def segment(data, seg_location, length):
os.chdir(data)
files = os.listdir(data)
speakers = speech_data.get_speakers(data)
waves = []
num = {}
for s in speakers:
num[s] = 0
c = 0
for f in files: # grab all wave files in list
waves.append(audio.from_wav(f))
c = c + 1
os.chdir(seg_location)
for f, w in zip(
files,
waves): # need to segment the data into one second intervals
begin = 0
end = 1
while (end * length) < int(w.duration_seconds):
segment = w[begin * 1000 * length:end * 1000 * length]
segment.export(
speech_data.speaker(f) + '_' +
str(num[speech_data.speaker(f)]) + '.wav', 'wav')
begin = begin + length
end = end + length
num[speech_data.speaker(f)] = num[speech_data.speaker(f)] + 1
def main():
speakers = data.get_speakers()
number_classes = len(speakers)
print("speakers", speakers)
model = make_model(number_classes)
model.load('classifier')
stream = audio.Stream()
while True:
raw_input('press enter to record!!!')
buff = stream.record(1.5)
sample = audio.stream_to_ints(buff)
test(model, speakers, sample)
def main():
speakers = data.get_speakers()
number_classes = len(speakers)
print("speakers", speakers)
# train(number_classes)
# return
model = ml.make_model(number_classes)
model.load('classifier')
stream = audio.Stream()
while True:
input('press enter to record!!!')
buff = stream.record(1.5)
sample = audio.stream_to_ints(buff)
label, conf = ml.predict(model, speakers, sample)
print("predicted : result = %s confidence = %.2f" % (label, conf))
def handle_speaker_rec_test_intent(self, message):
speakers = data.get_speakers()
number_classes = len(speakers)
#print("speakers",speakers)
#batch=data.wave_batch_generator(batch_size=1000, source=data.Source.DIGIT_WAVES, target=data.Target.speaker)
#X,Y=next(batch)
# Classification
#tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
net = tflearn.input_data(shape=[None, 8192]) #Two wave chunks
net = tflearn.fully_connected(net, 64)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net,
number_classes,
activation='softmax')
net = tflearn.regression(net,
optimizer='adam',
loss='categorical_crossentropy')
model = tflearn.DNN(net)
#model.fit(X, Y, n_epoch=100, show_metric=True, snapshot_step=100)
CWD_PATH = os.path.dirname(__file__)
path_to_model = os.path.join(CWD_PATH, 'model', 'model.tfl')
model.load(path_to_model)
demo_file = "8_Vicki_260.wav"
#demo_file = "8_Bruce_260.wav"
demo = data.load_wav_file(data.path + demo_file)
result = model.predict([demo])
result = data.one_hot_to_item(result, speakers)
if result == "Vicki":
self.speak("I am confident I'm speaking to %s" %
(result)) # ~ 97% correct
else:
self.speak("I'm sorry I don't recognize your voice")
#!/usr/bin/env PYTHONIOENCODING="utf-8" python
import tflearn
import pyaudio
import speech_data as data
# Simple speaker recognition demo, with 99% accuracy in under a minute ( on digits sample )
# | Adam | epoch: 030 | loss: 0.05330 - acc: 0.9966 -- iter: 0000/1000
# 'predicted speaker for 9_Vicki_260 : result = ', 'Vicki'
speakers = data.get_speakers()
number_classes = len(speakers)
##print("speakers",speakers)
WORD_WAVs = "spoken_words"
batch = data.wave_batch_generator(batch_size=1000,
source=WORD_WAVs,
target=data.Target.speaker)
X, Y = next(batch)
# Classification
tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
net = tflearn.input_data(shape=[None, 8192]) #Two wave chunks
net = tflearn.fully_connected(net, 64)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net, number_classes, activation='softmax')
net = tflearn.regression(net,
optimizer='adam',
loss='categorical_crossentropy')
import os import sys import librosa import tflearn import wave import pickle import tensorflow as tf import librosa.display import IPython.display import numpy as np import speech_data from pydub import AudioSegment as audio # now put all of the mfccs into an array data = '/home/cc/working/data/devclean_2_seg/' speakers = speech_data.get_speakers(data) audio_files = os.listdir(data) mfccs = [] Y = [] for f in audio_files: Y.append(speech_data.one_hot_from_item(speech_data.speaker(f), speakers)) y, sr = librosa.load(data + f) mfccs.append(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)) net = tflearn.input_data(shape=[None, 13, 44]) net = tflearn.fully_connected(net, 64) net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, 32) net = tflearn.fully_connected(net, len(speakers), activation='softmax') net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')
import os
import librosa
import tflearn
import speech_data
from pydub import AudioSegment as audio
speakers = speech_data.get_speakers('/home/cc/working/data/devclean_seg/')
net = tflearn.input_data(shape=[None, 13, 44])
net = tflearn.fully_connected(net, 64)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net, len(speakers), activation='softmax')
net = tflearn.regression(net,
optimizer='adam',
loss='categorical_crossentropy')
model = tflearn.DNN(net, tensorboard_verbose=3)
model.load('/home/cc/working/models/devclean/devclean_train.tflearn')
os.chdir('/home/cc/working/data/devclean_test/')
test = []
for f1 in os.listdir(os.getcwd()):
y, sr = librosa.load(f1)
test.append(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13))
result = model.predict(test)
c = 0
for f, r in zip(os.listdir(os.getcwd()), result):
res = speech_data.one_hot_to_item(r, speakers)
if res in f:
c = c + 1
print('correct: %s ; total: %s' % (str(c), str(len(test))))
import os import sys import librosa import tflearn import wave import tensorflow as tf import librosa.display import IPython.display import numpy as np import speech_data from pydub import AudioSegment as audio # now put all of the mfccs into an array data = '/home/cc/working/data/devclean_seg/' os.chdir(data) speakers = speech_data.get_speakers(os.getcwd()) audio_files = os.listdir(os.getcwd()) mfccs = [] Y = [] for f in audio_files: Y.append(speech_data.one_hot_from_item(speech_data.speaker(f), speakers)) y, sr = librosa.load(f) mfccs.append(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)) net = tflearn.input_data(shape=[None, 13, 44]) net = tflearn.fully_connected(net, 64) net = tflearn.dropout(net, 0.6) net = tflearn.fully_connected(net, len(speakers), activation='softmax') net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy') model = tflearn.DNN(net)
import numpy as np
# load constants - training directory, testing directory
training_seg = '/home/cc/Data/Dev-Clean-Train-Two/'
testing = '/home/cc/Data/Dev-Clean-Test-Two'
# size of fully connected layers
n = sys.argv[1]
#l = sys.argv[2]
m = 18
d = 0.8
# calculate the mfcc matrices for training from the segmented data
#X = []
#Y = []
speakers = speech_data.get_speakers(training_seg)
#for f in os.listdir(training_seg):
# Y.append(speech_data.one_hot_from_item(speech_data.speaker(f), speakers))
# y, sr = librosa.load(training_seg + f)
# X.append(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=int(m)))
#pickle.dump(X, open('/home/cc/Data/pickle_files/mfcc_len/train' + str(m) + '_X.p', 'wb'))
#pickle.dump(Y, open('/home/cc/Data/pickle_files/mfcc_len/train' + str(m) + '_Y.p', 'wb'))
X = pickle.load(
open('/home/cc/Data/pickle_files/mfcc_len/train' + str(m) + '_X.p', 'rb'))
Y = pickle.load(
open('/home/cc/Data/pickle_files/mfcc_len/train' + str(m) + '_Y.p', 'rb'))
# define the network and the model for training
tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
import wave
import pickle
import speech_data
import segment_data
import tensorflow as tf
import librosa.display
import numpy as np
# load constants - training directory, testing directory
training = '/home/cc/Data/train/'
testing = '/home/cc/Data/test/'
# calculate the mfcc matrices for training from the segmented data
X = []
Y = []
speakers = speech_data.get_speakers(training)
for f in os.listdir(training):
Y.append(speech_data.one_hot_from_item(speech_data.speaker(f), speakers))
y, sr = librosa.load(training + f)
mfcc = np.asarray(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20))
X.append(mfcc)
# input size for fully connected layers
layer_size = int(sys.argv[1])
dropout = float(sys.argv[2])
# define the network and the model for training
tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
# for just mfcc
net = tflearn.input_data(shape=[None, 20, 87])
#!/usr/bin/env PYTHONIOENCODING="utf-8" python
import random
random.seed(5)
import tflearn
import os
import sys
import librosa
import speech_data as data
import numpy as np
import pickle
test_data = '/home/edresson/Pti-embbending/Encoder-MFCC/Automatizado/Bases/Segments-5s/Validacao/Base1/X/'
train_data = '/home/edresson/Pti-embbending/Encoder-MFCC/Automatizado/Bases/Segments-5s/Treino/Base1/X-2/'
working = ''
# grab the speakers from the training directory
speakers = data.get_speakers(train_data)
number_classes = len(speakers)
#print(number_classes,speakers)
# create the MFCC arrays from the data for training
audio_files = os.listdir(working + train_data)
X = []
Y = []
try:
with open('rna-treino_X-5s.txt', 'rb') as f:
X = pickle.load(f)
with open('rna-treino_Y-5s.txt', 'rb') as f:
Y = pickle.load(f)
#!/usr/bin/env PYTHONIOENCODING="utf-8" python
import tflearn
import pyaudio
import speech_data as data
# Simple speaker recognition demo, with 99% accuracy in under a minute ( on digits sample )
# | Adam | epoch: 030 | loss: 0.05330 - acc: 0.9966 -- iter: 0000/1000
# 'predicted speaker for 9_Vicki_260 : result = ', 'Vicki'
speakers = data.get_speakers()
number_classes=len(speakers)
print("speakers",speakers)
WORD_WAVs="spoken_words"
batch=data.wave_batch_generator(batch_size=1000,source=WORD_WAVs,target=data.Target.speaker)
X,Y=next(batch)
# Classification
tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
net = tflearn.input_data(shape=[None, 8192]) #Two wave chunks
net = tflearn.fully_connected(net, 64)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net, number_classes, activation='softmax')
net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')
model = tflearn.DNN(net)
model.fit(X, Y, n_epoch=100, show_metric=True, snapshot_step=100)
#!/usr/bin/env PYTHONIOENCODING="utf-8" python import tflearn import os import speech_data as data # training and testing data sets train_data = '/home/cc/Data/small-clean-train/' test_data = '/home/cc/Data/small-clean-test/' # grab the speakers from the training directory speakers = data.get_speakers(train_data) number_classes = len(speakers) # create the MFCC arrays from the data for training batch=data.wave_batch_generator(batch_size=1000,source=WORD_WAVs,target=data.Target.speaker,speakers=speakers) X,Y=next(batch) # Classification tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5) net = tflearn.input_data(shape=[None, 8192]) #Two wave chunks net = tflearn.fully_connected(net, 64) # seems like a higher dropout rate works better -- why is this?? net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, number_classes, activation='softmax') net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy') model = tflearn.DNN(net) model.fit(X, Y, n_epoch=100, show_metric=True, snapshot_step=100)
