def train(callback=None, out_weights='weights.h5'):
reload(audiotransform)
reload(speechmodel)
hz = 6000
repeat = 1
goalSize = 30000 # samples after padding
embedSize = 10
model = speechmodel.makeModel()
model.compile(loss='mean_squared_error',
optimizer='rmsprop',
metrics=['accuracy'])
paths = []
words = []
for p in sampleSet1(): # or findSounds(words)
try:
raw = load(p, hz=hz)
crop = audiotransform.autoCrop(raw, rate=hz)
audiotransform.randomPad(crop, goalSize) # must not error
print 'using %s cropped to %s samples' % (p, len(crop))
except audiotransform.TooQuiet:
print '%s too quiet' % p
continue
paths.append(p)
word = soundFields(p)['word']
if word not in words:
words.append(word)
x = numpy.zeros((len(paths) * repeat, goalSize), dtype=numpy.float)
y = numpy.zeros((len(paths) * repeat, embedSize), dtype=numpy.float)
for row, p in enumerate(paths * repeat):
audio = load(p, hz=hz)
audio = audiotransform.autoCrop(audio, rate=hz)
#audio = audiotransform.rightPad(audio, goalSize)
audio = audiotransform.randomPad(audio, goalSize, path=p)
audio = audiotransform.randomScale(audio)
x[row,:] = audio
y[row,:] = np_utils.to_categorical(words.index(soundFields(p)['word']),
embedSize)
if callback:
callback.loaded_sound(row, len(paths) * repeat)
callbacks = []
#callbacks.append(keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True))
if callback:
callbacks.append(callback)
model.fit(x, y, batch_size=100, nb_epoch=20, validation_split=.0,
shuffle=True,
callbacks=callbacks)
model.save_weights(out_weights)
if callback:
callback.on_save(out_weights, fileSize=os.path.getsize(out_weights))
def findSounds(words):
# incomplete, no user filtering
top = FilePath('sounds/incoming/13EubbAsOYgy3eZX4LAHsB5Hzq72/will')
for p in sorted(top.walk()):
if p.isfile():
word = soundFields(p.path)['word']
yield p.path
def findSounds(words):
# incomplete, no user filtering
top = FilePath('sounds/incoming/')
for p in sorted(top.walk()):
if p.isfile():
word = soundFields(p)['word']
if word not in words:
continue
yield p.path
def get(self):
top = FilePath('sounds')
self.write({
'sounds': [{
'path': '/'.join(p.segmentsFrom(top)),
'fields': soundFields('/'.join(p.segmentsFrom(top))),
} for p in sorted(top.walk()) if p.isfile()],
'hostname':
socket.gethostname(),
})
def train(callback=None, out_weights='weights.h5'):
reload(audiotransform)
reload(speechmodel)
model = speechmodel.makeModel()
model.compile(loss='mean_squared_error',
optimizer=keras.optimizers.Nadam(lr=0.00002,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
schedule_decay=0.004),
metrics=['accuracy'])
paths = []
words = []
for p in sampleSet2(): # or findSounds(words)
try:
raw = load(p, hz=speechmodel.rate)
except:
print "load failed", p
continue
try:
crop = audiotransform.autoCrop(raw, rate=speechmodel.rate)
print 'using %s autocropped to %s samples' % (p, len(crop))
except audiotransform.TooQuiet:
print '%s too quiet' % p
continue
paths.append(p)
word = soundFields(p)['word']
if word not in words:
words.append(word)
repeat = 2
x = numpy.zeros((len(paths) * repeat, speechmodel.xWidth),
dtype=numpy.float)
y = numpy.zeros((len(paths) * repeat, speechmodel.embedSize),
dtype=numpy.float)
for row, p in enumerate(paths * repeat):
audio = load(p, hz=speechmodel.rate)
audio = audiotransform.autoCrop(audio, rate=speechmodel.rate)
#audio = audiotransform.rightPad(audio, speechmodel.goalSize)
audio = audiotransform.randomPad(audio, speechmodel.goalSize, path=p)
audio = audiotransform.randomScale(audio)
m = mfcc(audio, samplerate=speechmodel.rate)
x[row, :] = m.reshape((1, speechmodel.xWidth))
y[row, :] = np_utils.to_categorical(
words.index(soundFields(p)['word']), speechmodel.embedSize)
if callback:
callback.loaded_sound(row, len(paths) * repeat)
callbacks = []
#callbacks.append(keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True))
if callback:
callbacks.append(callback)
model.fit(x,
y,
batch_size=500,
epochs=500,
validation_split=.2,
shuffle=True,
callbacks=callbacks)
model.save_weights(out_weights)
with open(out_weights + '.words', 'w') as f:
f.write(json.dumps(words) + '\n')
if callback:
callback.on_save(out_weights, fileSize=os.path.getsize(out_weights))
def sampleSet3():
return [
p
for p in glob.glob('sounds/incoming/d8Lo6MJMqZOGXeGDbnHkpXzeovY2/*/*')
if soundFields(p)['word'] in ['i', 'like', 'pizza']
]