80 lines
4.2 KiB
Python
80 lines
4.2 KiB
Python
from speech_siamese import siamese_model
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from record_mic_speech import record_spectrogram
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# from importlib import reload
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# import speech_data
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# reload(speech_data)
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import numpy as np
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import os
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import pickle
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import tensorflow as tf
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import csv
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from speech_data import padd_zeros
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def predict_recording_with(m,sample_size=15):
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spec1 = record_spectrogram(n_sec=1.4)
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spec2 = record_spectrogram(n_sec=1.4)
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inp = create_test_pair(spec1,spec2,sample_size)
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return m.predict([inp[:, 0], inp[:, 1]])
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# while(True):
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# print(predict_recording_with(model))
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def test_with(audio_group):
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X,Y = speech_data(audio_group)
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print(np.argmax(model.predict([X[:, 0], X[:, 1]]),axis=1))
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print(Y.astype(np.int8))
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def evaluate_siamese(audio_group='audio',model_file = 'siamese_speech_model-46-epoch-0.29-acc.h5'):
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records_file = os.path.join('./outputs',audio_group+'.train.tfrecords')
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const_file = os.path.join('./outputs',audio_group+'.constants')
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model_weights_path =os.path.join('./models/story_words/',model_file)
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(n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
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print('evaluating tfrecords({}-train)...'.format(audio_group))
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model = siamese_model((n_spec, n_features))
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model.load_weights(model_weights_path)
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record_iterator = tf.python_io.tf_record_iterator(path=records_file)
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#tqdm(enumerate(record_iterator),total=n_records)
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with open('./outputs/' + audio_group + '.results.csv','w') as result_csv:
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result_csv_w = csv.writer(result_csv, quoting=csv.QUOTE_MINIMAL)
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for (i,string_record) in enumerate(record_iterator):
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# string_record = next(record_iterator)
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example = tf.train.Example()
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example.ParseFromString(string_record)
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spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
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spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
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spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
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spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
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spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
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spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
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p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
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input_arr = np.asarray([[p_spec1,p_spec2]])
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output_arr = np.asarray([example.features.feature['output'].int64_list.value])
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y_pred = model.predict([input_arr[:, 0], input_arr[:, 1]])
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predicted = np.asarray(y_pred[0]>0.5).astype(output_arr.dtype)
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expected = output_arr[0]
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if np.all(predicted == expected):
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continue
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word = example.features.feature['word'].bytes_list.value[0].decode()
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phoneme1 = example.features.feature['phoneme1'].bytes_list.value[0].decode()
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phoneme2 = example.features.feature['phoneme2'].bytes_list.value[0].decode()
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voice1 = example.features.feature['voice1'].bytes_list.value[0].decode()
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voice2 = example.features.feature['voice2'].bytes_list.value[0].decode()
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language = example.features.feature['language'].bytes_list.value[0].decode()
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rate1 = example.features.feature['rate1'].int64_list.value[0]
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rate2 = example.features.feature['rate2'].int64_list.value[0]
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variant1 = example.features.feature['variant1'].bytes_list.value[0].decode()
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variant2 = example.features.feature['variant2'].bytes_list.value[0].decode()
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file1 = example.features.feature['file1'].bytes_list.value[0].decode()
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file2 = example.features.feature['file2'].bytes_list.value[0].decode()
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print(phoneme1,phoneme2,voice1,voice2,rate1,rate2,variant1,variant2,file1,file2)
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result_csv_w.writerow([phoneme1,phoneme2,voice1,voice2,rate1,rate2,variant1,variant2,file1,file2])
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evaluate_siamese('story_words',model_file='siamese_speech_model-92-epoch-0.20-acc.h5')
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# test_with('rand_edu')
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# sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
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# print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))
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# print(sunflower_result)
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