pandas_parallel.py

@@ -0,0 +1,25 @@
+import multiprocessing
+import pandas as pd
+import numpy as np
+
+
+
+def _apply_df(args):
+    df, func, num, kwargs = args
+    return num, df.apply(func, **kwargs)
+
+def apply_by_multiprocessing(df,func,**kwargs):
+  cores = multiprocessing.cpu_count()
+  workers=kwargs.pop('workers') if 'workers' in kwargs else cores
+  pool = multiprocessing.Pool(processes=workers)
+  result = pool.map(_apply_df, [(d, func, i, kwargs) for i,d in enumerate(np.array_split(df, workers))])
+  pool.close()
+  result=sorted(result,key=lambda x:x[0])
+  return pd.concat([i[1] for i in result])
+
+def square(x):
+    return x**x
+
+if __name__ == '__main__':
+    df = pd.DataFrame({'a':range(10), 'b':range(10)})
+    apply_by_multiprocessing(df, square, axis=1, workers=4)

record_mic_speech.py

@@ -1,36 +1,15 @@
 import pyaudio
-from pysndfile import sndio as snd
 import numpy as np
 # from matplotlib import pyplot as plt
-from speech_spectrum import plot_stft, generate_spec_frec
+from spectro_gen import plot_stft, generate_spec_frec
 
-SAMPLE_RATE = 22050
-N_CHANNELS = 2
-
-def file_player():
-    p_oup = pyaudio.PyAudio()
-    def play_file(audiopath,plot=False):
-        print('playing',audiopath)
-        samples, samplerate, form = snd.read(audiopath)
-        stream = p_oup.open(
-            format=pyaudio.paFloat32,
-            channels=2,
-            rate=samplerate,
-            output=True)
-        one_channel = np.asarray([samples, samples]).T.reshape(-1)
-        audio_data = one_channel.astype(np.float32).tobytes()
-        stream.write(audio_data)
-        stream.close()
-        if plot:
-            plot_stft(samples, SAMPLE_RATE)
-    def close_player():
-        p_oup.terminate()
-    return play_file,close_player
 
 def record_spectrogram(n_sec, plot=False, playback=False):
-    # show_record_prompt()
+    SAMPLE_RATE = 22050
+    N_CHANNELS = 2
     N_SEC = n_sec
     CHUNKSIZE = int(SAMPLE_RATE * N_SEC / N_CHANNELS)  # fixed chunk size
+    # show_record_prompt()
     input('Press [Enter] to start recording sample... ')
     p_inp = pyaudio.PyAudio()
     stream = p_inp.open(

requirements-linux.txt

@@ -1,76 +0,0 @@
-bleach==1.5.0
-click==6.7
-cloudpickle==0.4.1
-cycler==0.10.0
-dask==0.15.4
-decorator==4.1.2
-distributed==1.19.3
-entrypoints==0.2.3
-enum34==1.1.6
-futures==3.1.1
-h5py==2.7.1
-HeapDict==1.0.0
-html5lib==0.9999999
-ipykernel==4.6.1
-ipython==6.2.1
-ipython-genutils==0.2.0
-ipywidgets==7.0.3
-jedi==0.11.0
-Jinja2==2.9.6
-jsonschema==2.6.0
-jupyter==1.0.0
-jupyter-client==5.1.0
-jupyter-console==5.2.0
-jupyter-core==4.3.0
-Keras==2.0.8
-locket==0.2.0
-Markdown==2.6.9
-MarkupSafe==1.0
-matplotlib==2.1.0
-mistune==0.7.4
-msgpack-python==0.4.8
-nbconvert==5.3.1
-nbformat==4.4.0
-notebook==5.2.0
-numexpr==2.6.4
-numpy==1.13.3
-pandas==0.20.3
-pandocfilters==1.4.2
-parso==0.1.0
-partd==0.3.8
-pexpect==4.2.1
-pickleshare==0.7.4
-progressbar2==3.34.3
-prompt-toolkit==1.0.15
-protobuf==3.4.0
-psutil==5.4.0
-ptyprocess==0.5.2
-PyAudio==0.2.11
-Pygments==2.2.0
-pyparsing==2.2.0
-pysndfile==1.0.0
-python-dateutil==2.6.1
-python-utils==2.2.0
-pytz==2017.2
-PyYAML==3.12
-pyzmq==16.0.2
-qtconsole==4.3.1
-scikit-learn==0.19.0
-scipy==0.19.1
-simplegeneric==0.8.1
-six==1.11.0
-sortedcontainers==1.5.7
-tables==3.4.2
-tblib==1.3.2
-tensorflow==1.3.0
-tensorflow-tensorboard==0.4.0rc1
-terminado==0.6
-testpath==0.3.1
-toolz==0.8.2
-tornado==4.5.2
-tqdm==4.19.4
-traitlets==4.3.2
-wcwidth==0.1.7
-Werkzeug==0.12.2
-widgetsnbextension==3.0.6
-zict==0.1.3

speech_data.py

@@ -1,32 +1,114 @@
 import pandas as pd
-from speech_utils import apply_by_multiprocessing
+from pandas_parallel import apply_by_multiprocessing
-from speech_utils import threadsafe_iter
 # import dask as dd
 # import dask.dataframe as ddf
 import tensorflow as tf
-from tensorflow.python.ops import data_flow_ops
 import numpy as np
-from speech_spectrum import generate_aiff_spectrogram
+from spectro_gen import generate_aiff_spectrogram
 from sklearn.model_selection import train_test_split
 import itertools
 import os
 import random
 import csv
 import gc
-import pickle
-from tqdm import tqdm
 
+def get_siamese_pairs(groupF1, groupF2):
+    group1 = [r for (i, r) in groupF1.iterrows()]
+    group2 = [r for (i, r) in groupF2.iterrows()]
+    diff = [(g1, g2) for g2 in group2 for g1 in group1]
+    same = [i for i in itertools.combinations(group1, 2)
+         ] + [i for i in itertools.combinations(group2, 2)]
+    random.shuffle(same)
+    random.shuffle(diff)
+    # return (random.sample(same,10), random.sample(diff,10))
+    return same[:10],diff[:10]
 
 def siamese_pairs(rightGroup, wrongGroup):
     group1 = [r for (i, r) in rightGroup.iterrows()]
     group2 = [r for (i, r) in wrongGroup.iterrows()]
     rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]
-    rightRightPairs = [i for i in itertools.combinations(group1, 2)]#+[i for i in itertools.combinations(group2, 2)]
+    rightRightPairs = [i for i in itertools.combinations(group1, 2)]
-    # random.shuffle(rightWrongPairs)
+    random.shuffle(rightWrongPairs)
-    # random.shuffle(rightRightPairs)
+    random.shuffle(rightRightPairs)
-    # return rightRightPairs[:10],rightWrongPairs[:10]
+    # return (random.sample(same,10), random.sample(diff,10))
-    return rightRightPairs[:32],rightWrongPairs[:32]
+    return rightRightPairs[:10],rightWrongPairs[:10]
 
+def append_zeros(spgr, max_samples):
+    return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
+                      'median')
+
+def padd_zeros(spgr, max_samples):
+    return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
+                      'constant')
+
+def to_onehot(a,class_count=2):
+    # >>> a = np.array([1, 0, 3])
+    a_row_n = a.shape[0]
+    b = np.zeros((a_row_n, class_count))
+    b[np.arange(a_row_n), a] = 1
+    return b
+
+def create_pair(l, r, max_samples):
+    l_sample = padd_zeros(l, max_samples)
+    r_sample = padd_zeros(r, max_samples)
+    return np.asarray([l_sample, r_sample])
+
+
+def create_test_pair(l, r, max_samples):
+    l_sample = append_zeros(l, max_samples)
+    r_sample = append_zeros(r, max_samples)
+    return np.asarray([[l_sample, r_sample]])
+
+
+def create_X(sp, max_samples):
+    return create_pair(sp[0]['spectrogram'], sp[1]['spectrogram'], max_samples)
+
+
+# def get_word_pairs_data(word, max_samples):
+#     audio_samples = pd.read_csv(
+#         './outputs/audio.csv',
+#         names=['word', 'voice', 'rate', 'variant', 'file'])
+#     audio_samples = audio_samples.loc[audio_samples['word'] ==
+#                                       word].reset_index(drop=True)
+#     audio_samples.loc[:, 'spectrogram'] = audio_samples.loc[:, 'file'].apply(
+#         lambda x: 'outputs/audio/' + x).apply(generate_aiff_spectrogram)
+#     max_samples = audio_samples['spectrogram'].apply(
+#         lambda x: x.shape[0]).max()
+#     same_data, diff_data = [], []
+#     for (w, g) in audio_samples.groupby(audio_samples['word']):
+#         sample_norm = g.loc[audio_samples['variant'] == 'normal']
+#         sample_phon = g.loc[audio_samples['variant'] == 'phoneme']
+#         same, diff = get_siamese_pairs(sample_norm, sample_phon)
+#         same_data.extend([create_X(s, max_samples) for s in same])
+#         diff_data.extend([create_X(d, max_samples) for d in diff])
+#     Y = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
+#     X = np.asarray(same_data + diff_data)
+#     # tr_pairs, te_pairs, tr_y, te_y = train_test_split(X, Y, test_size=0.1)
+#     return (X, Y)
+
+
+def create_spectrogram_data(audio_group='audio'):
+    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
+        , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
+        , quoting=csv.QUOTE_NONE)
+    # audio_samples = audio_samples.loc[audio_samples['word'] ==
+    #                                   'sunflowers'].reset_index(drop=True)
+    audio_samples['file_paths'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
+    audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_paths'], os.path.exists)
+    audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
+    audio_samples['spectrogram'] = apply_by_multiprocessing(audio_samples['file_paths'],generate_aiff_spectrogram)#.apply(
+    audio_samples['window_count'] = audio_samples.loc[:,'spectrogram'].apply(lambda x: x.shape[0])
+    audio_samples.to_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
+
+def create_spectrogram_tfrecords(audio_group='audio'):
+    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
+        , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
+        , quoting=csv.QUOTE_NONE)
+    # audio_samples = audio_samples.loc[audio_samples['word'] ==
+    #                                   'sunflowers'].reset_index(drop=True)
+    audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
+    audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
+    audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
 
     def _float_feature(value):
       return tf.train.Feature(float_list=tf.train.FloatList(value=value))
@@ -37,48 +119,21 @@ def _int64_feature(value):
     def _bytes_feature(value):
       return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
 
-def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_ratio=0.1):
+    writer = tf.python_io.TFRecordWriter('./outputs/' + audio_group + '.tfrecords')
-    '''
+    # audio_samples = audio_samples[:100]
-    http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
+    for (w, word_group) in audio_samples.groupby(audio_samples['word']):
-    http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html
-    '''
-    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv',index_col=0)
-    audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
-    n_records,n_spec,n_features = 0,0,0
-
-    def write_samples(wg,sample_name):
-        word_group_prog = tqdm(wg,desc='Computing spectrogram')
-        record_file = './outputs/{}.{}.tfrecords'.format(audio_group,sample_name)
-        writer = tf.python_io.TFRecordWriter(record_file)
-        for (w, word_group) in word_group_prog:
-            word_group_prog.set_postfix(word=w,sample_name=sample_name)
         g = word_group.reset_index()
         g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
-            sample_right = g.loc[g['variant'] == 'low']
+        sample_right = g.loc[audio_samples['variant'] == 'low']
-            sample_wrong = g.loc[g['variant'] == 'medium']
+        sample_wrong = g.loc[audio_samples['variant'] == 'medium']
         same, diff = siamese_pairs(sample_right, sample_wrong)
         groups = [([0,1],same),([1,0],diff)]
         for (output,group) in groups:
-                group_prog = tqdm(group,desc='Writing Spectrogram')
+            for sample1,sample2 in group:
-                for sample1,sample2 in group_prog:
-                    same = sample1['variant'] == sample2['variant']
-                    phon_same = sample1['phonemes'] == sample2['phonemes']
-                    voice_diff = sample1['voice'] != sample2['voice']
-                    if not same and phon_same:
-                        continue
-                    if same and not voice_diff:
-                        continue
-                    group_prog.set_postfix(output=output
-                        ,var1=sample1['variant']
-                        ,var2=sample2['variant'])
                 spectro1,spectro2 = sample1['spectrogram'],sample2['spectrogram']
                 spec_n1,spec_n2 = spectro1.shape[0],spectro2.shape[0]
                 spec_w1,spec_w2 = spectro1.shape[1],spectro2.shape[1]
                 spec1,spec2 = spectro1.reshape(-1),spectro2.reshape(-1)
-                    nonlocal n_spec,n_records,n_features
-                    n_spec = max([n_spec,spec_n1,spec_n2])
-                    n_features = spec_w1
-                    n_records+=1
                 example = tf.train.Example(features=tf.train.Features(
                   feature={
                     'word': _bytes_feature([w.encode('utf-8')]),
@@ -103,144 +158,63 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_r
                   }
                 ))
                 writer.write(example.SerializeToString())
-                group_prog.close()
-        word_group_prog.close()
     writer.close()
 
-    word_groups = [i for i in audio_samples.groupby('word')]
+def create_tagged_data(audio_samples):
-    wg_sampled = reservoir_sample(word_groups,sample_count) if sample_count > 0 else word_groups
+    same_data, diff_data = [], []
-    tr_audio_samples,te_audio_samples = train_test_split(wg_sampled,test_size=train_test_ratio)
+    for (w, g) in audio_samples.groupby(audio_samples['word']):
-    write_samples(tr_audio_samples,'train')
+        # sample_norm = g.loc[audio_samples['variant'] == 'low']
-    write_samples(te_audio_samples,'test')
+        # sample_phon = g.loc[audio_samples['variant'] == 'medium']
-    const_file = os.path.join('./outputs',audio_group+'.constants')
+        sample_norm = g.loc[audio_samples['variant'] == 'normal']
-    pickle.dump((n_spec,n_features,n_records),open(const_file,'wb'))
+        sample_phon = g.loc[audio_samples['variant'] == 'phoneme']
+        same, diff = get_siamese_pairs(sample_norm, sample_phon)
+        same_data.extend([create_X(s) for s in same])
+        diff_data.extend([create_X(d) for d in diff])
+    print('creating all speech pairs')
+    Y_f = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
+    Y = to_onehot(Y_f.astype(np.int8))
+    print('casting as array speech pairs')
+    X = np.asarray(same_data + diff_data)
+    return X,Y
 
-def padd_zeros(spgr, max_samples):
+def create_speech_pairs_data(audio_group='audio'):
-    return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
+    audio_samples = pd.read_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
-                      'constant')
+    # sample_size = audio_samples['spectrogram'][0].shape[1]
+    tr_audio_samples,te_audio_samples = train_test_split(audio_samples, test_size=0.1)
+    def save_samples_for(sample_name,samples):
+        print('generating {} siamese speech pairs'.format(sample_name))
+        X,Y = create_tagged_data(samples)
+        print('shuffling array speech pairs')
+        rng_state = np.random.get_state()
+        np.random.shuffle(X)
+        np.random.set_state(rng_state)
+        np.random.shuffle(Y)
+        print('pickling X/Y')
+        np.save('outputs/{}-train-X.npy'.format(audio_group), X)
+        np.save('outputs/{}-train-Y.npy'.format(audio_group), Y)
+    save_samples_for('train',tr_audio_samples)
+    save_samples_for('test',te_audio_samples)
 
-def reservoir_sample(iterable, k):
+def speech_data(audio_group='audio'):
-    it = iter(iterable)
+    X = np.load('outputs/{}-X.npy'.format(audio_group)) / 255.0
-    if not (k > 0):
+    Y = np.load('outputs/{}-Y.npy'.format(audio_group))
-        raise ValueError("sample size must be positive")
+    return (X,Y)
 
-    sample = list(itertools.islice(it, k)) # fill the reservoir
+def speech_model_data():
-    random.shuffle(sample) # if number of items less then *k* then
+    tr_pairs = np.load('outputs/tr_pairs.npy') / 255.0
-                           #   return all items in random order.
+    te_pairs = np.load('outputs/te_pairs.npy') / 255.0
-    for i, item in enumerate(it, start=k+1):
+    tr_pairs[tr_pairs < 0] = 0
-        j = random.randrange(i) # random [0..i)
+    te_pairs[te_pairs < 0] = 0
-        if j < k:
+    tr_y = np.load('outputs/tr_y.npy')
-            sample[j] = item # replace item with gradually decreasing probability
+    te_y = np.load('outputs/te_y.npy')
-    return sample
+    return tr_pairs, te_pairs, tr_y, te_y
 
 
-def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size=0):
-    records_file  = os.path.join('./outputs',audio_group+'.train.tfrecords')
-    input_pairs = []
-    output_class = []
-    const_file = os.path.join('./outputs',audio_group+'.constants')
-    (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
-    print('reading tfrecords({}-train)...'.format(audio_group))
-
-    # @threadsafe_iter
-    def record_generator():
-        input_data = []
-        output_data = []
-        while True:
-            record_iterator = tf.python_io.tf_record_iterator(path=records_file)
-            #tqdm(enumerate(record_iterator),total=n_records)
-            for (i,string_record) in enumerate(record_iterator):
-                example = tf.train.Example()
-                example.ParseFromString(string_record)
-                spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
-                spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
-                spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
-                spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
-                spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
-                spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
-                p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
-                input_data.append(np.asarray([p_spec1,p_spec2]))
-                output = example.features.feature['output'].int64_list.value
-                output_data.append(np.asarray(output))
-                if len(input_data) == batch_size:
-                    input_arr = np.asarray(input_data)
-                    output_arr = np.asarray(output_data)
-                    yield ([input_arr[:, 0], input_arr[:, 1]],output_arr)
-                    input_data = []
-                    output_data = []
-
-    # Read test in one-shot
-    te_records_file  = os.path.join('./outputs',audio_group+'.test.tfrecords')
-    te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
-    te_n_records = len([i for i in te_re_iterator])
-    te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
-    print('reading tfrecords({}-test)...'.format(audio_group))
-    test_size = min([test_size,te_n_records]) if test_size > 0 else te_n_records
-    input_data = np.zeros((test_size,2,n_spec,n_features))
-    output_data = np.zeros((test_size,2))
-    random_samples = enumerate(reservoir_sample(te_re_iterator,test_size))
-    for (i,string_record) in tqdm(random_samples,total=test_size):
-        example = tf.train.Example()
-        example.ParseFromString(string_record)
-        spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
-        spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
-        spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
-        spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
-        spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
-        spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
-        p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
-        input_data[i] = np.asarray([p_spec1,p_spec2])
-        output = example.features.feature['output'].int64_list.value
-        output_data[i] = np.asarray(output)
-
-    return record_generator,input_data,output_data,n_spec,n_features,n_records
-
-def audio_samples_word_count(audio_group='audio'):
-    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv')
-    return len(audio_samples.groupby(audio_samples['word']))
-
-def fix_csv(audio_group='audio'):
-    audio_csv_lines = open('./outputs/' + audio_group + '.csv.orig','r').readlines()
-    audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
-    proper_rows = [i for i in audio_csv_data if len(i) == 7]
-    with open('./outputs/' + audio_group + '.csv','w') as fixed_csv:
-        fixed_csv_w = csv.writer(fixed_csv, quoting=csv.QUOTE_MINIMAL)
-        fixed_csv_w.writerows(proper_rows)
-    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
-        , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file'])
-    audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
-    audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
-    audio_samples = audio_samples[audio_samples['file_exists'] == True]
-    audio_samples = audio_samples.drop(['file_path','file_exists'],axis=1).reset_index(drop=True)
-    audio_samples.to_csv('./outputs/' + audio_group + '.csv')
-
-def convert_old_audio():
-    audio_samples = pd.read_csv( './outputs/audio.csv.old'
-        , names=['word', 'voice', 'rate', 'variant', 'file'])
-    audio_samples['phonemes'] = 'unknown'
-    audio_samples['language'] = 'en-US'
-    audio_samples.loc[audio_samples['variant'] == 'normal','variant'] = 'low'
-    audio_samples.loc[audio_samples['variant'] == 'phoneme','variant'] = 'medium'
-    audio_samples = audio_samples[['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']]
-    audio_samples.to_csv('./outputs/audio_new.csv',index=False,header=False)
-
 if __name__ == '__main__':
     # sunflower_pairs_data()
     # create_spectrogram_data()
     # create_spectrogram_data('story_words')
-    # create_spectrogram_tfrecords('story_words')
+    create_spectrogram_tfrecords('story_words')
-    # create_spectrogram_tfrecords('story_words_test')
-    # read_siamese_tfrecords('story_all')
-    # read_siamese_tfrecords('story_words_test')
-    # padd_zeros_siamese_tfrecords('story_words')
-    # fix_csv('story_words')
-    # pickle_constants('story_words')
-    # create_spectrogram_tfrecords('audio',sample_count=100)
-    # create_spectrogram_tfrecords('story_all',sample_count=25)
-    create_spectrogram_tfrecords('story_words',sample_count=10,train_test_ratio=0.2)
-    # create_spectrogram_tfrecords('audio',sample_count=50)
-    # read_siamese_tfrecords_generator('audio')
-    # padd_zeros_siamese_tfrecords('audio')
     # create_padded_spectrogram()
     # create_speech_pairs_data()
     # print(speech_model_data())

speech_siamese.py

@@ -1,8 +1,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-# from speech_data import speech_model_data
+from speech_data import speech_model_data
-from speech_data import read_siamese_tfrecords_generator
 from keras.models import Model,load_model
 from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate
 from keras.losses import categorical_crossentropy
@@ -12,44 +11,41 @@ from keras.utils import to_categorical
 from keras.optimizers import RMSprop
 from keras.callbacks import TensorBoard, ModelCheckpoint
 from keras import backend as K
-from speech_utils import create_dir
 
-# def euclidean_distance(vects):
+
-#     x, y = vects
+def euclidean_distance(vects):
-#     return K.sqrt(
+    x, y = vects
-#         K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
+    return K.sqrt(
-#
+        K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
-#
+
-# def eucl_dist_output_shape(shapes):
+
-#     shape1, shape2 = shapes
+def eucl_dist_output_shape(shapes):
-#     return (shape1[0], 1)
+    shape1, shape2 = shapes
-#
+    return (shape1[0], 1)
-#
+
-# def contrastive_loss(y_true, y_pred):
+
-#     '''Contrastive loss from Hadsell-et-al.'06
+def contrastive_loss(y_true, y_pred):
-#     http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
+    '''Contrastive loss from Hadsell-et-al.'06
-#     '''
+    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
-#     return K.mean(y_true * K.square(y_pred) +
+    '''
-#                   (1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
+    return K.mean(y_true * K.square(y_pred) +
+                  (1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
 
 def create_base_rnn_network(input_dim):
     '''Base network to be shared (eq. to feature extraction).
     '''
     inp = Input(shape=input_dim)
-    ls0 = LSTM(512, return_sequences=True)(inp)
+    ls1 = LSTM(256, return_sequences=True)(inp)
-    ls1 = LSTM(256, return_sequences=True)(ls0)
     ls2 = LSTM(128, return_sequences=True)(ls1)
     # ls3 = LSTM(32, return_sequences=True)(ls2)
     ls4 = LSTM(64)(ls2)
-    d1 = Dense(128, activation='relu')(ls4)
-    d2 = Dense(64, activation='relu')(d1)
     return Model(inp, ls4)
 
 
 def compute_accuracy(y_true, y_pred):
     '''Compute classification accuracy with a fixed threshold on distances.
     '''
-    pred = y_pred.ravel() > 0.5
+    pred = y_pred.ravel() < 0.5
     return np.mean(pred == y_true)
 
 
@@ -60,12 +56,11 @@ def accuracy(y_true, y_pred):
 
 def dense_classifier(processed):
     conc_proc = Concatenate()(processed)
-    d1 = Dense(64, activation='relu')(conc_proc)
+    d1 = Dense(16, activation='relu')(conc_proc)
     # dr1 = Dropout(0.1)(d1)
-    d2 = Dense(128, activation='relu')(d1)
+    d2 = Dense(8, activation='relu')(d1)
-    d3 = Dense(8, activation='relu')(d2)
     # dr2 = Dropout(0.1)(d2)
-    return Dense(2, activation='softmax')(d3)
+    return Dense(2, activation='softmax')(d2)
 
 def siamese_model(input_dim):
     # input_dim = (15, 1654)
@@ -83,24 +78,17 @@ def siamese_model(input_dim):
     return model
 
 
-def train_siamese(audio_group = 'audio'):
+def train_siamese():
     # the data, shuffled and split between train and test sets
-    # tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
+    tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
-    batch_size = 128
+    tr_y = to_categorical(tr_y_e, num_classes=2)
-    model_dir = './models/'+audio_group
+    te_y = to_categorical(te_y_e, num_classes=2)
-    create_dir(model_dir)
+    input_dim = (tr_pairs.shape[2], tr_pairs.shape[3])
-    log_dir = './logs/'+audio_group
-    create_dir(log_dir)
-    tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size=batch_size)
-    tr_gen = tr_gen_fn()
-    # tr_y = to_categorical(tr_y_e, num_classes=2)
-    # te_y = to_categorical(te_y_e, num_classes=2)
-    input_dim = (n_step, n_features)
 
     model = siamese_model(input_dim)
 
     tb_cb = TensorBoard(
-        log_dir=log_dir,
+        log_dir='./logs/siamese_logs',
         histogram_freq=1,
         batch_size=32,
         write_graph=True,
@@ -109,45 +97,39 @@ def train_siamese(audio_group = 'audio'):
         embeddings_freq=0,
         embeddings_layer_names=None,
         embeddings_metadata=None)
-    cp_file_fmt = model_dir+'/siamese_speech_model-{epoch:02d}-epoch-{val_loss:0.2f}\
+    cp_file_fmt = './models/siamese_speech_model-{epoch:02d}-epoch-{val_loss:0.2f}\
 -acc.h5'
 
     cp_cb = ModelCheckpoint(
         cp_file_fmt,
         monitor='val_loss',
         verbose=0,
-        save_best_only=True,
+        save_best_only=False,
-        save_weights_only=True,
+        save_weights_only=False,
         mode='auto',
         period=1)
     # train
-    rms = RMSprop()#lr=0.001
+    rms = RMSprop(lr=0.001)
     model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
-    # model.fit(
+    model.fit(
-    #     [tr_pairs[:, 0], tr_pairs[:, 1]],
+        [tr_pairs[:, 0], tr_pairs[:, 1]],
-    #     tr_y,
+        tr_y,
-    #     batch_size=128,
+        batch_size=128,
-    #     epochs=100,
+        epochs=50,
-    #     validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
+        validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
-    #     callbacks=[tb_cb, cp_cb])
+        callbacks=[tb_cb, cp_cb])
-    model.fit_generator(tr_gen
-                        ,epochs=1000
-                        ,steps_per_epoch=n_records//batch_size
-                        ,validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
-                        ,use_multiprocessing=True, workers=1
-                        ,callbacks=[tb_cb, cp_cb])
-    model.save(model_dir+'/siamese_speech_model-final.h5')
-    # compute final accuracy on training and test sets
-    # y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
-    # tr_acc = compute_accuracy(tr_y, y_pred)
-    # print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
 
+    model.save('./models/siamese_speech_model-final.h5')
+    # compute final accuracy on training and test sets
+    y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
+    tr_acc = compute_accuracy(tr_y, y_pred)
     y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
     te_acc = compute_accuracy(te_y, y_pred)
+
+    print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
     print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
 
 
 
 if __name__ == '__main__':
-    train_siamese('story_words')
+    train_siamese()
-    # train_siamese('audio')

speech_utils.py

@@ -1,74 +0,0 @@
-import os
-import threading
-
-import multiprocessing
-import pandas as pd
-import numpy as np
-
-
-
-def _apply_df(args):
-    df, func, num, kwargs = args
-    return num, df.apply(func, **kwargs)
-
-def apply_by_multiprocessing(df,func,**kwargs):
-  cores = multiprocessing.cpu_count()
-  workers=kwargs.pop('workers') if 'workers' in kwargs else cores
-  pool = multiprocessing.Pool(processes=workers)
-  result = pool.map(_apply_df, [(d, func, i, kwargs) for i,d in enumerate(np.array_split(df, workers))])
-  pool.close()
-  result=sorted(result,key=lambda x:x[0])
-  return pd.concat([i[1] for i in result])
-
-def square(x):
-    return x**x
-
-if __name__ == '__main__':
-    df = pd.DataFrame({'a':range(10), 'b':range(10)})
-    apply_by_multiprocessing(df, square, axis=1, workers=4)
-
-
-def rm_rf(d):
-    for path in (os.path.join(d,f) for f in os.listdir(d)):
-        if os.path.isdir(path):
-            rm_rf(path)
-        else:
-            os.unlink(path)
-    os.rmdir(d)
-
-def create_dir(direc):
-    if not os.path.exists(direc):
-        os.makedirs(direc)
-    else:
-        rm_rf(direc)
-        create_dir(direc)
-
-
-#################### Now make the data generator threadsafe ####################
-
-class threadsafe_iter:
-    """Takes an iterator/generator and makes it thread-safe by
-    serializing call to the `next` method of given iterator/generator.
-    """
-    def __init__(self, it):
-        self.it = it
-        self.lock = threading.Lock()
-
-    def __iter__(self):
-        return self
-
-    def __next__(self): # Py3
-        with self.lock:
-            return next(self.it)
-
-    def next(self):     # Py2
-        with self.lock:
-            return self.it.next()
-
-
-def threadsafe_generator(f):
-    """A decorator that takes a generator function and makes it thread-safe.
-    """
-    def g(*a, **kw):
-        return threadsafe_iter(f(*a, **kw))
-    return g

test_siamese.py

@@ -1,15 +1,13 @@
-# from speech_siamese import siamese_model
+from speech_siamese import siamese_model
-from speech_tools import record_spectrogram, file_player
+from record_mic_speech import record_spectrogram
-# from importlib import reload
+from importlib import reload
 # import speech_data
 # reload(speech_data)
+from speech_data import create_test_pair,get_word_pairs_data,speech_data
 import numpy as np
-import pandas as pd
+
-import os
+model = siamese_model((15, 1654))
-import pickle
+model.load_weights('./models/siamese_speech_model-final.h5')
-import tensorflow as tf
-import csv
-from speech_data import padd_zeros
 
 def predict_recording_with(m,sample_size=15):
     spec1 = record_spectrogram(n_sec=1.4)
@@ -26,85 +24,7 @@ def test_with(audio_group):
     print(np.argmax(model.predict([X[:, 0], X[:, 1]]),axis=1))
     print(Y.astype(np.int8))
 
-def evaluate_siamese(audio_group='audio',model_file = 'siamese_speech_model-305-epoch-0.20-acc.h5'):
+test_with('rand_edu')
-    # audio_group='audio';model_file = 'siamese_speech_model-305-epoch-0.20-acc.h5'
-    records_file  = os.path.join('./outputs',audio_group+'.train.tfrecords')
-    const_file = os.path.join('./outputs',audio_group+'.constants')
-    model_weights_path =os.path.join('./models/story_words/',model_file)
-    (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
-    print('evaluating tfrecords({}-train)...'.format(audio_group))
-
-    model = siamese_model((n_spec, n_features))
-    model.load_weights(model_weights_path)
-    record_iterator = tf.python_io.tf_record_iterator(path=records_file)
-    #tqdm(enumerate(record_iterator),total=n_records)
-    result_csv = open('./outputs/' + audio_group + '.results.csv','w')
-    result_csv_w = csv.writer(result_csv, quoting=csv.QUOTE_MINIMAL)
-    result_csv_w.writerow(["phoneme1","phoneme2","voice1","voice2","rate1","rate2","variant1","variant2","file1","file2"])
-    for (i,string_record) in enumerate(record_iterator):
-        # string_record = next(record_iterator)
-        example = tf.train.Example()
-        example.ParseFromString(string_record)
-        spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
-        spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
-        spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
-        spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
-        spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
-        spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
-        p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
-        input_arr = np.asarray([[p_spec1,p_spec2]])
-        output_arr = np.asarray([example.features.feature['output'].int64_list.value])
-        y_pred = model.predict([input_arr[:, 0], input_arr[:, 1]])
-        predicted = np.asarray(y_pred[0]>0.5).astype(output_arr.dtype)
-        expected = output_arr[0]
-        if np.all(predicted == expected):
-            continue
-        word = example.features.feature['word'].bytes_list.value[0].decode()
-        phoneme1 = example.features.feature['phoneme1'].bytes_list.value[0].decode()
-        phoneme2 = example.features.feature['phoneme2'].bytes_list.value[0].decode()
-        voice1 = example.features.feature['voice1'].bytes_list.value[0].decode()
-        voice2 = example.features.feature['voice2'].bytes_list.value[0].decode()
-        language = example.features.feature['language'].bytes_list.value[0].decode()
-        rate1 = example.features.feature['rate1'].int64_list.value[0]
-        rate2 = example.features.feature['rate2'].int64_list.value[0]
-        variant1 = example.features.feature['variant1'].bytes_list.value[0].decode()
-        variant2 = example.features.feature['variant2'].bytes_list.value[0].decode()
-        file1 = example.features.feature['file1'].bytes_list.value[0].decode()
-        file2 = example.features.feature['file2'].bytes_list.value[0].decode()
-        print(phoneme1,phoneme2,voice1,voice2,rate1,rate2,variant1,variant2,file1,file2)
-        result_csv_w.writerow([phoneme1,phoneme2,voice1,voice2,rate1,rate2,variant1,variant2,file1,file2])
-    result_csv.close()
-
-
-def play_results(audio_group='audio'):
-    result_data = pd.read_csv('./outputs/' + audio_group + '.results.csv')
-    play_file,close_player = file_player()
-    quit = False
-    for (i,r) in result_data.iterrows():
-        if quit:
-            break
-        keys = ["phoneme1","phoneme2","voice1","voice2","rate1","rate2","variant1","variant2"]
-        row_vals = [str(r[k]) for k in keys]
-        h_str = '\t'.join(keys)
-        row_str = '\t'.join(row_vals)
-        while True:
-            print(h_str)
-            print(row_str)
-            play_file('./outputs/'+audio_group+'/'+r['file1'],True)
-            play_file('./outputs/'+audio_group+'/'+r['file2'],True)
-            a = input("press 'r/q/[Enter]' to replay/quit/continue:\t")
-            if a == 'r':
-                continue
-            if a == 'q':
-                quit = True
-                break
-            else:
-                break
-    close_player()
-
-# evaluate_siamese('story_words',model_file='siamese_speech_model-305-epoch-0.20-acc.h5')
-play_results('story_words')
-# test_with('rand_edu')
 # sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
 # print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))
 # print(sunflower_result)

tts_samplegen.py

@@ -3,7 +3,6 @@ from AppKit import NSSpeechSynthesizer, NSSpeechInputModeProperty
 from AppKit import NSSpeechModePhoneme
 from Foundation import NSURL
 import json
-import csv
 import random
 import os
 import re
@@ -82,11 +81,6 @@ class SynthFile(object):
 
         return ','.join([str(c) for c in cols])+'\n'
 
-    def get_values(self):
-        cols = [self.word, self.phoneme, self.voice,
-                self.voice_lang, self.rate, self.variant,
-                self.filename]
-        return [str(c) for c in cols]
 
 class SynthVariant(object):
     """docstring for SynthVariant."""
@@ -197,11 +191,22 @@ def synth_generator():
         print("It took {} to synthsize all variants.".format(time_str))
     return synth_for_words
 
+
+def write_synths(synth_list, fname, csv=False):
+    f = open(fname, 'w')
+    if csv:
+        for s in synth_list:
+            f.write(s.get_csv())
+    else:
+        json.dump([s.get_json() for s in synth_list], f)
+    f.close()
+
+
 def synth_logger(fname, csv=False):
     f = open(fname, 'w')
-    s_csv_w = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
+
     def csv_writer(s):
-        s_csv_w.writerow(s.get_values())
+        f.write(s.get_csv())
     synth_list = []
 
     def json_writer(s):