y_frqbatch, ratio=0.5)
loss.backward()
optimizer.update()
#model parameter saving
l1_W.append(cuda.to_cpu(model.l1.W.data))
l2_W.append(cuda.to_cpu(model.l2.W.data))
l3_W.append(cuda.to_cpu(model.l3.W.data))
print 'epoch time:{0}sec'.format(
np.round(time.time() - startepoch, decimals=2))
f = open('selectfrq_estimated_data/pretrain_write1.csv', 'w')
writer = csv.writer(f)
writer.writerows(l1_W)
f.close()
f = open('selectfrq_estimated_data/pretrain_write2.csv', 'w')
writer = csv.writer(f)
writer.writerows(l2_W)
f.close()
f = open('selectfrq_estimated_data/pretrain_write3.csv', 'w')
writer = csv.writer(f)
writer.writerows(l3_W)
f.close()
#test loop
SNRList = ["-10dB", "-5dB", "0dB", "5dB", "10dB", "-20dB"]
for SNRnum, SNR in enumerate(SNRList): #-10,-5,0,5,10,-20dB
loss_sum = np.zeros(testsize)
for idx in np.arange(learnsize + SNRnum * testsize,
learnsize + (SNRnum + 1) * testsize):
fs, signal_data = read(estimated_signal[idx], "r")
fs, noise_data = read(estimated_noise[idx], "r")
fs, teacher_data = read(
teacher_signal[idx - testsize * SNRnum], "r")
signal_data = signal_data / np.sqrt(np.mean(signal_data**2))
noise_data = noise_data / np.sqrt(np.mean(noise_data**2))
teacher_data = teacher_data / np.sqrt(np.mean(teacher_data**2))
Sspectrum_, synparam = FFTanalysis.FFTanalysis(signal_data)
Nspectrum_, synparam = FFTanalysis.FFTanalysis(noise_data)
Tspectrum_, synparam = FFTanalysis.FFTanalysis(teacher_data)
N_FRAMES = np.shape(Sspectrum_)[0]
HFFTL = np.shape(Sspectrum_)[1]
x_data = np.zeros((N_FRAMES, HFFTL * 2))
y_data = np.zeros((N_FRAMES, HFFTL))
for nframe in xrange(N_FRAMES):
spectrum = np.append(Sspectrum_[nframe],
Nspectrum_[nframe])
x_data[nframe] = [
np.sqrt(c.real**2 + c.imag**2) for c in spectrum
] #DNN indata
#phaseSpectrum = [np.arctan2(c.imag, c.real) for c in spectrum]
Spower = np.array([
np.sqrt(c.real**2 + c.imag**2)
for c in Sspectrum_[nframe]
])
Tpower = np.array([
np.sqrt(c.real**2 + c.imag**2)
for c in Tspectrum_[nframe]
])
for i, x in enumerate(Spower):
if x == 0:
Spower[i] = 1e-10
y_data[nframe] = Tpower / Spower
calcSNR = np.empty((N_FRAMES, 0), float)
totalloss = np.zeros(HFFTL, float)
# testing
for frq in xrange(HFFTL):
model.l1.W.data = cuda.to_gpu(l1_W[frq])
model.l2.W.data = cuda.to_gpu(l2_W[frq])
model.l3.W.data = cuda.to_gpu(l3_W[frq])
# testing
x_frqdata = np.zeros(
(np.shape(x_data)[0], 2 * (dim * 2 + 1)), float)
x_frqdata[:, dim] = x_data[:, frq]
x_frqdata[:, dim * 3 + 1] = x_data[:, frq + HFFTL]
for j in np.arange(1, dim + 1):
if (frq - j) >= 0:
x_frqdata[:, dim - j] = x_data[:, frq - j]
x_frqdata[:, dim * 3 + 1 - j] = x_data[:, frq +
HFFTL - j]
if ((HFFTL - 1) - (j + frq)) >= 0:
x_frqdata[:, dim + j] = x_data[:, frq + j]
x_frqdata[:, dim * 3 + 1 + j] = x_data[:, frq +
HFFTL + j]
y_frqdata = np.zeros((np.shape(y_data)[0], 1), float)
y_frqdata = y_data[:, frq].reshape(np.shape(y_data)[0], 1)
x_frqdata = x_frqdata.astype(np.float32)
y_frqdata = y_frqdata.astype(np.float32)
if args.gpu >= 0:
x_frqdata = cuda.to_gpu(x_frqdata)
y_frqdata = cuda.to_gpu(y_frqdata)
loss, pred = forward(x_frqdata, y_frqdata, train=False)
totalloss[frq] = cuda.to_cpu(loss.data)
pred = np.reshape(cuda.to_cpu(pred.data), (N_FRAMES, 1))
calcSNR = np.append(calcSNR, pred, axis=1)
fs, teacher_data = read(
teacher_signal[idx - testsize * SNRnum], "r")
if teacher_data.dtype == "int16":
teacher_data = teacher_data / norm
y_out = Sspectrum_ * calcSNR
wf_signal = FFTanalysis.Synth(y_out, synparam, BPFon=0)
wf_signal = wf_signal * np.sqrt(
np.mean(teacher_data**2) / np.mean(wf_signal**2))
write(
dir + SNR + "/dim{}_DNNbased_No{}.wav".format(
dim, idx - testsize * SNRnum), Fs, wf_signal)
print 'exec time:{0}sec'.format(
np.round(time.time() - startexec, decimals=2))
print "data: ", learned_data
from scipy.io import wavfile
import numpy as np
ii = 0
length = 20
half_length = length//2
frequency = 48000
for i in range(0, 46):
print(i)
temp_wav = wavfile.read('youtube_downloader/wav/'+str(i)+'.wav')[1]
wav_length = len(temp_wav)
if (wav_length > frequency * length):
mid = wav_length // 2
temp_wav = temp_wav[mid-half_length * frequency: mid+half_length*frequency]
if (not np.isnan(temp_wav).any()) and (not np.isinf(temp_wav).any()):
wavfile.write('48000_wavs/'+str(ii)+'.wav', frequency, temp_wav)
ii += 1
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def _read_wav(self, wave_file):
self.rate, self.data = wf.read(wave_file)
self.channels = len(self.data.shape)
self.filename = wave_file
return self
createPath(TEMP_FOLDER)
command = "ffmpeg -i " + INPUT_FILE + " -qscale:v " + str(
FRAME_QUALITY) + " " + TEMP_FOLDER + "/frame%06d.jpg -hide_banner"
subprocess.call(command, shell=True)
command = "ffmpeg -i " + INPUT_FILE + " -ab 160k -ac 2 -ar " + str(
SAMPLE_RATE) + " -vn " + TEMP_FOLDER + "/audio.wav"
subprocess.call(command, shell=True)
command = "ffmpeg -i " + TEMP_FOLDER + "/input.mp4 2>&1"
f = open(TEMP_FOLDER + "/params.txt", "w")
subprocess.call(command, shell=True, stdout=f)
sampleRate, audioData = wavfile.read(TEMP_FOLDER + "/audio.wav")
audioSampleCount = audioData.shape[0]
maxAudioVolume = getMaxVolume(audioData)
f = open(TEMP_FOLDER + "/params.txt", 'r+')
pre_params = f.read()
f.close()
params = pre_params.split('\n')
for line in params:
m = re.search('Stream #.*Video.* ([0-9]*) fps', line)
if m is not None:
frameRate = float(m.group(1))
samplesPerFrame = sampleRate / frameRate
audioFrameCount = int(math.ceil(audioSampleCount / samplesPerFrame))
import numpy as np
import scipy as sp
from scipy.io.wavfile import read
from scipy.io.wavfile import write
from scipy import signal
from scipy.signal.signaltools import wiener
import matplotlib.pyplot as plt
# get_ipython().magic('matplotlib inline')
(Frequency, array) = read('mimii_dummy.wav')
len(array)
plt.plot(array)
plt.title('Original Signal Spectrum')
plt.xlabel('Frequency(Hz)')
plt.ylabel('Amplitude')
# plt.show()
FourierTransformation = sp.fft(array)
# array = sp.fft(array)
scale = sp.linspace(0, Frequency, len(array))
plt.stem(scale[0:5000], np.abs(FourierTransformation[0:5000]), 'r')
# array = FourierTransformation
filteredSignal = signal.wiener(array)
plt.plot(filteredSignal) # plotting the signal.
plt.title('wiener filter plot')
plt.xlabel('Frequency(Hz)')
from scipy.fftpack import fft, ifft
import numpy as np
import matplotlib.pyplot as pl
from scipy.io.wavfile import read
def configFilter(data):
out = []
for i in range(1, len(data), 1):
out.append(data[i] - 0.98 * data[i - 1])
return out
fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7) = pl.subplots(7, 1, sharex=False)
rate, data = read('Xe.wav')
sizecut = 512
start = int(0.4 * rate)
# start = 100
sg = data[start:start + sizecut]
ax1.plot(sg)
ax1.set_title('Original')
sg = configFilter(sg)
ax2.plot(sg)
ax2.set_title('After Adjust Filter')
# coding:utf-8
'''
@time: Created on 2018-10-19 06:02:12
@author: Lanqing
@Func: src.wav
'''
from scipy.io import wavfile
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree, preprocessing
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
import pandas as pd, numpy as np
fs1, data1 = wavfile.read('C:/Users/jhh/Desktop/audio/REC20181019054833.wav')
fs2, data2 = wavfile.read('C:/Users/jhh/Desktop/audio/REC20181019055029.wav')
df1 = pd.DataFrame(data1).iloc[:, 0]
df2 = pd.DataFrame(data2).iloc[:, 0]
df1.plot()
plt.show()
df2.plot()
plt.show()
n = 800
timestep = 1 / 48000
list_df1 = [list(df1[i:i + n].values) for i in range(0, df1.shape[0], n)][:-1]
list_df2 = [list(df2[i:i + n].values) for i in range(0, df2.shape[0], n)][:-1]
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the sequence
if end_ix > len(sequence) - 1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
rate, raw_seq = wavfile.read('songs/hakuna_matata.wav')
raw_seq = raw_seq[np.logical_not(np.isnan(raw_seq))]
raw_seq = raw_seq.astype(int)
print(max(raw_seq))
# choose a number of time steps
n_steps = 1
# sample
#raw_seq = raw_seq # random sample. dev purposes.
# split into samples
X = raw_seq[0:1323000] #split_sequence(raw_seq, n_steps)
y = raw_seq[1323000:1345050]
# reshape from [samples, timesteps] into [samples, timesteps, features]
def audio_fft():
rate, data = wav.read('31beethovens3a.wav')
fft_out = fft(data)
signs = data / np.absolute(data)
return [fft_out, signs]
import IPython.display as ipd
import os
fulldatasetpath = "D:/Datasets/UrbanSound8K/"
os.chdir(fulldatasetpath)
import librosa
from scipy.io import wavfile as wav
import numpy as np
filename = fulldatasetpath + 'audio/fold9/106955-6-0-0.wav'
librosa_audio, librosa_sample_rate = librosa.load(filename)
scipy_sample_rate, scipy_audio = wav.read(filename)
print('Original sample rate:', scipy_sample_rate)
print('Librosa sample rate:', librosa_sample_rate)
print('Original audio file min~max range:', np.min(scipy_audio), 'to',
np.max(scipy_audio))
print('Librosa audio file min~max range:', np.min(librosa_audio), 'to',
np.max(librosa_audio))
import matplotlib.pyplot as plt
# Original audio with 2 channels
#plt.figure(figsize=(12, 4))
#plt.plot(scipy_audio)
mfccs = librosa.feature.mfcc(y=librosa_audio,
sr=librosa_sample_rate,
B = np.asarray(B).reshape(y1, x1)
rgbArray = np.zeros((y1, x1, 3), 'uint8')
rgbArray[..., 0] = R
rgbArray[..., 1] = G
rgbArray[..., 2] = B
plt.figure(dpi=1200)
plt.imshow(rgbArray, interpolation='spline16')
plt.style.use('dark_background')
plt.axis('off')
plt.savefig(filename)
plt.close()
fs, data = wavfile.read('Melulu_rev1_viz.wav') # load the data
a_0 = data.T[0] / (2.**15
) # this is a two channel soundtrack,get the first track
n_0 = len(a_0)
sample_period = 0.2 ### in seconds
sample_count = mh.floor(n_0 / (fs * sample_period))
a_1 = chunkIt(a_0, sample_count)
for a in a_1:
freqArray_top5 = np.empty([])
n = len(a)
freqArray, dB = freq_dB(a, n, fs, -300)
dB_sort = np.argsort(dB)
freqArray = freqArray[dB_sort]
def read_files():
org = wfile.read('org.wav')
long = wfile.read('long2.wav')
short = wfile.read('short2.wav')
return org, long, short
for j in range(1,taille2):
matrice_distance_elastique[0,j] = matrice_distance_elastique[0,j-1] + cout_horiz*matrice_distance[0,j]
# remplissage du reste
for i in range(1,taille1):
for j in range(1,taille2):
chemin_vert = matrice_distance_elastique[i-1,j] + cout_vert*matrice_distance[i,j]
chemin_horiz = matrice_distance_elastique[i,j-1] + cout_horiz*matrice_distance[i,j]
chemin_diag = matrice_distance_elastique[i-1,j-1] + cout_diag*matrice_distance[i,j]
matrice_distance_elastique[i,j] = min(chemin_vert, chemin_horiz, chemin_diag)
return matrice_distance_elastique[-1,-1] /(taille1+taille2)
#%% test
nbr_prononce = 5
locuteur=liste_nom[0]
essai = 2
N = 10
chemin1 = genere_nom(nbr_prononce, locuteur, essai)
chemin2 = genere_nom(5, liste_nom[0], 2)
samplerate1, data1 = wav.read(chemin1)
samplerate2, data2 = wav.read(chemin2)
if data1.ndim > 1 :
data1 = data1[:,0]
#print(np.shape(calcul_coeff_lpc_fenetre(data[0:160], 10)))
matrice1 =calcul_lpc(samplerate1, data1, 10)
matrice2 =calcul_lpc(samplerate2, data2, 10)
truc_a_print = distance_elastique(matrice1, matrice2)
print(truc_a_print)
#affichage(chemin)
def getCaliPower(file):
fs, data = read(file)
data = data.astype("float32")
filtData = bandPass(data, 1000, fs)
cali = (filtData * filtData).sum()
return cali
#print "DISTANCE!!!" + str(dist)
if dist < distmin:
distmin = dist
speaker = k
return speaker
print "|||||STARTING TEST|||||\n"
for i, wave_file in enumerate(wave_files):
fname = '/' + wave_file
to_print = 'Speaker [' + str(
i) + '] File:' + wave_file + ' Testing features...'
print to_print
(fs, s) = read(directory + fname)
#Passing test file to MFCC
mel_coefs = mfcc_p(s, fs)
mel_coefs = mel_coefs.transpose()
mel_coefs[0, :] = np.zeros(
mel_coefs.shape[1]
) # 0th coefficient does not carry significant information
#Passing test file to LPC
lpc_coefs = lpc(s, fs, orderLPC)
sp_mfcc = minDistance(mel_coefs, codebooks_mfcc)
sp_lpc = minDistance(lpc_coefs, codebooks_lpc)
print 'Speaker [' + str(i) + '] matches Speaker [' + str(
sp_mfcc) + '] ||MFCC||'
filename = '..\Data\TallShips.wav'
port = 'com4'
baudRate = 12_000_000
blockSize = 3
scale = 2 ** 21 # 75 kHz frequency deviation
lowPassCutOff = 15_000
lowPassOrder = 11
chunkSize = 20_000
updatePeriod = 1000
uart = serial.Serial(port, baudrate = baudRate, bytesize = 8, parity = 'N', stopbits = 1)
print('Loading file \'%s\'...' % filename)
audioRate, wave = wavfile.read(filename)
print('Low-pass filtering...')
b, a = signal.butter(lowPassOrder, lowPassCutOff / (audioRate / 2.0), 'low')
waveL = signal.filtfilt(b, a, wave[:, 0].astype(numpy.float))
waveR = signal.filtfilt(b, a, wave[:, 1].astype(numpy.float))
print('Resampling...')
symbolRate = baudRate / (blockSize * 10)
resampleScale = symbolRate / audioRate
waveL = interpolation.zoom(waveL, zoom = resampleScale, order = 3)
waveR = interpolation.zoom(waveR, zoom = resampleScale, order = 3)
# 'F': 'happiness',
# 'T': 'sadness',
# 'N': 'neutral',
# }
emo = {
'W': '1',
'L': '2',
'E': '3',
'A': '4',
'F': '5',
'T': '6',
'N': '0',
}
(rate, sig) = wav.read(sys.argv[1])
print("Wave File Read.")
mfcc_feat = mfcc(sig, rate)
print("MFCC Calculated.")
print("Writing to test file....")
rf = open("mfcc_file.te", "w")
for x in mfcc_feat:
j = 0
rf.write("0 ")
while j < 12:
j += 1
rf.write(str(j))
rf.write(":")
rf.write(str(x[j]))
rf.write(" ")
rf.write("\n")
def load_wav_to_torch(full_path):
"""
Loads wavdata into torch array
"""
sampling_rate, data = read(full_path)
return torch.from_numpy(data).float(), sampling_rate
def getPower(file):
fs, data = read(file)
data = data.astype("float32")
power = (data*data).sum()
return power
def get_sound_and_normalize(file_name):
rate, sound = read(file_name)
sound = sound.astype(np.float64)
sound -= sound.mean()
sound /= sound.std()
return rate, sound
natural_speech_wavfile = args['-n']
world_anasyn_speech_wavfile = args['-w']
modified_anasyn_speech_wavfile = args['-m']
labfile = args['-l']
outdir = args['-o']
outdir_for_natural = join(outdir, "natural")
outdir_for_world = join(outdir, "world")
outdir_for_modified = join(outdir, "modified")
for outdir in [outdir_for_natural, outdir_for_world, outdir_for_modified]:
if not exists(outdir):
os.makedirs(outdir)
fs, natural = wavfile.read(natural_speech_wavfile)
world_anasyn = wavfile.read(world_anasyn_speech_wavfile)[1]
modified_anasyn = wavfile.read(modified_anasyn_speech_wavfile)[1]
with open(labfile, "r") as f:
label = f.readlines()
label = [x.split(' ') for x in label]
# int(x) / 10000 -> [ms]
# [ms] / 1000 * fs -> the number of samples
label = [[
int(int(x[0]) / 10000 / 1000. * fs),
int(int(x[1]) / 10000 / 1000. * fs), x[2].split()[0]
] for x in label]
for number, lab in enumerate(label):
"""This script tests basic read/write functionality"""
import numpy as np
import sys
import matplotlib.pyplot as plt
import scipy.io.wavfile as wavfile
print(sys.path)
# write sinusoid to 16-bit, 44100 Hz PCM Mono
samplerate = 187321 # samples/s
sinusoid_frequency = 1000 # Hz
length_seconds = 1.0 # seconds
t = np.linspace(0., length_seconds, int(np.rint(length_seconds*samplerate)))
# amplitude = np.iinfo(np.int32).max
amplitude = 1.0
data = amplitude*np.sin(2.*np.pi*sinusoid_frequency*t)
data = data.astype(np.float32)
wavfile.write("..//audio_examples//example_write.wav",samplerate, data )
samplerate2, data2 = wavfile.read("..//audio_examples//OneCD.wav")
nsamples = data2.shape[0]
t2 = np.linspace(0., nsamples/samplerate2, nsamples)
# plt.plot(t,data)
plt.plot(t2,data2[:,0])
# Discard the imaginary component.
comp_clip = n.fft.ifft(comp_fft).real
# calculate actual compression ratio based on storage size
original_size = 16.0 * L # uncompressed size (16 bits per sample)
compressed_size = 8.0 * 2.0 * len(idxs) + 16.0 * len(
idxs
) + 32.0 # sparse 8-bit spectra, 16-bit spectral indices, 32-bit scale factor
real_comp_ratio = compressed_size / original_size
return (comp_clip, comp_fft[0:L2], orig_fft, real_comp_ratio)
# read wav file
# this is the audio signal to be compressed
ts = sw.read("original.wav")
sr = ts[0] # sample rate
clip = ts[1] # extract audio file as numpy data vector
if len(clip.shape) == 2: # if stereo, only use one channel
print("using only one stereo channel")
clip = ts[1][:, 0]
# compress and decompress audio file
# compression_ratio=0.95 means 95% reduction in
# file size.
cr = 0.95
# compress full length of the clip
W = 100000
n_window = int(n.floor(len(clip) / W))
comp_clip = n.zeros(len(clip))
