def get_training_data(self):
# waves = waveUtils.generateRandomFrequencies(self.input_s, num=self.num_freqs, freq_min=self.min_freq,
# freq_max=self.max_freq, num_to_combine=4,
# samples_per_sec=self.sample_rate)
waves1, original_sample_rate1 = waveUtils.loadAudioFiles("chello")
sample_factor1 = (original_sample_rate1 // self.target_sample_rate)
self.sample_rate = original_sample_rate1 / sample_factor1
waves1 = waveUtils.reduceQuality(waves1, sample_factor1)
waves1 = waveUtils.extractCenters(waves1, num_samples=self.input_s, latest_start=self.sample_rate)
waves2, original_sample_rate2 = waveUtils.loadAudioFiles("flute")
sample_factor2 = (original_sample_rate2 // self.target_sample_rate)
if self.sample_rate != original_sample_rate2 / sample_factor2:
print "Mismatching sample rates!"
exit(1)
waves2 = waveUtils.reduceQuality(waves2, sample_factor2)
waves2 = waveUtils.extractCenters(waves2, num_samples=self.input_s, latest_start=self.sample_rate)
all_data = []
all_originals = []
for i in range(len(waves1)):
for j in range(len(waves2)):
all_data.append(waves1[i] + waves2[j])
all_originals.append([waves1[i], waves2[j]])
num_samples = len(all_data)
perm = np.random.permutation(num_samples)
all_data = np.asarray(all_data)[perm]
all_originals = np.asarray(all_originals)[perm]
all_data = np.asarray(all_data)
all_data = all_data + np.random.normal(scale=self.random_noise_stdev, size=all_data.shape)
self._val_data = all_data[:self.validation_samples]
self._val_originals = all_originals[:self.validation_samples]
return all_data[self.validation_samples:], all_originals[self.validation_samples:]
def get_training_data(self):
# waves = waveUtils.generateRandomFrequencies(self.input_s, num=self.num_freqs, freq_min=self.min_freq,
# freq_max=self.max_freq, num_to_combine=4,
# samples_per_sec=self.sample_rate)
waves, self.original_sample_rate = waveUtils.loadAudioFiles("chello")
sample_factor = (self.original_sample_rate // self.target_sample_rate)
self.sample_rate = self.original_sample_rate / sample_factor
waves = waveUtils.reduceQuality(waves, sample_factor)
waves = waveUtils.extractCenters(waves, num_samples=self.input_s)
all_data = []
all_originals = []
for i in range(len(waves)):
for j in range(i + 1, len(waves)):
all_data.append(waves[i] + waves[j])
all_originals.append([waves[i], waves[j]])
num_samples = len(all_data)
perm = np.random.permutation(num_samples)
all_data = np.asarray(all_data)[perm]
all_originals = np.asarray(all_originals)[perm]
all_data = np.asarray(all_data)
all_data = all_data + np.random.normal(scale=self.random_noise_stdev,
size=all_data.shape)
self._val_data = all_data[:self.validation_samples]
self._val_originals = all_originals[:self.validation_samples]
return all_data[self.validation_samples:], all_originals[
self.validation_samples:]
