requirements-linux.txt

@@ -41,7 +41,6 @@ partd==0.3.8
 pexpect==4.2.1
 pickleshare==0.7.4
 pkg-resources==0.0.0
-praat-parselmouth==0.2.0
 progressbar2==3.34.3
 prompt-toolkit==1.0.15
 protobuf==3.4.0

speech_data.py

@@ -254,7 +254,7 @@ if __name__ == '__main__':
     # create_spectrogram_tfrecords('story_all',sample_count=25)
     # fix_csv('story_words_test')
     #fix_csv('story_phrases')
-    create_spectrogram_tfrecords('story_phrases',sample_count=500,train_test_ratio=0.1)
+    create_spectrogram_tfrecords('story_phrases',sample_count=100,train_test_ratio=0.1)
     # create_spectrogram_tfrecords('audio',sample_count=50)
     # read_siamese_tfrecords_generator('audio')
     # padd_zeros_siamese_tfrecords('audio')

speech_model.py

@@ -18,9 +18,9 @@ def create_base_rnn_network(input_dim):
     inp = Input(shape=input_dim)
     # ls0 = LSTM(512, return_sequences=True)(inp)
     ls1 = Bidirectional(LSTM(128, return_sequences=True))(inp)
-    #ls2 = LSTM(128, return_sequences=True)(ls1)
+    ls2 = LSTM(128, return_sequences=True)(ls1)
     # ls3 = LSTM(32, return_sequences=True)(ls2)
-    ls4 = LSTM(64)(ls1)
+    ls4 = LSTM(64)(ls2)
     # d1 = Dense(128, activation='relu')(ls4)
     #d2 = Dense(64, activation='relu')(ls2)
     return Model(inp, ls4)
@@ -75,7 +75,7 @@ def train_siamese(audio_group = 'audio'):
     log_dir = './logs/'+audio_group
     create_dir(log_dir)
     tr_gen_fn,te_pairs,te_y,copy_read_consts = read_siamese_tfrecords_generator(audio_group,batch_size=batch_size,test_size=batch_size)
-    n_step,n_features,n_records = copy_read_consts(model_dir)
+    n_step,n_features,n_records = copy_read_consts()
     tr_gen = tr_gen_fn()
     input_dim = (n_step, n_features)
 

speech_similar.py

@@ -1,141 +0,0 @@
-import pandas as pd
-import pronouncing
-import re
-import numpy as np
-import random
-
-# mapping = {
-#     s.split()[0]: s.split()[1]
-#     for s in """
-# AA AA
-# AE AE
-# AH UX
-# AO AO
-# AW AW
-# AY AY
-# B  b
-# CH C
-# D  d
-# DH D
-# EH EH
-# ER UXr
-# EY EY
-# F  f
-# G  g
-# HH h
-# IH IH
-# IY IY
-# JH J
-# K  k
-# L  l
-# M  m
-# N  n
-# NG N
-# OW OW
-# OY OY
-# P  p
-# R  r
-# S  s
-# SH S
-# T  t
-# TH T
-# UH UH
-# UW UW
-# V  v
-# W  w
-# Y  y
-# Z  z
-# ZH Z
-# """.strip().split('\n')
-# }
-
-# sim_mat = pd.read_csv('./similarity.csv', header=0, index_col=0)
-#
-#
-# def convert_ph(ph):
-#     stress_level = re.search("(\w+)([0-9])", ph)
-#     if stress_level:
-#         return stress_level.group(2) + mapping[stress_level.group(1)]
-#     else:
-#         return mapping[ph]
-#
-#
-# def sim_mat_to_apple_table(smt):
-#     colnames = [convert_ph(ph) for ph in smt.index.tolist()]
-#     smt = pd.DataFrame(np.nan_to_num(smt.values))
-#     fsmt = (smt.T + smt)
-#     np.fill_diagonal(fsmt.values, 100.0)
-#     asmt = pd.DataFrame.copy(fsmt)
-#     asmt.columns = colnames
-#     asmt.index = colnames
-#     apple_sim_table = asmt.stack().reset_index()
-#     apple_sim_table.columns = ['q', 'r', 's']
-#     return apple_sim_table
-#
-#
-# apple_sim_table = sim_mat_to_apple_table(sim_mat)
-#
-#
-# def top_match(ph):
-#     selected = apple_sim_table[(apple_sim_table.q == ph)
-#                                & (apple_sim_table.s < 100) &
-#                                (apple_sim_table.s >= 70)]
-#     tm = ph
-#     if len(selected) > 0:
-#         tm = pd.DataFrame.sort_values(selected, 's', ascending=False).iloc[0].r
-#     return tm
-
-
-apple_phonemes = [
-    '%', '@', 'AE', 'EY', 'AO', 'AX', 'IY', 'EH', 'IH', 'AY', 'IX', 'AA', 'UW',
-    'UH', 'UX', 'OW', 'AW', 'OY', 'b', 'C', 'd', 'D', 'f', 'g', 'h', 'J', 'k',
-    'l', 'm', 'n', 'N', 'p', 'r', 's', 'S', 't', 'T', 'v', 'w', 'y', 'z', 'Z'
-]
-
-class ApplePhoneme(object):
-    """docstring for ApplePhoneme."""
-
-    def __init__(self, phone, stress, vowel=False):
-        super(ApplePhoneme, self).__init__()
-        self.phone = phone
-        self.stress = stress
-        self.vowel = vowel
-
-    def __str__(self):
-        return (str(self.stress) if (self.vowel and self.stress>0) else '') + self.phone
-
-    def __repr__(self):
-        return "'{}'".format(str(self))
-
-    def adjust_stress(self):
-        self.stress = random.choice([i for i in range(3) if i != self.stress])
-
-
-def parse_apple_phonemes(ph_str):
-    for i in range(len(ph_str)):
-        pref, rest = ph_str[:i + 1], ph_str[i + 1:]
-        if pref in apple_phonemes:
-            vowel = pref[0] in 'AEIOU'
-            return [ApplePhoneme(pref, 0, vowel)] + parse_apple_phonemes(rest)
-        elif pref[0].isdigit() and pref[1:] in apple_phonemes:
-            return [ApplePhoneme(pref[1:], int(pref[0]) , True)] + parse_apple_phonemes(rest)
-        elif not pref.isalnum():
-            return [ApplePhoneme(pref, 0, False)] + parse_apple_phonemes(rest)
-    return []
-
-
-def similar_phoneme_word(ph_str):
-    phons = parse_apple_phonemes(ph_str)
-    vowels = [i for i in phons if i.vowel]
-    random.choice(vowels).adjust_stress()
-    return ''.join([str(i) for i in phons])
-
-def similar_phoneme_phrase(ph_str):
-    return ' '.join([similar_phoneme_word(w) for w in ph_str.split()])
-
-def similar_word(word_str):
-    similar = pronouncing.rhymes(word_str)
-    return random.choice(similar) if len(similar) > 0 else word_str
-
-def similar_phrase(ph_str):
-    return ' '.join([similar_word(w) for w in ph_str.split()])

speech_tts_queue.py

@@ -1,73 +0,0 @@
-import objc
-from AppKit import *
-from Foundation import NSURL
-from PyObjCTools import AppHelper
-from time import time
-
-apple_phonemes = [
-    '%', '@', 'AE', 'EY', 'AO', 'AX', 'IY', 'EH', 'IH', 'AY', 'IX', 'AA', 'UW',
-    'UH', 'UX', 'OW', 'AW', 'OY', 'b', 'C', 'd', 'D', 'f', 'g', 'h', 'J', 'k',
-    'l', 'm', 'n', 'N', 'p', 'r', 's', 'S', 't', 'T', 'v', 'w', 'y', 'z', 'Z'
-]
-len(apple_phonemes)
-
-speech_phoneme_data = []
-
-class SpeechDelegate (NSObject):
-    def speechSynthesizer_willSpeakWord_ofString_(self, sender, word, text):
-        '''Called automatically when the application has launched'''
-        print("Speaking word {} in sentence {}".format(word,text))
-
-    def speechSynthesizer_willSpeakPhoneme_(self,sender,phoneme):
-        phon_ch = apple_phonemes[phoneme]
-        # print('first',speech_phoneme_data)
-        # prev_time = speech_phoneme_data[-1][1]
-        # print('prev_time',prev_time)
-        speech_phoneme_data.append((phon_ch,time()))
-        print("phoneme boundary for {} time {}".format(phon_ch,time()))
-        # NSApp().terminate_(self)
-
-    def speechSynthesizer_didFinishSpeaking_(self,synth,didFinishSpeaking):
-        speech_phoneme_data.append(('%',time()))
-        print("finished speaking time {}".format(time()))
-        diff_time = []
-        for i in range(len(speech_phoneme_data)-1):
-            dur = speech_phoneme_data[i+1][1] - speech_phoneme_data[i][1]
-            diff_time.append((speech_phoneme_data[i][0],dur))
-        print(diff_time)
-
-# del SpeechDelegate
-class Delegate (NSObject):
-    def applicationDidFinishLaunching_(self, aNotification):
-        '''Called automatically when the application has launched'''
-        print("Window, World!")
-
-    def windowWillClose_(self, aNotification):
-        '''Called automatically when the window is closed'''
-        print("Window has been closed")
-        # Terminate the application
-        NSApp().terminate_(self)
-
-
-def main():
-    speech_delg = SpeechDelegate.alloc().init()
-    speech_delg.speechSynthesizer_didFinishSpeaking_('t',True)
-    voices = NSSpeechSynthesizer.availableVoices()
-    identifier = voices[2]
-    time()
-    alex_voice = NSSpeechSynthesizer.alloc().initWithVoice_(identifier)
-    alex_voice.setDelegate_(speech_delg)
-    alex_voice.startSpeakingString_("This is a test for speech synthesis generation")
-    # Create a new application instance ...
-    a=NSApplication.sharedApplication()
-    # ... and create its delgate.  Note the use of the
-    # Objective C constructors below, because Delegate
-    # is a subcalss of an Objective C class, NSObject
-    delegate = Delegate.alloc().init()
-    # Tell the application which delegate object to use.
-    a.setDelegate_(delegate)
-
-    AppHelper.runEventLoop()
-
-if __name__ == '__main__':
-    main()