def get_file_indices(file_indices_path, data_path): try: file_indices = np.load(file_indices_path) except: file_indices = gtrd.get_data_files_indices(data_path) np.save(file_indices_path, file_indices) return file_indices
import numpy as np root_path = '/var/tmp/stoehr/Template-Speech-Recognition/' data_path = root_path + 'Data/' old_exp_path = root_path + 'Notebook/1/' old_data_path = old_exp_path + 'data/' exp_path = root_path + 'Notebook/2/' tmp_data_path = exp_path + 'data/' import sys, os, cPickle sys.path.append(root_path) import template_speech_rec.get_train_data as gtrd import template_speech_rec.estimate_template as et train_data_path = root_path+'Data/Train/' file_indices = gtrd.get_data_files_indices(train_data_path) syllable = np.array(['aa','r']) clipped_bgd = np.load(old_data_path+'clipped_bgd_102012.npy') padded_examples = np.load(old_data_path+'aar_padded_examples_bgd.npy') lengths = np.load(old_data_path+'aar_lengths.npy') test_example_lengths = np.load(old_data_path+'test_example_lengths_102012.npy') detection_array = np.zeros((test_example_lengths.shape[0], test_example_lengths.max() + 2),dtype=np.float32)
sp = gtrd.SpectrogramParameters( sample_rate=16000, num_window_samples=320, num_window_step_samples=80, fft_length=512, kernel_length=7, freq_cutoff=3000, use_mel=True) ep = gtrd.EdgemapParameters(block_length=40, spread_length=1, threshold=.7) utterances_path = '/home/mark/Template-Speech-Recognition/Data/Train/' file_indices = gtrd.get_data_files_indices(utterances_path) syllable=('aa','r') syllable_features,avg_bgd=gtrd.get_syllable_features_directory(utterances_path,file_indices,syllable, S_config=sp,E_config=ep,offset=0, E_verbose=False,return_avg_bgd=True) np.save('data/aar_bgd_mel.npy',avg_bgd.E) example_mat = gtrd.recover_example_map(syllable_features) lengths,waveforms = gtrd.recover_waveforms(syllable_features,example_mat) np.savez('data/aar_waveforms_lengths.npz',waveforms,lengths,example_mat) Slengths,Ss = gtrd.recover_specs(syllable_features,example_mat)
root_path = '/home/mark/Template-Speech-Recognition/' #root_path = '/var/tmp/stoehr/Template-Speech-Recognition/' data_path = root_path + 'Data/' old_exp_path = root_path + 'Notebook/1/' old_data_path = old_exp_path + 'data/' exp_path = root_path + 'Notebook/2/' tmp_data_path = exp_path + 'data/' import sys, os, cPickle sys.path.append(root_path) import template_speech_rec.get_train_data as gtrd import template_speech_rec.estimate_template as et file_indices = gtrd.get_data_files_indices(data_path+'Test/') num_mix = 3 i = 0 false_positive_rates =np.load(tmp_data_path+'fpr_aar_registered_new%d_%d.npy' % (num_mix,i)) true_positive_rates = np.load(tmp_data_path+'tpr_aar_registered_new%d_%d.npy' % (num_mix,i)) import matplotlib.pyplot as plt colors = ['r','b','g','y','k','m','c'] markers = ['s','o','^','>','v','<','d','p'] plt.close() plt.figure()
def get_params( args, sample_rate=16000, num_window_samples=320, num_window_step_samples=80, fft_length=512, kernel_length=7, freq_cutoff=3000, use_mel=False, ): # we get the basic file paths right here # TODO: make this system adaptive root_path = "/home/mark/Template-Speech-Recognition/" utterances_path = "/home/mark/Template-Speech-Recognition/Data/Train/" try: file_indices = np.load("data_parts/train_file_indices.npy") except: file_indices = gtrd.get_data_files_indices(utterances_path) np.save("data_parts/train_file_indices.npy", file_indices) num_mix_params = [1, 2, 3, 5, 7, 9] test_path = "/home/mark/Template-Speech-Recognition/Data/Test/" train_path = "/home/mark/Template-Speech-Recognition/Data/Train/" try: test_example_lengths = np.load("data_parts/test_example_lengths.npy") test_file_indices = np.load("data_parts/test_file_indices.npy") except: test_file_indices = gtrd.get_data_files_indices(test_path) test_example_lengths = gtrd.get_detect_lengths(test_file_indices, test_path) np.save("data_parts/test_example_lengths.npy", test_example_lengths) np.save("data_parts/test_file_indices.npy", test_file_indices) try: train_example_lengths = np.load("data_parts/train_example_lengths.npy") train_file_indices = np.load("data_parts/train_file_indices.npy") except: train_file_indices = gtrd.get_data_files_indices(train_path) train_example_lengths = gtrd.get_detect_lengths(train_file_indices, train_path) np.save("data_parts/train_example_lengths.npy", train_example_lengths) np.save("data_parts/train_file_indices.npy", train_file_indices) return ( gtrd.SpectrogramParameters( sample_rate=16000, num_window_samples=320, num_window_step_samples=80, fft_length=512, kernel_length=7, freq_cutoff=3000, use_mel=args.use_mel, ), gtrd.makeEdgemapParameters( block_length=args.edgeMapBlockLength, spread_length=args.edgeMapSpreadLength, threshold=args.edgeMapThreshold, ), root_path, utterances_path, file_indices, num_mix_params, test_path, train_path, train_example_lengths, train_file_indices, test_example_lengths, test_file_indices, )
sp = gtrd.SpectrogramParameters( sample_rate=16000, num_window_samples=320, num_window_step_samples=80, fft_length=512, kernel_length=7, freq_cutoff=3000, use_mel=False) ep = gtrd.EdgemapParameters(block_length=40, spread_length=1, threshold=.7) utterances_path = '/home/mark/Template-Speech-Recognition/Data/Train/' file_indices = gtrd.get_data_files_indices(utterances_path) syllable=('aa','r') syllable_features,avg_bgd=gtrd.get_syllable_features_directory(utterances_path,file_indices,syllable, S_config=sp,E_config=ep,offset=0, E_verbose=False,return_avg_bgd=True, waveform_offset=15) np.save('data/aar_bgd.npy',avg_bgd.E) example_mat = gtrd.recover_example_map(syllable_features) lengths,waveforms = gtrd.recover_waveforms(syllable_features,example_mat) np.savez('data/aar_waveforms_lengths.npz',waveforms,lengths,example_mat) import scipy.io