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speech_model_trained.py
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speech_model_trained.py
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__author__ = 'pat'
'''
Bidirectional Recurrent Neural Network
with Connectionist Temporal Classification (CTC)
courtesy of https://github.com/shawntan/rnn-experiment
courtesy of https://github.com/rakeshvar/rnn_ctc
implemented in Theano and optimized for use on a GPU
'''
import theano
import theano.tensor as T
from theano_toolkit import utils as U
from theano_toolkit import updates
import numpy as np
import cPickle as pickle
import time
#THEANO_FLAGS='device=cpu,floatX=float32'
#theano.config.warn_float64='warn'
#theano.config.optimizer = 'fast_compile'
theano.config.exception_verbosity='high'
theano.config.on_unused_input='warn'
class FeedForwardLayer:
def __init__(self, inputs, input_size, output_size, rng, dropout_rate, parameters=None):
self.activation_fn = lambda x: T.minimum(x * (x > 0), 20)
if parameters is None:
self.W = U.create_shared(U.initial_weights(input_size, output_size), name='W')
self.b = U.create_shared(U.initial_weights(output_size), name='b')
else:
self.W = theano.shared(parameters['W'], name='W')
self.b = theano.shared(parameters['b'], name='b')
self.output = T.cast(self.activation_fn( (T.dot(inputs, self.W) + self.b)*(1.0-dropout_rate) ), dtype=theano.config.floatX)
self.params = [self.W, self.b]
def get_parameters(self):
params = {}
for param in self.params:
params[param.name] = param.get_value()
return params
def set_parameters(self, parameters):
self.W.set_value(parameters['W'])
self.b.set_value(parameters['b'])
class RecurrentLayer:
def __init__(self, inputs, input_size, output_size, is_backward=False, parameters=None):
if parameters is None:
self.W_if = U.create_shared(U.initial_weights(input_size, output_size), name='W_if')
self.W_ff = U.create_shared(U.initial_weights(output_size, output_size), name='W_ff')
self.b = U.create_shared(U.initial_weights(output_size), name='b')
else:
self.W_if = theano.shared(parameters['W_if'], name='W_if')
self.W_ff = theano.shared(parameters['W_ff'], name='W_ff')
self.b = theano.shared(parameters['b'], name='b')
initial = T.zeros((output_size,))
self.is_backward = is_backward
self.activation_fn = lambda x: T.cast(T.minimum(x * (x > 0), 20), dtype='float32')#dtype=theano.config.floatX)
nonrecurrent = T.dot(inputs, self.W_if) + self.b
self.output, _ = theano.scan(
lambda in_t, out_tminus1, weights: self.activation_fn(in_t + T.dot(out_tminus1, weights)),
sequences=[nonrecurrent],
outputs_info=[initial],
non_sequences=[self.W_ff],
go_backwards=self.is_backward
)
self.params = [self.W_if, self.W_ff, self.b]
def get_parameters(self):
params = {}
for param in self.params:
params[param.name] = param.get_value()
return params
def set_parameters(self, parameters):
self.W_if.set_value(parameters['W_if'])
self.W_ff.set_value(parameters['W_ff'])
self.b.set_value(parameters['b'])
class SoftmaxLayer:
def __init__(self, inputs, input_size, output_size, parameters=None):
if parameters is None:
self.W = U.create_shared(U.initial_weights(input_size, output_size), name='W')
self.b = U.create_shared(U.initial_weights(output_size), name='b')
else:
self.W = theano.shared(parameters['W'], name='W')
self.b = theano.shared(parameters['b'], name='b')
self.output = T.nnet.softmax(T.dot(inputs, self.W) + self.b)
self.params = [self.W, self.b]
def get_parameters(self):
params = {}
for param in self.params:
params[param.name] = param.get_value()
return params
def set_parameters(self, parameters):
self.W.set_value(parameters['W'])
self.b.set_value(parameters['b'])
class BRNN:
def __init__(self, input_dimensionality, output_dimensionality, params=None, learning_rate=0.0001, momentum=.25):
self.input_dimensionality = input_dimensionality
self.output_dimensionality = output_dimensionality
self.learning_rate = learning_rate
srng = theano.tensor.shared_randomstreams.RandomStreams(seed=1234)
input_seq = T.fmatrix('input_seq')
dropoutRate = T.fscalar('dropoutRate')
if params is None:
self.ff1 = FeedForwardLayer(input_seq, self.input_dimensionality, 2000, rng=srng, dropout_rate=dropoutRate)
self.ff2 = FeedForwardLayer(self.ff1.output, 2000, 1000, rng=srng, dropout_rate=dropoutRate)
self.ff3 = FeedForwardLayer(self.ff2.output, 1000, 800, rng=srng, dropout_rate=dropoutRate)
self.rf = RecurrentLayer(self.ff3.output, 800, 500, False) # Forward layer
self.rb = RecurrentLayer(self.ff3.output, 800, 500, True) # Backward layer
# REVERSE THE BACKWARDS RECURRENT OUTPUTS IN TIME (from [T-1, 0] ===> [0, T-1]
self.s = SoftmaxLayer(T.concatenate((self.rf.output, self.rb.output[::-1, :]), axis=1), 2*500, self.output_dimensionality)
else:
self.ff1 = FeedForwardLayer(input_seq, self.input_dimensionality, 2000, parameters=params[0], rng=srng, dropout_rate=dropoutRate)
self.ff2 = FeedForwardLayer(self.ff1.output, 2000, 1000, parameters=params[1], rng=srng, dropout_rate=dropoutRate)
self.ff3 = FeedForwardLayer(self.ff2.output, 1000, 800, parameters=params[2], rng=srng, dropout_rate=dropoutRate)
self.rf = RecurrentLayer(self.ff3.output, 800, 500, False, parameters=params[3]) # Forward layer
self.rb = RecurrentLayer(self.ff3.output, 800, 500, True, parameters=params[4]) # Backward layer
# REVERSE THE BACKWARDS RECURRENT OUTPUTS IN TIME (from [T-1, 0] ===> [0, T-1]
self.s = SoftmaxLayer(T.concatenate((self.rf.output, self.rb.output[::-1, :]), axis=1), 2*500, self.output_dimensionality, parameters=params[5])
self.probabilities = theano.function(
inputs=[input_seq, dropoutRate],
outputs=[self.s.output],
allow_input_downcast=True
)
def dump(self, f_path):
f = file(f_path, 'wb')
for obj in [self.ff1.get_parameters(), self.ff2.get_parameters(), self.ff3.get_parameters(), self.rf.get_parameters(), self.rb.get_parameters(), self.s.get_parameters()]:
pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
class Network:
def __init__(self):
self.nn = None
def create_network(self, input_dimensionality, output_dimensionality, learning_rate=0.01, momentum=.25):
self.nn = BRNN(input_dimensionality, output_dimensionality, params=None, learning_rate=learning_rate, momentum=momentum)
return self.nn
def load_network(self, path, input_dimensionality, output_dimensionality, learning_rate=0.00001, momentum=.75):
f = file(path, 'rb')
parameters = []
for i in np.arange(6):
parameters.append(pickle.load(f))
f.close()
self.nn = BRNN(input_dimensionality, output_dimensionality, params=parameters, learning_rate=learning_rate, momentum=momentum)
return self.nn
def dump_network(self, path):
if self.nn is None:
return False
self.nn.dump(path)
def get_network(self):
assert(self.nn is not None)
return [self.nn.ff1.get_parameters(), self.nn.ff2.get_parameters(), self.nn.ff3.get_parameters(), self.nn.rf.get_parameters(), self.nn.rb.get_parameters(), self.nn.s.get_parameters()]
def set_network(self, parameters):
assert(type(parameters) == list)
assert(len(parameters) == 6)
self.nn.ff1.set_parameters(parameters[0])
self.nn.ff2.set_parameters(parameters[1])
self.nn.ff3.set_parameters(parameters[2])
self.nn.rf.set_parameters(parameters[3])
self.nn.rb.set_parameters(parameters[4])
self.nn.s.set_parameters(parameters[5])
def set_network_from_file(self, fname):
f = file(fname, 'rb')
parameters = []
for i in np.arange(6):
parameters.append(pickle.load(f))
f.close()
self.set_network(parameters)