From 3b0e16d0eec9f9abb94a3fbcfe984fc51df4c42c Mon Sep 17 00:00:00 2001 From: Camilo Vasquez Date: Mon, 2 May 2022 14:04:39 -0500 Subject: [PATCH] tensorflow 2 support --- docs/environment.yml | 3 +- phonet/example.py | 13 +- phonet/phonclasses/pataka | 316 ------------------- phonet/phonclasses/patakapost.png | Bin 43240 -> 0 bytes phonet/phonclasses/sentence_all | 175 ---------- phonet/phonclasses/sentence_allpost.png | Bin 51638 -> 0 bytes phonet/phonclasses/sentence_nasal | 175 ---------- phonet/phonclasses/sentence_nasalpost.png | Bin 34674 -> 0 bytes phonet/phonclasses/sentence_strident | 175 ---------- phonet/phonclasses/sentence_stridentpost.png | Bin 37238 -> 0 bytes phonet/phonclasses2/pataka.csv | 316 ------------------- phonet/phonclasses2/sentence.csv | 175 ---------- phonet/phonet.py | 176 ++++------- phonet/post.png | Bin 301409 -> 0 bytes setup.py | 5 +- tests/test.py | 51 --- 16 files changed, 68 insertions(+), 1512 deletions(-) delete mode 100644 phonet/phonclasses/pataka delete mode 100644 phonet/phonclasses/patakapost.png delete mode 100644 phonet/phonclasses/sentence_all delete mode 100644 phonet/phonclasses/sentence_allpost.png delete mode 100644 phonet/phonclasses/sentence_nasal delete mode 100644 phonet/phonclasses/sentence_nasalpost.png delete mode 100644 phonet/phonclasses/sentence_strident delete mode 100644 phonet/phonclasses/sentence_stridentpost.png delete mode 100644 phonet/phonclasses2/pataka.csv delete mode 100644 phonet/phonclasses2/sentence.csv delete mode 100644 phonet/post.png delete mode 100644 tests/test.py diff --git a/docs/environment.yml b/docs/environment.yml index 0c3a49fa1..45718157d 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -5,9 +5,8 @@ channels: dependencies: - python==3.7 - - tensorflow==1.13.1 + - tensorflow==2.7.0 - pip: - - keras - pandas - pysptk - six diff --git a/phonet/example.py b/phonet/example.py index 00cecc204..3f65191b0 100644 --- a/phonet/example.py +++ b/phonet/example.py @@ -14,26 +14,29 @@ PATH=os.path.dirname(os.path.abspath(__file__)) + path_results=os.path.join(PATH, "examples") + if not os.path.exists(path_results): + os.makedirs(path_results) ## get the "stop" phonological posterior from a single file file_audio=PATH+"/audios/pataka.wav" - file_feat=PATH+"/phonclasses/pataka" + file_feat=path_results+"/pataka" phon=Phonet(["stop"]) phon.get_phon_wav(file_audio, file_feat, True) # get the "nasal" phonological posterior from a single file file_audio=PATH+"/audios/sentence.wav" - file_feat=PATH+"/phonclasses/sentence_nasal" + file_feat=path_results+"/sentence_nasal" phon=Phonet(["nasal"]) phon.get_phon_wav(file_audio, file_feat, True) # get the "strident" phonological posterior from a single file - file_feat=PATH+"/phonclasses/sentence_strident" + file_feat=path_results+"/sentence_strident" phon=Phonet(["strident"]) phon.get_phon_wav(file_audio, file_feat, True) # get "strident, nasal, and back" phonological posteriors from a single file - file_feat=PATH+"/phonclasses/sentence_all" + file_feat=path_results+"/sentence_all" phon=Phonet(["strident", "nasal", "back"]) phon.get_phon_wav(file_audio, file_feat, True) @@ -46,7 +49,7 @@ # get phonological posteriors from de audio files included in a directory directory=PATH+"/phonclasses/" phon=Phonet(["vocalic", "strident", "nasal", "back", "stop", "pause"]) - phon.get_phon_path(PATH+"/audios/", PATH+"/phonclasses2/") + phon.get_phon_path(PATH+"/audios/", path_results) ## get the PLLR features from an audio file phon=Phonet(["all"]) diff --git a/phonet/phonclasses/pataka b/phonet/phonclasses/pataka deleted file mode 100644 index bbe5cac44..000000000 --- a/phonet/phonclasses/pataka +++ /dev/null @@ -1,316 +0,0 @@ -,phoneme,stop,time -0,p,0.5402161,0.0 -1,p,0.6521192,0.01 -2,p,0.814767,0.02 -3,p,0.9467721,0.03 -4,p,0.96937054,0.04 -5,p,0.92899907,0.05 -6,p,0.7969514,0.06 -7,p,0.57442534,0.07 -8,p,0.24581347,0.08 -9,a,0.09913461,0.09 -10,a,0.047853928,0.1 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-173,0.037907712,0.0066896025,0.7028417,s,0.012801748,0.9578269,1.73,0.015462107 diff --git a/phonet/phonet.py b/phonet/phonet.py index d022758ac..c7f6f3d5e 100644 --- a/phonet/phonet.py +++ b/phonet/phonet.py @@ -15,16 +15,10 @@ import pandas as pd import matplotlib.pyplot as plt plt.rcParams["font.family"] = "Times New Roman" -from matplotlib.patches import Rectangle, Ellipse -from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import -from scipy.signal import resample_poly -from keras.layers import Input, BatchNormalization, Bidirectional, GRU, Permute, Dropout, Dense, TimeDistributed -from keras.utils import np_utils -from keras.models import Model -from keras import optimizers +from scipy.signal import resample_poly +from tensorflow import keras import gc -from keras import backend as K from matplotlib import cm try: from phonet.Phonological import Phonological @@ -61,7 +55,7 @@ def __init__(self, phonological_classes): self.nfilt=33 self.len_seq=40 self.names=self.Phon.get_list_phonological_keys() - self.num_labels=num_labels=[2 for j in range(len(self.names))] + self.num_labels=[2 for j in range(len(self.names))] self.nfeat=34 self.thrplot=0.7 self.nphonemes=len(self.phonemes) @@ -74,7 +68,6 @@ def __init__(self, phonological_classes): self.MU, self.STD=self.load_scaler() def load_model(self): - Models=[] input_size=(self.len_seq, self.nfeat) model_file=self.path+"/models/model.h5" Model=self.model(input_size) @@ -120,15 +113,15 @@ def modelp(self, input_size): :param input_size: size of input for the BGRU layers (number of features x sequence length). :returns: A Keras model of a 2-layer BGRU neural network. """ - input_data=Input(shape=(input_size)) + input_data=keras.layers.Input(shape=(input_size)) x=input_data - x=BatchNormalization()(x) - x=Bidirectional(GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) - x=Bidirectional(GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) - x = TimeDistributed(Dense(self.hidden_size, activation='relu'))(x) - x = TimeDistributed(Dense(self.nphonemes, activation='softmax'))(x) - modelGRU=Model(inputs=input_data, outputs=x) - opt=optimizers.Adam(lr=self.lr) + x=keras.layers.BatchNormalization()(x) + x=keras.layers.Bidirectional(keras.layers.GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) + x=keras.layers.Bidirectional(keras.layers.GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) + x = keras.layers.TimeDistributed(keras.layers.Dense(self.hidden_size, activation='relu'))(x) + x = keras.layers.TimeDistributed(keras.layers.Dense(self.nphonemes, activation='softmax'))(x) + modelGRU=keras.Model(inputs=input_data, outputs=x) + opt=keras.optimizers.Adam(learning_rate=self.lr) modelGRU.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['categorical_accuracy']) return modelGRU @@ -140,23 +133,23 @@ def model(self, input_size): :param input_size: size of input for the BGRU layers (number of features x sequence length). :returns: A Keras model of a 2-layer BGRU neural network. """ - input_data=Input(shape=(input_size)) + input_data=keras.layers.Input(shape=(input_size)) x=input_data - x=BatchNormalization()(x) - x=Bidirectional(GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) - x=Bidirectional(GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) - x=Dropout(0.2)(x) - x = TimeDistributed(Dense(self.hidden_size, activation='relu'))(x) - x=Dropout(0.2)(x) + x=keras.layers.BatchNormalization()(x) + x=keras.layers.Bidirectional(keras.layers.GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) + x=keras.layers.Bidirectional(keras.layers.GRU(self.GRU_size, recurrent_dropout=self.recurrent_droput_prob, return_sequences=True, reset_after=False))(x) + x=keras.layers.Dropout(0.2)(x) + x = keras.layers.TimeDistributed(keras.layers.Dense(self.hidden_size, activation='relu'))(x) + x=keras.layers.Dropout(0.2)(x) # multi-task xout=[] out=[] for j in range(len(self.names)): - xout.append(TimeDistributed(Dense(self.hidden_size, activation='relu'))(x)) - out.append(TimeDistributed(Dense(2, activation='softmax'), name=self.names[j])(xout[-1])) + xout.append(keras.layers.TimeDistributed(keras.layers.Dense(self.hidden_size, activation='relu'))(x)) + out.append(keras.layers.TimeDistributed(keras.layers.Dense(2, activation='softmax'), name=self.names[j])(xout[-1])) - modelGRU=Model(inputs=input_data, outputs=out) - opt=optimizers.Adam(lr=self.lr) + modelGRU=keras.Model(inputs=input_data, outputs=out) + opt=keras.optimizers.Adam(learning_rate=self.lr) alphas=list(np.ones(len(self.names))/len(self.names)) modelGRU.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['categorical_accuracy'], sample_weight_mode="temporal", loss_weights=alphas) return modelGRU @@ -234,7 +227,6 @@ def get_phon_wav(self, audio_file, feat_file="", plot_flag=True): fs, signal=read(audio_file) if fs!=16000: - #raise ValueError(str(fs)+" is not a valid sampling frequency") signal=resample_poly(signal, 16000, fs) fs=16000 feat=self.get_feat(signal,fs) @@ -282,47 +274,45 @@ def get_phon_wav(self, audio_file, feat_file="", plot_flag=True): dfa[problem]=self.mask_correction(post) if plot_flag: - n_plots=int(np.ceil(len(self.keys_val)/4)) - figsize=(6,int(n_plots*3)) - colors = cm.get_cmap('Accent', 5) - col_order=[0,1,2,3]*n_plots - plt.figure(figsize=figsize) + self.plot_phonological(feat_file, fs, signal, dfa, phonemes_list, t2) + + dfa=pd.DataFrame(dfa) + if len(feat_file)>0: + dfa.to_csv(feat_file, index=False) + gc.collect() + return dfa + + def plot_phonological(self, feat_file, fs, signal, dfa, phonemes_list, t2): + n_plots=int(np.ceil(len(self.keys_val)/4)) + figsize=(6,int(n_plots*3)) + colors = cm.get_cmap('Accent', 5) + col_order=[0,1,2,3]*n_plots + plt.figure(figsize=figsize) for l, problem in enumerate(self.keys_val): - df[problem]=dfa[problem] - - if plot_flag: - - if (l==0) or (l==4) or (l==8) or (l==12) or (l==16): - subp=int(l/4+1) - plt.subplot(n_plots,1, subp) - t=np.arange(len(signal))/fs - signal=signal-np.mean(signal) - plt.plot(t,signal/np.max(np.abs(signal)), color=colors.colors[4], alpha=0.5) - plt.grid() - - plt.plot(t2,df[problem], color=colors.colors[col_order[l]], label=problem, linewidth=2) - ini=t2[0] - for nu in range(1,len(phonemes_list)): - if phonemes_list[nu]!=phonemes_list[nu-1] or nu==len(phonemes_list)-1: - difft=t2[nu]-ini - plt.text(x=ini+difft/2, y=1, s=phonemes_list[nu-1], color="k", fontsize=10) - ini=t2[nu] - - plt.xlabel("Time (s)") - plt.ylabel("Phonological posteriors") - plt.legend(loc=8, ncol=2) + if (l==0) or (l==4) or (l==8) or (l==12) or (l==16): + subp=int(l/4+1) + plt.subplot(n_plots,1, subp) + t=np.arange(len(signal))/fs + signal=signal-np.mean(signal) + plt.plot(t,signal/np.max(np.abs(signal)), color=colors.colors[4], alpha=0.5) + plt.grid() - if plot_flag: - plt.tight_layout() - plt.savefig(feat_file+"post.png") - plt.show() + plt.plot(t2,dfa[problem], color=colors.colors[col_order[l]], label=problem, linewidth=2) + ini=t2[0] + for nu in range(1,len(phonemes_list)): + if phonemes_list[nu]!=phonemes_list[nu-1] or nu==len(phonemes_list)-1: + difft=t2[nu]-ini + plt.text(x=ini+difft/2, y=1, s=phonemes_list[nu-1], color="k", fontsize=10) + ini=t2[nu] - df2=pd.DataFrame(df) - if len(feat_file)>0: - df2.to_csv(feat_file) - gc.collect() - return df2 + plt.xlabel("Time (s)") + plt.ylabel("Phonological posteriors") + plt.legend(loc=8, ncol=2) + + plt.tight_layout() + plt.savefig(feat_file+"post.png") + plt.show() @@ -460,63 +450,11 @@ def get_PLLR(self, audio_file, feat_file="", projected=True, plot_flag=False): P=I-Ones.T*Ones PLLRp=np.matmul(PLLR,P) - if plot_flag: - figsize=(10,3) - - fig=plt.figure(figsize=figsize) - - ax = fig.add_subplot(131, projection='3d') - indexes=np.random.randint(0,PLLR.shape[1], 3) - ax.scatter(post[:,indexes[0]],post[:,indexes[1]], post[:,indexes[2]], c='b', alpha=0.2, s=20) - ax.set_xlabel(self.keys_val[indexes[0]]) - ax.set_ylabel(self.keys_val[indexes[1]]) - ax.set_zlabel(self.keys_val[indexes[2]]) - plt.title("Posteriors") - ax = fig.add_subplot(132, projection='3d') - ax.scatter(PLLR[:,indexes[0]],PLLR[:,indexes[1]],PLLR[:,indexes[2]], c='b', alpha=0.2, s=20) - ax.set_xlabel(self.keys_val[indexes[0]]) - ax.set_ylabel(self.keys_val[indexes[1]]) - ax.set_zlabel(self.keys_val[indexes[2]]) - plt.title("PLLR") - ax = fig.add_subplot(133, projection='3d') - ax.scatter(PLLRp[:,indexes[0]],PLLRp[:,indexes[1]],PLLRp[:,indexes[2]], c='b', alpha=0.2, s=20) - ax.set_xlabel(self.keys_val[indexes[0]]) - ax.set_ylabel(self.keys_val[indexes[1]]) - ax.set_zlabel(self.keys_val[indexes[2]]) - plt.title("Projected PLLR") - plt.tight_layout() - for l, problem in enumerate(self.keys_val): if projected: dfPLLR[problem]=PLLRp[:,l] else: dfPLLR[problem]=PLLR[:,l] - - if plot_flag: - ncols=2 - nrows=int(np.ceil(len(self.keys_val)/4)) - figsize=(6,int(nrows*4)) - colors = cm.get_cmap('Accent', 4) - colorsn=[0,1,2,3]*len(self.keys_val) - plt.figure(figsize=figsize) - for l, problem in enumerate(self.keys_val): - - if (l==0) or (l==4) or (l==8) or (l==12) or (l==16): - subp1=int(2*l/4+1) - subp2=int(2*l/4+2) - plt.subplot(nrows,ncols, subp1) - plt.hist(df[problem], color=colors.colors[colorsn[l]], label=problem, alpha=0.5) - if l==len(self.keys_val)-1: - plt.xlabel("Phonological posteriors") - plt.subplot(nrows,ncols, subp2) - plt.hist(dfPLLR[problem], color=colors.colors[colorsn[l]], label=problem, alpha=0.5) - if l==len(self.keys_val)-1: - plt.xlabel("PLLR") - plt.legend() - plt.tight_layout() - plt.grid() - plt.show() - dfPLLR=pd.DataFrame(dfPLLR) if len(feat_file)>0: dfPLLR.to_csv(feat_file) diff --git a/phonet/post.png b/phonet/post.png deleted file mode 100644 index db0134f3958716b59428388b1b37a8ea43bd8116..0000000000000000000000000000000000000000 GIT binary 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zMRc+RW}Z)XJGv6gEO1s=*IHM_0Zlxx2DNx5BGpTjRLl^xn)7(WF(FpGWdVzXjb{P@ zOqSi0>wv>~xHo$5e@A zq7v6xL&H#X8?hm{Ve6hp>k(iqINsgk2)e*1p_ DE4s|0 diff --git a/setup.py b/setup.py index 93c199871..db98f4185 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,6 @@ install_requires = [ 'tensorflow', - 'Keras', 'pandas', 'pysptk', 'six', @@ -21,12 +20,12 @@ setup( name='phonet', - version='0.3.5', + version='0.3.7', description='Compute phonological posteriors from speech signals using a deep learning scheme', author='J. C. Vasquez-Correa', author_email='juan.vasquez@fau.de', url='https://github.com/jcvasquezc/phonet', - download_url='https://github.com/jcvasquezc/phonet/archive/0.3.5.tar.gz', + download_url='https://github.com/jcvasquezc/phonet/archive/0.3.7.tar.gz', license='MIT', install_requires=install_requires, packages=['phonet'], diff --git a/tests/test.py b/tests/test.py deleted file mode 100644 index 7a862bdf0..000000000 --- a/tests/test.py +++ /dev/null @@ -1,51 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Feb 28 2019 -@author: J. C. Vasquez-Correa - Pattern recognition Lab, University of Erlangen-Nuremberg - Faculty of Engineering, University of Antioquia, - juan.vasquez@fau.de -""" - -import os -import sys - -PATH=os.path.dirname(os.path.abspath(__file__))+"/../" -sys.path.append(PATH) -from phonet import Phonet - - -if __name__=="__main__": - - - ## get the "stop" phonological posterior from a single file - file_audio=PATH+"/phonet/audios/pataka.wav" - file_feat=PATH+"/phonet/phonclasses/pataka" - phon=Phonet(["stop"]) - phon.get_phon_wav(file_audio, file_feat, False) - - # get the "nasal" phonological posterior from a single file - file_audio=PATH+"/phonet/audios/sentence.wav" - file_feat=PATH+"/phonet/phonclasses/sentence_nasal" - phon=Phonet(["nasal"]) - phon.get_phon_wav(file_audio, file_feat, False) - - # get the "strident" phonological posterior from a single file - file_feat=PATH+"/phonet/phonclasses/sentence_strident" - phon=Phonet(["strident"]) - phon.get_phon_wav(file_audio, file_feat, False) - - # get "strident, nasal, and back" phonological posteriors from a single file - file_feat=PATH+"/phonet/phonclasses/sentence_all" - phon=Phonet(["strident", "nasal", "back"]) - phon.get_phon_wav(file_audio, file_feat, False) - - - # get phonological posteriors from de audio files included in a directory - directory=PATH+"/phonet/phonclasses/" - phon=Phonet(["vocalic", "strident", "nasal", "back", "stop", "pause"]) - phon.get_phon_path(PATH+"/phonet/audios/", PATH+"/phonet/phonclasses2/") - - ## get the PLLR features from an audio file - phon=Phonet(["all"]) - PLLR=phon.get_PLLR(file_audio, plot_flag=False)