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datasets.py
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datasets.py
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# A Wavenet For Speech Denoising - Dario Rethage - 19.05.2017
# Datasets.py
import util
import os
import numpy as np
import logging
class NSDTSEADataset():
def __init__(self, config, model):
self.model = model
self.path = config['dataset']['path']
self.sample_rate = config['dataset']['sample_rate']
self.file_paths = {'train': {'clean': [], 'noisy': []}, 'test': {'clean': [], 'noisy': []}}
self.sequences = {'train': {'clean': [], 'noisy': []}, 'test': {'clean': [], 'noisy': []}}
self.voice_indices = {'train': [], 'test': []}
self.regain_factors = {'train': [], 'test': []}
self.speakers = {'train': [], 'test': []}
self.speaker_mapping = {}
self.batch_size = config['training']['batch_size']
self.noise_only_percent = config['dataset']['noise_only_percent']
self.regain = config['dataset']['regain']
self.extract_voice = config['dataset']['extract_voice']
self.in_memory_percentage = config['dataset']['in_memory_percentage']
self.num_sequences_in_memory = 0
self.condition_encode_function = util.get_condition_input_encode_func(config['model']['condition_encoding'])
def load_dataset(self):
print ('Loading NSDTSEA dataset...')
for set in ['train', 'test']:
for condition in ['clean', 'noisy']:
current_directory = os.path.join(self.path, condition+'_'+set+'set_wav')
sequences, file_paths, speakers, speech_onset_offset_indices, regain_factors = \
self.load_directory(current_directory, condition)
self.file_paths[set][condition] = file_paths
self.speakers[set] = speakers
self.sequences[set][condition] = sequences
if condition == 'clean':
self.voice_indices[set] = speech_onset_offset_indices
self.regain_factors[set] = regain_factors
return self
def load_directory(self, directory_path, condition):
filenames = [filename for filename in os.listdir(directory_path) if filename.endswith('.wav')]
speakers = []
file_paths = []
speech_onset_offset_indices = []
regain_factors = []
sequences = []
for filename in filenames:
speaker_name = filename[0:4]
speakers.append(speaker_name)
filepath = os.path.join(directory_path, filename)
if condition == 'clean':
sequence = util.load_wav(filepath, self.sample_rate)
sequences.append(sequence)
self.num_sequences_in_memory += 1
regain_factors.append(self.regain / util.rms(sequence))
if self.extract_voice:
speech_onset_offset_indices.append(util.get_subsequence_with_speech_indices(sequence))
else:
if self.in_memory_percentage == 1 or np.random.uniform(0, 1) <= (self.in_memory_percentage-0.5)*2:
sequence = util.load_wav(filepath, self.sample_rate)
sequences.append(sequence)
self.num_sequences_in_memory += 1
else:
sequences.append([-1])
if speaker_name not in self.speaker_mapping:
self.speaker_mapping[speaker_name] = len(self.speaker_mapping) + 1
file_paths.append(filepath)
return sequences, file_paths, speakers, speech_onset_offset_indices, regain_factors
def get_num_sequences_in_dataset(self):
return len(self.sequences['train']['clean']) + len(self.sequences['train']['noisy']) + len(self.sequences['test']['clean']) + len(self.sequences['test']['noisy'])
def retrieve_sequence(self, set, condition, sequence_num):
if len(self.sequences[set][condition][sequence_num]) == 1:
sequence = util.load_wav(self.file_paths[set][condition][sequence_num], self.sample_rate)
if (float(self.num_sequences_in_memory) / self.get_num_sequences_in_dataset()) < self.in_memory_percentage:
self.sequences[set][condition][sequence_num] = sequence
self.num_sequences_in_memory += 1
else:
sequence = self.sequences[set][condition][sequence_num]
return np.array(sequence)
def get_random_batch_generator(self, set):
if set not in ['train', 'test']:
raise ValueError("Argument SET must be either 'train' or 'test'")
while True:
sample_indices = np.random.randint(0, len(self.sequences[set]['clean']), self.batch_size)
condition_inputs = []
batch_inputs = []
batch_outputs_1 = []
batch_outputs_2 = []
for i, sample_i in enumerate(sample_indices):
while True:
speech = self.retrieve_sequence(set, 'clean', sample_i)
noisy = self.retrieve_sequence(set, 'noisy', sample_i)
noise = noisy - speech
if self.extract_voice:
speech = speech[self.voice_indices[set][sample_i][0]:self.voice_indices[set][sample_i][1]]
speech_regained = speech * self.regain_factors[set][sample_i]
noise_regained = noise * self.regain_factors[set][sample_i]
if len(speech_regained) < self.model.input_length:
sample_i = np.random.randint(0, len(self.sequences[set]['clean']))
else:
break
offset = np.squeeze(np.random.randint(0, len(speech_regained) - self.model.input_length, 1))
speech_fragment = speech_regained[offset:offset + self.model.input_length]
noise_fragment = noise_regained[offset:offset + self.model.input_length]
input = noise_fragment + speech_fragment
output_speech = speech_fragment
output_noise = noise_fragment
if self.noise_only_percent > 0:
if np.random.uniform(0, 1) <= self.noise_only_percent:
input = output_noise #Noise only
output_speech = np.array([0] * self.model.input_length) #Silence
batch_inputs.append(input)
batch_outputs_1.append(output_speech)
batch_outputs_2.append(output_noise)
if np.random.uniform(0, 1) <= 1.0 / self.get_num_condition_classes():
condition_input = 0
else:
condition_input = self.speaker_mapping[self.speakers[set][sample_i]]
if condition_input > 28: #If speaker is in test set, use wildcard condition class 0
condition_input = 0
condition_inputs.append(condition_input)
batch_inputs = np.array(batch_inputs, dtype='float32')
batch_outputs_1 = np.array(batch_outputs_1, dtype='float32')
batch_outputs_2 = np.array(batch_outputs_2, dtype='float32')
batch_outputs_1 = batch_outputs_1[:, self.model.get_padded_target_field_indices()]
batch_outputs_2 = batch_outputs_2[:, self.model.get_padded_target_field_indices()]
condition_inputs = self.condition_encode_function(np.array(condition_inputs, dtype='uint8'), self.model.num_condition_classes)
batch = {'data_input': batch_inputs}, {
'data_output_1': batch_outputs_1, 'data_output_2': batch_outputs_2}
yield batch
def get_condition_input_encode_func(self, representation):
if representation == 'binary':
return util.binary_encode
else:
return util.one_hot_encode
def get_num_condition_classes(self):
return 29
def get_target_sample_index(self):
return int(np.floor(self.fragment_length / 2.0))
def get_samples_of_interest_indices(self, causal=False):
if causal:
return -1
else:
target_sample_index = self.get_target_sample_index()
return range(target_sample_index - self.half_target_field_length - self.target_padding,
target_sample_index + self.half_target_field_length + self.target_padding + 1)
def get_sample_weight_vector_length(self):
if self.samples_of_interest_only:
return len(self.get_samples_of_interest_indices())
else:
return self.fragment_length