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rename file, and avoid import *

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wassname
2018-12-29 14:43:19 +08:00
parent c414f91f59
commit d5c65b8f78
2 changed files with 83 additions and 102 deletions
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BitLit_param.py → BitLit_model_param.py
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poem_generator.py
+83 -102
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@@ -1,32 +1,57 @@
''' """
Voice to text to poem to speech Voice to text to poem to speech
Credits: Michel, Lauren, Thomas Credits: Michel, Lauren, Thomas
''' """
import sys import sys
from gtts import gTTS ## Packages for Text to voice from gtts import gTTS ## Packages for Text to voice
import os import os
import numpy as np import numpy as np
import speech_recognition as sr ## Packages for voice recognizer import speech_recognition as sr ## Packages for voice recognizer
os.environ['CUDA_VISIBLE_DEVICES']="" # To try without cuda
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import tensorflow as tf import tensorflow as tf
tf.enable_eager_execution() tf.enable_eager_execution()
from tensorflow.keras.layers import Embedding, GRU, Dense from tensorflow.keras.layers import Embedding, GRU, Dense
import re import re
from textblob import TextBlob from textblob import TextBlob
import random import random
from BitLit_param import * from BitLit_model_param import (
parameters_rhymes,
parameters_poems,
char2idx_poems,
units_poems,
embedding_dim_poems,
gru_weights_poems,
fc_weights_poems,
embedding_weights_poems,
embedding_weights_rhymes,
word2idx_rhymes,
fc_weights_rhymes,
gru_weights_rhymes,
units_rhymes,
idx2word_rhymes,
idx2char_poems,
)
# Architechture of the GRU # Architechture of the GRU
class Model(tf.keras.Model): class Model(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, units, batch_size): def __init__(self, vocab_size, embedding_dim, units, batch_size):
super(Model, self).__init__() super(Model, self).__init__()
self.units = units self.units = units
self.batch_sz = batch_size self.batch_sz = batch_size
self.embedding = Embedding(vocab_size, embedding_dim) 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.gru = GRU(
self.units,
return_sequences=True,
return_state=True,
recurrent_activation="sigmoid",
recurrent_initializer="glorot_uniform",
)
self.fc = Dense(vocab_size) self.fc = Dense(vocab_size)
def call(self, x, hidden): def call(self, x, hidden):
@@ -36,14 +61,15 @@ class Model(tf.keras.Model):
x = self.fc(output) x = self.fc(output)
return x, states return x, states
# Creation of the poem models and rhymes model # Creation of the poem models and rhymes model
model_poems = Model(vocab_size_poems, embedding_dim_poems, units_poems, BATCH_SIZE_poems) model_poems = Model(**parameters_poems)
model_rhymes = Model(vocab_size_rhymes, embedding_dim_rhymes, units_rhymes, BATCH_SIZE_rhymes) model_rhymes = Model(**parameters_rhymes)
# Set the weights for the poems model # Set the weights for the poems model
num_generate = 1 num_generate = 1
start_string = 'child'[::-1] start_string = "child"[::-1]
input_eval = [char2idx_poems[s] for s in start_string] input_eval = [char2idx_poems[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0) input_eval = tf.expand_dims(input_eval, 0)
hidden = [tf.zeros((1, units_poems))] hidden = [tf.zeros((1, units_poems))]
@@ -56,8 +82,10 @@ model_poems.fc.set_weights(fc_weights_poems)
# Set the weights for the rhymes model # Set the weights for the rhymes model
num_generate = 1 # number of characters to generate num_generate = 1 # number of characters to generate
start_string = ['fell'] # beginning of the generated text. TODO: try start_string = ' ' 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 = [
word2idx_rhymes[s] for s in start_string
] # converts start_string to numbers the model understands
input_eval = tf.expand_dims(input_eval, 0) input_eval = tf.expand_dims(input_eval, 0)
hidden = [tf.zeros((1, units_rhymes))] hidden = [tf.zeros((1, units_rhymes))]
predictions, hidden = model_rhymes(input_eval, hidden) predictions, hidden = model_rhymes(input_eval, hidden)
@@ -66,135 +94,88 @@ model_rhymes.embedding.set_weights(np.asarray(embedding_weights_rhymes))
model_rhymes.gru.set_weights(gru_weights_rhymes) model_rhymes.gru.set_weights(gru_weights_rhymes)
model_rhymes.fc.set_weights(fc_weights_rhymes) model_rhymes.fc.set_weights(fc_weights_rhymes)
def poem(USER_INPUT):
###https://pythonprogramminglanguage.com/text-to-speech/
#### cmd 1:::: sudo pip install gTTS
#### cmd 2:::: sudo pip install pyttsx
def poem(USER_INPUT):
#######################################################
##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 ### ML POEM PREDICTOR
########################### ###########################
# USER INPUT a line # # USER INPUT a line #
########################### ###########################
USER_INPUT = USER_INPUT.lower() USER_INPUT = USER_INPUT.lower()
USER_INPUT = re.sub('[^a-z\n]', ' ', USER_INPUT) USER_INPUT = re.sub("[^a-z\n]", " ", USER_INPUT)
text_generated = USER_INPUT[::-1] text_generated = USER_INPUT[::-1]
first_rhyme = USER_INPUT.split(' ')[-1] # Michel's magic first_rhyme = USER_INPUT.split(" ")[-1] # Michel's magic
###################### ######################
# RHYMES GENERATION # # RHYMES GENERATION #
###################### ######################
temperature = 0.09 temperature = 0.09
num_generate = 5 # number of characters to generate num_generate = 5 # number of characters to generate
if first_rhyme in idx2word_rhymes.values(): if first_rhyme in idx2word_rhymes.values():
start_string = [first_rhyme] start_string = [first_rhyme]
else: else:
start_string = [random.choice(list(word2idx_rhymes.keys()))] start_string = [random.choice(list(word2idx_rhymes.keys()))]
print('The word {} is not in our corpus of rhymes yet.'.format(first_rhyme)) 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 = [
input_eval = tf.expand_dims(input_eval, 0) 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 = [] rhymes = []
hidden = [tf.zeros((1, units_rhymes))] hidden = [tf.zeros((1, units_rhymes))]
for i in range(num_generate): 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, hidden = model_rhymes(
input_eval, hidden
predictions = predictions / temperature # alters characters' probabilities to be picked (but keeps the order) ) # predictions holds the probabily for each character to be most adequate continuation
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy() # picks the next character for the generated text
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) input_eval = tf.expand_dims([predicted_id], 0)
rhymes += [idx2word_rhymes[predicted_id]] rhymes += [idx2word_rhymes[predicted_id]]
print('rhymes:', rhymes) print("rhymes:", rhymes)
#################### ####################
# POEM GENERATION # # POEM GENERATION #
#################### ####################
temperature = 0.8 temperature = 0.8
text_generated = USER_INPUT text_generated = USER_INPUT
text_generated = text_generated[::-1] + '\n' text_generated = text_generated[::-1] + "\n"
num_generate = 150 num_generate = 150
for rhyme in rhymes: for rhyme in rhymes:
start_string = text_generated + rhyme[::-1] start_string = text_generated + rhyme[::-1]
input_eval = [char2idx_poems[s] for s in start_string] input_eval = [char2idx_poems[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0) input_eval = tf.expand_dims(input_eval, 0)
hidden = [tf.zeros((1, units_poems))] hidden = [tf.zeros((1, units_poems))]
b = True b = True
c = 1 c = 1
added_text = ' ' added_text = " "
while b == True: while b == True:
predictions, hidden = model_poems(input_eval, hidden) predictions, hidden = model_poems(input_eval, hidden)
predictions = predictions / temperature predictions = predictions / temperature
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy() predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][
0
].numpy()
input_eval = tf.expand_dims([predicted_id], 0) input_eval = tf.expand_dims([predicted_id], 0)
added_text += idx2char_poems[predicted_id] added_text += idx2char_poems[predicted_id]
c += 1 c += 1
if idx2char_poems[predicted_id] == '\n' or c > num_generate: if idx2char_poems[predicted_id] == "\n" or c > num_generate:
text_generated = rhyme[::-1] + added_text + text_generated text_generated = rhyme[::-1] + added_text + text_generated
b = False b = False
text_generated = text_generated[::-1] # That's the poem to return to the user in voice format text_generated = text_generated[
::-1
text_generated = re.sub(' +',' ',text_generated) ] # 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()) text_generated = str(TextBlob(text_generated).correct())
return text_generated return text_generated
#### 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()