visualizing and playing sound files where prediction fails

speech_data.py

@@ -6,7 +6,7 @@ from speech_utils import threadsafe_iter
 import tensorflow as tf
 from tensorflow.python.ops import data_flow_ops
 import numpy as np
-from spectro_gen import generate_aiff_spectrogram
+from speech_spectrum import generate_aiff_spectrogram
 from sklearn.model_selection import train_test_split
 import itertools
 import os

speech_tools.py

@@ -1,15 +1,36 @@
 import pyaudio
+from pysndfile import sndio as snd
 import numpy as np
 # from matplotlib import pyplot as plt
-from spectro_gen import plot_stft, generate_spec_frec
+from speech_spectrum import plot_stft, generate_spec_frec
 
-
-def record_spectrogram(n_sec, plot=False, playback=False):
 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()
     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(

test_siamese.py

@@ -1,9 +1,10 @@
-from speech_siamese import siamese_model
+# from speech_siamese import siamese_model
-from record_mic_speech import record_spectrogram
+from speech_tools import record_spectrogram, file_player
 # from importlib import reload
 # import speech_data
 # reload(speech_data)
 import numpy as np
+import pandas as pd
 import os
 import pickle
 import tensorflow as tf
@@ -25,7 +26,8 @@ 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-46-epoch-0.29-acc.h5'):
+def evaluate_siamese(audio_group='audio',model_file = 'siamese_speech_model-305-epoch-0.20-acc.h5'):
+    # 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)
@@ -36,8 +38,9 @@ def evaluate_siamese(audio_group='audio',model_file = 'siamese_speech_model-46-e
     model.load_weights(model_weights_path)
     record_iterator = tf.python_io.tf_record_iterator(path=records_file)
     #tqdm(enumerate(record_iterator),total=n_records)
-    with open('./outputs/' + audio_group + '.results.csv','w') as result_csv:
+    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()
@@ -70,9 +73,37 @@ def evaluate_siamese(audio_group='audio',model_file = 'siamese_speech_model-46-e
         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()
 
 
-evaluate_siamese('story_words',model_file='siamese_speech_model-92-epoch-0.20-acc.h5')
+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))