added code to record and generate spectrogram, wip test model

record_mic_speech.py

@@ -1,36 +1,42 @@
 import pyaudio
 import numpy as np
 # from matplotlib import pyplot as plt
-from spectro_gen import plot_stft
+from spectro_gen import plot_stft, generate_spectrogram
 
+
+def record_spectrogram(n_sec, plot=False, playback=False):
     SAMPLE_RATE = 22050
-N_SEC = 1.5
-CHUNKSIZE = int(SAMPLE_RATE * N_SEC)  # fixed chunk size
-
+    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(
         format=pyaudio.paFloat32,
-    channels=2,
+        channels=N_CHANNELS,
         rate=SAMPLE_RATE,
         input=True,
         frames_per_buffer=CHUNKSIZE)
-
     data = stream.read(CHUNKSIZE)
     numpydata = np.frombuffer(data, dtype=np.float32)
     multi_channel = np.abs(np.reshape(numpydata, (-1, 2))).mean(axis=1)
     one_channel = np.asarray([multi_channel, -1 * multi_channel]).T.reshape(-1)
     mean_channel_data = one_channel.tobytes()
-plot_stft(one_channel, SAMPLE_RATE)
-# plt.plot(one_channel)
-# plt.show()
-
     stream.stop_stream()
     stream.close()
     p_inp.terminate()
-
+    if plot:
+        plot_stft(one_channel, SAMPLE_RATE)
+    if playback:
         p_oup = pyaudio.PyAudio()
         stream = p_oup.open(
-    format=pyaudio.paFloat32, channels=2, rate=SAMPLE_RATE, output=True)
+            format=pyaudio.paFloat32,
+            channels=2,
+            rate=SAMPLE_RATE,
+            output=True)
         stream.write(mean_channel_data)
         stream.close()
         p_oup.terminate()
+    ims, _ = generate_spectrogram(one_channel, SAMPLE_RATE)
+    return ims

spectro_gen.py

@@ -128,7 +128,7 @@ def play_sunflower():
     sample_r = snd.get_info('./outputs/sunflowers-Alex-150-normal-589.aiff')[0]
     snd_data_f64 = snd.read('./outputs/sunflowers-Alex-150-normal-589.aiff')[0]
     snd_data_f32 = snd_data_f64.astype(np.float32)
-    snd_data_f32.shape
+    print(snd_data_f32.shape)
     snd_data = snd_data_f32.tobytes()
     p_oup = pyaudio.PyAudio()
     stream = p_oup.open(

speech_data.py

@@ -15,6 +15,16 @@ def get_siamese_pairs(groupF1, groupF2):
     return (t, f)
 
 
+def create_X(sp, max_samples):
+    def append_zeros(spgr):
+        return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
+                          'median')
+
+    l_sample = append_zeros(sp[0]['spectrogram'])
+    r_sample = append_zeros(sp[1]['spectrogram'])
+    return np.asarray([l_sample, r_sample])
+
+
 def sunflower_pairs_data():
     audio_samples = pd.read_csv(
         './outputs/audio.csv',
@@ -35,19 +45,7 @@ def sunflower_pairs_data():
     Y = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
     X_sample_pairs = same_data + diff_data
 
-    def append_zeros(spgr):
-        sample = np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
-                            'median')
-        return np.expand_dims(sample, axis=0)
-
-    def create_X(sp):
-        # sample_count = sp[0]['file'].shape[0]
-        l_sample = append_zeros(sp[0]['spectrogram'])
-        r_sample = append_zeros(
-            sp[1]['spectrogram'])
-        return np.expand_dims(np.vstack([l_sample, r_sample]), axis=0)
-
-    X_list = (create_X(sp) for sp in X_sample_pairs)
+    X_list = (create_X(sp, max_samples) for sp in X_sample_pairs)
     X = np.vstack(X_list)
     tr_pairs, te_pairs, tr_y, te_y = train_test_split(X, Y, test_size=0.1)
     return train_test_split(X, Y, test_size=0.1)
@@ -69,27 +67,16 @@ def create_speech_pairs_data(audio_group='audio'):
     audio_samples = pd.read_pickle('outputs/spectrogram.pkl')
     max_samples = audio_samples['spectrogram'].apply(
         lambda x: x.shape[0]).max()
-
     # sample_size = audio_samples['spectrogram'][0].shape[1]
 
-    def append_zeros(spgr):
-        sample = np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
-                            'median')
-        return sample
-
-    def create_X(sp):
-        l_sample = append_zeros(sp[0]['spectrogram'])
-        r_sample = append_zeros(sp[1]['spectrogram'])
-        return np.asarray([l_sample, r_sample])
-
     print('generating siamese speech pairs')
     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) for s in same[:10]])
-        diff_data.extend([create_X(d) for d in diff[:10]])
+        same_data.extend([create_X(s, max_samples) for s in same[:10]])
+        diff_data.extend([create_X(d, max_samples) for d in diff[:10]])
     print('creating all speech pairs')
     Y = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
     print('casting as array speech pairs')

speech_siamese.py

@@ -2,9 +2,9 @@ from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
 from speech_data import speech_model_data
-from keras.models import Model
+from keras.models import Model,load_model
 from keras.layers import Input, Dense, Dropout, LSTM, Lambda
-from keras.optimizers import RMSprop, SGD
+from keras.optimizers import RMSprop
 from keras.callbacks import TensorBoard, ModelCheckpoint
 from keras import backend as K
 
@@ -63,6 +63,7 @@ def accuracy(y_true, y_pred):
     return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
 
 
+def train_siamese():
     # the data, shuffled and split between train and test sets
     tr_pairs, te_pairs, tr_y, te_y = speech_model_data()
     input_dim = (tr_pairs.shape[2], tr_pairs.shape[3])
@@ -107,7 +108,6 @@ cp_cb = ModelCheckpoint(
         period=1)
     # train
     rms = RMSprop(lr=0.001)
-sgd = SGD(lr=0.001)
     model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
     model.fit(
         [tr_pairs[:, 0], tr_pairs[:, 1]],
@@ -126,3 +126,23 @@ 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))
+
+
+def trained_siamese_model():
+    # input_dim = (15, 1654)
+    # base_network = create_base_rnn_network(input_dim)
+    # input_a = Input(shape=input_dim)
+    # input_b = Input(shape=input_dim)
+    # processed_a = base_network(input_a)
+    # processed_b = base_network(input_b)
+    # distance = Lambda(
+    #     euclidean_distance,
+    #     output_shape=eucl_dist_output_shape)([processed_a, processed_b])
+    #
+    # model = Model([input_a, input_b], distance)
+    model = load_model('./models/siamese_speech_model-final.h5')
+    return model
+
+
+if __name__ == '__main__':
+    train_siamese()

test_siamese.py

@@ -0,0 +1,7 @@
+# from speech_siamese import trained_siamese_model
+from keras.models import load_model
+from record_mic_speech import record_spectrogram
+
+model = load_model('./models/siamese_speech_model-final.h5')
+spec1 = record_spectrogram(n_sec=1.2)
+spec2 = record_spectrogram(n_sec=1.2)