-
Notifications
You must be signed in to change notification settings - Fork 4
/
speech_to_poem.py
250 lines (200 loc) · 8.91 KB
/
speech_to_poem.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
'''
Voice to text to poem to speech
Credits: Michel, Lauren, Thomas
'''
###https://pythonprogramminglanguage.com/text-to-speech/
#### cmd 1:::: sudo pip install gTTS
#### cmd 2:::: sudo pip install pyttsx
import sys
from gtts import gTTS ## Packages for Text to voice
import os
import speech_recognition as sr ## Packages for voice recognizer
if sys.version_info[0]==3:
from thinker import*
else:
import Tkinter as tk ## Packages for form
import tensorflow as tf
tf.enable_eager_execution()
from tensorflow.keras.layers import Embedding, GRU, Dense
import numpy as np
import re
from textblob import TextBlob
import random
#######################################################
##sys.path
##sys.path.append('/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python')
##sys.path.append('/Users/ShebMichel/Library/Python/2.7/lib/python/site-packages'
################################################################################
############ AUDIO CONVERSION TO TEST
r = sr.Recognizer()
with sr.Microphone() as source:
# tts = gTTS(text='HELLO! My Name is BIT-LIT. PLEASE SPEAK IN ABOUT 3 SECONDS.', lang='en')
# tts.save("hello.mp3")
# os.system("start hello.mp3")
# ######
print("SPEAK NOW-SPEAK NOW-SPEAK NOW:")
audio = r.listen(source)
tts = gTTS(text='THANK YOU! GIVE ME A SECOND TO READ OUT YOUR POEM', lang='en')
tts.save("thanks.mp3")
os.system("start thanks.mp3")
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)
AA0=r.recognize_google(audio)
USER_INPUT=AA0
print("You said: " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Could not understand audio")
except sr.RequestError as e:
print("Could not request results; {0}".format(e))
#################################################################################
### ML POEM PREDICTOR
#####################
# BACKGROUND STUFF #
#####################
'''
Each time we run the script, we load the parameters and set the weights.
This is inefficient. Is there a way to run the background stuff only once ? (lines 60 to 140)
'''
# Load the poems model parameters (hyperparameters and weights)
parameters_poems = np.load('model_poems.npy')[()]
embedding_weights_poems = parameters_poems['embedding_weights']
gru_weights_poems = parameters_poems['gru_weights']
fc_weights_poems = parameters_poems['fc_weights']
char2idx_poems = parameters_poems['char2idx']
idx2char_poems = parameters_poems['idx2char']
max_length_poems = parameters_poems['max_length']
embedding_dim_poems = parameters_poems['embedding_dim']
units_poems = parameters_poems['units']
BATCH_SIZE_poems = parameters_poems['BATCH_SIZE']
BUFFER_SIZE_poems = parameters_poems['BUFFER_SIZE']
vocab_size_poems = len(dict(idx2char_poems))
# Load hyperparameters and layers' weights previously saved
parameters_rhymes = np.load('model_rhymes.npy')[()]
embedding_weights_rhymes = parameters_rhymes['embedding_weights']
gru_weights_rhymes = parameters_rhymes['gru_weights']
fc_weights_rhymes = parameters_rhymes['fc_weights']
word2idx_rhymes = parameters_rhymes['word2idx']
idx2word_rhymes = parameters_rhymes['idx2word']
max_length_rhymes = parameters_rhymes['max_length']
embedding_dim_rhymes = parameters_rhymes['embedding_dim']
units_rhymes = parameters_rhymes['units']
BATCH_SIZE_rhymes = parameters_rhymes['BATCH_SIZE']
BUFFER_SIZE_rhymes = parameters_rhymes['BUFFER_SIZE']
vocab_size_rhymes = len(dict(idx2word_rhymes))
# Architechture of the GRU
class Model(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, units, batch_size):
super(Model, self).__init__()
self.units = units
self.batch_sz = batch_size
self.embedding = Embedding(vocab_size, embedding_dim)
self.gru = GRU(self.units, return_sequences=True, return_state=True, recurrent_activation='sigmoid', recurrent_initializer='glorot_uniform')
self.fc = Dense(vocab_size)
def call(self, x, hidden):
x = self.embedding(x)
output, states = self.gru(x, initial_state=hidden)
output = tf.reshape(output, (-1, output.shape[2]))
x = self.fc(output)
return x, states
# Creation of the poem models and rhymes model
model_poems = Model(vocab_size_poems, embedding_dim_poems, units_poems, BATCH_SIZE_poems)
model_rhymes = Model(vocab_size_rhymes, embedding_dim_rhymes, units_rhymes, BATCH_SIZE_rhymes)
# Set the weights for the poems model
num_generate = 1
start_string = 'child'[::-1]
input_eval = [char2idx_poems[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
hidden = [tf.zeros((1, units_poems))]
predictions, hidden = model_poems(input_eval, hidden)
model_poems.embedding.set_weights(np.asarray(embedding_weights_poems))
model_poems.gru.set_weights(gru_weights_poems)
model_poems.fc.set_weights(fc_weights_poems)
# Set the weights for the rhymes model
num_generate = 1 # number of characters to generate
start_string = ['fell'] # beginning of the generated text. TODO: try start_string = ' '
input_eval = [word2idx_rhymes[s] for s in start_string] # converts start_string to numbers the model understands
input_eval = tf.expand_dims(input_eval, 0)
hidden = [tf.zeros((1, units_rhymes))]
predictions, hidden = model_rhymes(input_eval, hidden)
model_rhymes.embedding.set_weights(np.asarray(embedding_weights_rhymes))
model_rhymes.gru.set_weights(gru_weights_rhymes)
model_rhymes.fc.set_weights(fc_weights_rhymes)
'''
End of the background thingy
'''
###########################
# USER INPUT a line #
###########################
USER_INPUT = USER_INPUT.lower()
USER_INPUT = re.sub('[^a-z\n]', ' ', USER_INPUT)
text_generated = USER_INPUT[::-1]
first_rhyme = USER_INPUT.split(' ')[-1] # Michel's magic
######################
# RHYMES GENERATION #
######################
temperature = 0.09
num_generate = 5 # number of characters to generate
if first_rhyme in idx2word_rhymes.values():
start_string = [first_rhyme]
else:
start_string = [random.choice(list(word2idx_rhymes.keys()))]
print('The word {} is not in our corpus of rhymes yet.'.format(first_rhyme))
input_eval = [word2idx_rhymes[s] for s in start_string] # converts start_string to numbers the model understands
input_eval = tf.expand_dims(input_eval, 0)
rhymes = []
hidden = [tf.zeros((1, units_rhymes))]
for i in range(num_generate):
predictions, hidden = model_rhymes(input_eval, hidden) # predictions holds the probabily for each character to be most adequate continuation
predictions = predictions / temperature # alters characters' probabilities to be picked (but keeps the order)
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy() # picks the next character for the generated text
input_eval = tf.expand_dims([predicted_id], 0)
rhymes += [idx2word_rhymes[predicted_id]]
print('rhymes:', rhymes)
####################
# POEM GENERATION #
####################
temperature = 0.8
text_generated = USER_INPUT
text_generated = text_generated[::-1] + '\n'
num_generate = 150
for rhyme in rhymes:
start_string = text_generated + rhyme[::-1]
input_eval = [char2idx_poems[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
hidden = [tf.zeros((1, units_poems))]
b = True
c = 1
added_text = ' '
while b == True:
predictions, hidden = model_poems(input_eval, hidden)
predictions = predictions / temperature
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()
input_eval = tf.expand_dims([predicted_id], 0)
added_text += idx2char_poems[predicted_id]
c += 1
if idx2char_poems[predicted_id] == '\n' or c > num_generate:
text_generated = rhyme[::-1] + added_text + text_generated
b = False
text_generated = text_generated[::-1] # That's the poem to return to the user in voice format
text_generated = re.sub(' +',' ',text_generated)
text_generated = str(TextBlob(text_generated).correct())
#### END CODE
#########################################################
################# TEXT CONVERSION IN AUDIO
################# FEED POEM TO TRANSCRIBER
print('ML POEM is:', text_generated)
tts = gTTS(text=text_generated, lang='en')
tts.save("poem.mp3")
os.system("start poem.mp3")
#########################################################
####
print("BIT-LIT ENDING STATEMENT:")
tts = gTTS(text='THANK YOU! CHECK ME OUT IN THE NEWS SOON.', lang='en')
tts.save("goodbye.mp3")
#os.system("start goodbye.mp3")
### USING JUPITER
# import IPython.display as ipd
# ipd.Audio(filename='path/to/file.mp3')
#tk.mainloop()