def gen_audio_stim(MatrixDir, OutDir, indexes):
if os.path.exists(OutDir):
shutil.rmtree(OutDir)
os.makedirs(OutDir)
wavFiles = globDir(MatrixDir, '*.wav')
wavFileMatrix = organiseWavs(wavFiles)
wavDir = os.path.join(OutDir, "wav")
dir_must_exist(wavDir)
wavDir = os.path.join(wavDir, "noise-sentences")
dir_must_exist(wavDir)
files = []
n = 0
o = 0
for sentenceList in indexes:
n += 1
o = 0
files.append([])
for ind in sentenceList:
o += 1
y, wavInfo, partnames = synthesize_trial(wavFileMatrix, ind)
fileName = os.path.join(wavDir,
'Trial_{0:02d}_{1:02d}.wav'.format(n, o))
print("Generating: " + fileName)
sndio.write(fileName, y, **wavInfo)
files[-1].append(fileName)
return files
def generate_audio_stimulus(MatrixDir, OutDir, indexes, socketio=None):
# Get matrix wav file paths
wavFiles = globDir(MatrixDir, '*.wav')
wavFileMatrix = organiseWavs(wavFiles)
wav_dir = os.path.join(args['OutDir'], "wav")
dir_must_exist(wav_dir)
sentence_dir = os.path.join(wav_dir, "sentence-lists")
dir_must_exist(sentence_dir)
# Synthesize audio for each trial using generated word choices
sentence_lists = {}
for key in indexes.keys():
files = []
list_dir = os.path.join(sentence_dir, key)
dir_must_exist(list_dir)
with open(os.path.join(list_dir, 'stim_parts.csv'), 'w') as csvfile:
partwriter = csv.writer(csvfile)
inds, strings = indexes[key]
for sentence_ind, (component_inds, component_strings) in enumerate(
zip(inds, strings)):
if socketio:
percent = (l / Length) * 100.
socketio.emit('update-progress',
{'data': '{}%'.format(percent)},
namespace='/main')
y, wavInfo, partnames = synthesize_trial(
wavFileMatrix, component_inds)
partwriter.writerow(component_strings)
file_name = os.path.join(
list_dir, 'Trial_{0:05d}.wav'.format(sentence_ind + 1))
sndio.write(file_name, y, **wavInfo)
files.append(file_name)
sentence_lists[key] = np.array(files)
return sentence_lists
def main():
'''
'''
fs = 44100
f = 1000.0
n = np.arange(fs * 60 * 5)
y = np.sin(2*np.pi*f*n/fs)
y = np.array([y, y]).T
sndio.write("./out/stimulus/1k_tone.wav", y, fs, format='wav', enc='pcm16')
def main():
wavs = globDir('./', 'stim.wav')
for wav in wavs:
x, fs, enc, fmt = sndio.read(wav, return_format=True)
y = x[:, :2]
head, tail = os.path.splitext(wav)
out_filepath = "{0}_old{1}".format(head, tail)
os.rename(wav, out_filepath)
sndio.write(wav, y, rate=fs, format=fmt, enc=enc)
def write_wav_skip_existing(path, y, sr):
if not os.path.exists(path):
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path), exists_ok=True)
sndio.write(path, data=y, rate=sr, format="wav", enc='pcm16')
#soundfile.write(path, y, sr, "PCM_16")
else:
print("WARNING: Tried writing audio to " + path +
", but audio file exists already. Skipping file!")
return Sample.from_array(path, y, sr)
def main():
'''
'''
wavs = globDir("./stimulus", "*.wav")
for wav in wavs:
x, fs, enc, fmt = sndio.read(wav, return_format=True)
idx = np.arange(x.shape[0])
breakpoint()
y = np.vstack([x, x, np.zeros(x.shape[0])]).T
trigger = gen_trigger(idx, 2., 0.01, fs)
y[:, 2] = trigger
wav_out = os.path.splitext(wav)[0] + "_trig.wav"
sndio.write(wav_out, y, rate=fs, format=fmt, enc=enc)
def main():
'''
Generate train of equally spaced clicks
'''
fs = 44100
n = np.arange(fs * 10)
trig_s = np.where(np.mod(n, fs / 2.) == 0)
click = np.ones(int(0.01 * fs))
y = np.zeros(n.size)
for i in trig_s[0]:
y[i:i + click.size] = click
sndio.write("./trig_test.wav", y, fs, format='wav', enc='pcm16')
pdb.set_trace()
def main():
'''
'''
freq = 20.0
fs = 44100
period = fs / freq
length = period * 3000.
y = (np.arange(length) % period == 0).astype(float)
y[np.where(y == 1.0)[0][1::2]] = -1.0
y = np.concatenate([np.zeros(fs), y, np.zeros(fs)])
idx = np.arange(y.size)
trigger = gen_trigger(idx, 2., 0.01, fs)
y = np.vstack((y, y, trigger)).T
print("Number of clicks generated: {}".format(
np.sum(np.abs(y[:, 0]) == 1.0)))
sndio.write('./click_3000_20Hz.wav', y, rate=fs, format='wav', enc='pcm16')
def _save_estimates(self,
user_estimates,
track,
estimates_dir,
write_stems=False):
track_estimate_dir = op.join(estimates_dir, track.subset, track.name)
if not os.path.exists(track_estimate_dir):
os.makedirs(track_estimate_dir)
# write out tracks to disk
for target, estimate in list(user_estimates.items()):
target_path = op.join(track_estimate_dir, target + '.wav')
try:
sndio.write(target_path,
estimate,
track.rate,
format="wav",
enc="pcm16")
except NameError:
sf.write(target_path, estimate, track.rate)
pass
def flattenRMS(AudioFile, AnnotationFile):
with open(AnnotationFile, 'r') as f:
csvData = pd.read_csv(f)
data, fs, encStr, fmtStr = sndio.read(AudioFile, return_format=True)
csvData['start'] *= fs
csvData['start'] = csvData['start'].astype(int)
csvData['stop'] *= fs
csvData['stop'] = csvData['stop'].astype(int)
zerox = np.where(np.diff(np.sign(data)))[0]
# get silent sections
silences = csvData.loc[csvData['name'] == '#']
audio = csvData.loc[csvData['name'] != '#']
# Find nearest zero-crossing to start and stop times of silences
nearestZerox = zerox[np.abs(zerox - csvData['start'][:, np.newaxis]).argmin(axis=1)]
csvData['start'] = nearestZerox
nearestZerox = zerox[np.abs(zerox - csvData['stop'][:, np.newaxis]).argmin(axis=1)]
csvData['stop'] = nearestZerox
csvData['rms'] = np.nan
for ind, chunk in csvData.iterrows():
if not chunk['name'] == '#':
rms = np.sqrt(np.mean(data[chunk['start']:chunk['stop']]**2))
csvData.iloc[ind, csvData.columns.get_loc('rms')] = rms
avgRMS = csvData['rms'][csvData['rms'].notnull()].mean()
silentData = np.zeros(int(0.3*fs))
out = np.array([])
for ind, chunk in csvData.iterrows():
if chunk['name'] == '#':
out = np.append(out, silentData)
else:
rmsCorFactor = avgRMS / chunk['rms']
out = np.append(out, data[chunk['start']:chunk['stop']])#*rmsCorFactor)
print(np.sqrt(np.mean((data[chunk['start']:chunk['stop']]*rmsCorFactor)**2)))
sndio.write('./out.wav', out, rate=fs, format=fmtStr, enc=encStr)
def gen_da_stim(n, outpath):
da_file = './BioMAP_da-40ms.wav'
da_stim, fs, enc, fmt = sndio.read(da_file, return_format=True)
prestim_size = 0.0158
# Repetition rate in Hz
repetition_rate = 10.9
full_stim_size = 1. / repetition_rate
da_size = da_stim.size / fs
prestim = np.zeros(int(fs * prestim_size))
poststim = np.zeros(int(fs * ((full_stim_size - prestim_size) - da_size)))
y_part = np.concatenate([prestim, da_stim, poststim])
pdb.set_trace()
y_part_inv = -y_part
loc_part = np.zeros(y_part.size)
loc_part[prestim.size + 1] = 1
y_2part = np.concatenate([y_part, y_part_inv])
loc = np.concatenate([loc_part, loc_part])
y_r = np.tile(y_2part, n)
loc = np.tile(loc, n)
loc = np.insert(loc, 0, np.zeros(fs))
loc = np.where(loc)[0]
y_r = np.insert(y_r, 0, np.zeros(fs))
y_r = resampy.resample(y_r, fs, 44100)
rat = 44100 / fs
fs = 44100
y_l = np.zeros(y_r.size)
loc = loc * rat
loc = loc.round().astype(int)
np.save('./stimulus/3000_da_locs.npy', loc)
idx = np.arange(y_l.size)
trigger = gen_trigger(idx, 2., 0.01, fs)
y = np.vstack((y_l, y_r, trigger)).T
sndio.write(outpath, y, rate=44100, format=fmt, enc=enc)
return outpath
def main():
'''
'''
fs = 44100
f = 1000.0
n = np.arange(fs * 60 * 5)
y = np.sin(2 * np.pi * f * n / fs)
coef = np.load('./out/calibration_coefficients/click_cal_coef.npy')
y *= coef
dir_must_exist('./out/calibrated_stim/')
sndio.write("./out/calibrated_stim/1k_tone.wav",
y,
fs,
format='wav',
enc='pcm16')
coef = np.load('./out/calibration_coefficients/da_cal_coef.npy')
y, fs, enc = sndio.read('./out/stimulus/da_cal_stim.wav')
sndio.write('./out/calibrated_stim/da_cal_stim.wav',
y * coef,
fs,
format='wav',
enc='pcm16')
coef = np.load('./out/calibration_coefficients/mat_cal_coef.npy')
y, fs, enc = sndio.read('./out/stimulus/mat_cal_stim.wav')
sndio.write('./out/calibrated_stim/mat_cal_stim.wav',
y * coef,
fs,
format='wav',
enc='pcm16')
coef = np.load('./out/calibration_coefficients/story_cal_coef.npy')
y, fs, enc = sndio.read('./out/stimulus/story_cal_stim.wav')
sndio.write('./out/calibrated_stim/story_cal_stim.wav',
y * coef,
fs,
format='wav',
enc='pcm16')
# Create directory for saving the pre-processed data if it does not exist
if not os.path.exists(args.WAV_path):
os.makedirs(args.WAV_path)
print("Created " + args.WAV_path + " directory.")
# Create directory for saving the pre-processed data if it does not exist
if not os.path.exists(args.frames_path):
os.makedirs(args.frames_path)
print("Created " + args.frames_path + " directory.")
for filename in tqdm(os.listdir(args.WAV_path)):
samples, sample_freq = load_audio(args.WAV_path + filename)
resample_freq = sample_freq
samples = samples.flatten()
if args.resample_freq is not None:
secs = len(samples) / sample_freq # Calculate seconds of wav audio
sample_count = secs * float(
args.resample_freq
) # Calculate samples needed in the resampled signal with the desired frequency
samples = resample(samples, int(sample_count)) # Resample
sample_freq = args.resample_freq
resample_freq = int(args.resample_freq)
seq_length = args.win_length * sample_freq
frames = enframe2(samples, args.win_length, args.win_overlap)
# Save frames to wav files
for idx, frame in enumerate(frames):
write(args.frames_path + "_" + str(idx) + "_" + filename,
frame,
rate=resample_freq)
def loadStimulus(self):
'''
'''
self.participant.load('mat_test')
try:
srt_50 = self.participant.data['mat_test']['srt_50']
s_50 = self.participant.data['mat_test']['s_50']
except KeyError:
raise KeyError(
"Behavioural matrix test results not available, make "
"sure the behavioural test has been run before "
"running this test.")
save_dir = self.participant.data_paths['eeg_test/stimulus']
'''
# Estimate speech intelligibility thresholds using predicted
# psychometric function
s_50 *= 0.01
x = logit(self.si * 0.01)
snrs = (x/(4*s_50))+srt_50
snrs = np.append(snrs, np.inf)
snr_map = pd.DataFrame({"speech_intel" : np.append(self.si, 0.0), "snr": snrs})
snr_map_path = os.path.join(save_dir, "snr_map.csv")
snr_map.to_csv(snr_map_path)
snrs = np.repeat(snrs[np.newaxis], 4, axis=0)
snrs = roll_independant(snrs, np.array([0,-1,-2,-3]))
stim_dirs = [x for x in os.listdir(self.listDir) if os.path.isdir(os.path.join(self.listDir, x))]
shuffle(stim_dirs)
'''
snrs = self.participant.data['parameters']['decoder_test_SNRs'] + srt_50
stim_dirs = [
x for x in os.listdir(self.listDir)
if os.path.isdir(os.path.join(self.listDir, x))
]
ordered_stim_dirs = []
for ind in self.participant_parameters['decoder_test_lists']:
for folder in stim_dirs:
if re.match(f'Stim_({int(ind)})', folder):
ordered_stim_dirs.append(folder)
# ordered_stim_dirs *= int(len(snrs))
noise_file = PySndfile(self.noise_path, 'r')
wav_files = []
wav_metas = []
question = []
marker_files = []
self.socketio.emit('test_stim_load', namespace='/main')
for ind, dir_name in enumerate(ordered_stim_dirs[:snrs.shape[1]]):
logger.debug(
f"Processing list directory {ind+1} of {snrs.shape[1]}")
stim_dir = os.path.join(self.listDir, dir_name)
wav = globDir(stim_dir, "*.wav")[0]
csv_files = natsorted(globDir(stim_dir, "*.csv"))
marker_file = csv_files[0]
question_files = csv_files[1:]
# rms_file = globDir(stim_dir, "*.npy")[0]
# speech_rms = float(np.load(rms_file))
snr = snrs[:, ind]
audio, fs, enc, fmt = sndio.read(wav, return_format=True)
speech = audio[:, :2]
triggers = audio[:, 2]
#speech_rms, _, _ = asl_P56(speech, fs, 16.)
rms_no_silences(speech, fs, -30.)
wf = []
wm = []
for ind2, s in enumerate(snr):
start = randint(0, noise_file.frames() - speech.shape[0])
noise_file.seek(start)
noise = noise_file.read_frames(speech.shape[0])
noise_rms = np.sqrt(np.mean(noise**2))
# noise_rms = asl_P56(noise, fs, 16)
snr_fs = 10**(-s / 20)
if snr_fs == np.inf:
snr_fs = 0.
elif snr_fs == -np.inf:
raise ValueError(
"Noise infinitely louder than signal at snr: {}".
format(snr))
noise = noise * (speech_rms / noise_rms)
out_wav_path = os.path.join(
save_dir, "Stim_{0}_{1}.wav".format(ind, ind2))
out_meta_path = os.path.join(
save_dir, "Stim_{0}_{1}.npy".format(ind, ind2))
with np.errstate(divide='raise'):
try:
out_wav = (speech + (np.stack([noise, noise], axis=1) *
snr_fs)) * self.reduction_coef
except:
set_trace()
out_wav = np.concatenate([out_wav, triggers[:, np.newaxis]],
axis=1)
sndio.write(out_wav_path, out_wav, fs, fmt, enc)
np.save(out_meta_path, s)
wf.append(out_wav_path)
wm.append(out_meta_path)
wav_metas.append(wm)
wav_files.append(wf)
out_marker_path = os.path.join(save_dir,
"Marker_{0}.csv".format(ind))
marker_files.append(out_marker_path)
copyfile(marker_file, out_marker_path)
for q_file in question_files:
out_q_path = os.path.join(
save_dir, "Questions_{0}_{1}.csv".format(ind, ind2))
self.question_files.append(out_q_path)
copyfile(q_file, out_q_path)
for q_file_path in question_files:
q = []
with open(q_file_path, 'r') as q_file:
q_reader = csv.reader(q_file)
for line in q_reader:
q.append(line)
question.append(q)
self.wav_files = [item for sublist in wav_files for item in sublist]
self.wav_metas = [item for sublist in wav_metas for item in sublist]
self.question.extend(question)
for item in marker_files:
self.marker_files.extend([item] * 4)
self.answers = np.empty(np.shape(self.question)[:2])
self.answers[:] = np.nan