def batch_analysis(x, fs, chunk_size):
"""
batch_analysis(x,fs,chunk_size)
computes the fundamental frequency/pitch of blocks/,voiced_samples and the rms values
that are important for analysis and will be used for pre-process
Parameters: x-discrete data from the wavefile
fs-sampling frequency
Chunk_Size- The size of block containing datas
Returns: fundamental_frequency_in_blocks- This is a fundamental frequency(or pitch)
for the blocks in Chunk_Size
voiced_samples-This are samples that contain the voiced samples.Will be used
for the entire process and is important for the synthesis process as well.
rms- is the root mean square computation that will be important for
categorizing inflecion/pitch bending samples.
"""
fundamental_frequency_in_blocks = alysis.pitch_detect(x, fs, chunk_size)
rms = alysis.root_mean_square(x, chunk_size, fs)
voiced_unvoiced_starting_info_object = alysis.starting_info(
x, fundamental_frequency_in_blocks, fs, chunk_size)
voiced_samples = voiced_unvoiced_starting_info_object['VSamp']
return fundamental_frequency_in_blocks, voiced_samples, rms
def emotive_speech(x, fs, typeOfEmotion):
CHUNK_SIZE = 1024
NUM_BLOCKS = int(np.ceil(len(x) / CHUNK_SIZE))
SAMPLE_PERIOD = 1 / float(fs) * CHUNK_SIZE
TIME_STAMPS = (np.arange(0, NUM_BLOCKS - 1) * (CHUNK_SIZE / float(fs)))
QFACTOR = 1
#---------------------Analysis---------------------------------------#
data_in_blocks = alysis.data_blocks(x, CHUNK_SIZE)
fundamental_frequency_in_blocks = alysis.pitch_detect(x, fs, CHUNK_SIZE)
voiced_unvoiced_starting_info_object = alysis.starting_info(
x, fundamental_frequency_in_blocks, fs, CHUNK_SIZE)
voiced_samples = voiced_unvoiced_starting_info_object['VSamp']
voiced_regions = alysis.voiced_regions(
x, fundamental_frequency_in_blocks,
voiced_unvoiced_starting_info_object, CHUNK_SIZE)
consecutive_blocks = 1 + int(0.5 / SAMPLE_PERIOD)
#---------------------preprocess-------------------------------------#
inflection_voice_samples = prep.pre_process(voiced_samples)
frequency_of_voiced_samples = fundamental_frequency_in_blocks[
inflection_voice_samples]
rms = prep.root_mean_square(x, CHUNK_SIZE, fs)[0]
frequency_for_inflection = prep.potential_inflection_fundamental_frequency(
frequency_of_voiced_samples)
inflection_sample_numbers = prep.matrix_of_sample_numbers(
rms, inflection_voice_samples)
inflect_blocks = prep.consecutive_blocks_for_inflection(
inflection_sample_numbers, consecutive_blocks)
selected_inflect_block = prep.alteration_of_discrete_data(
inflection_sample_numbers, consecutive_blocks, inflect_blocks)
n = prep.consecutive_blocks_in_selected_blocks(selected_inflect_block,
consecutive_blocks)
reshaped_inflect_blocks = prep.reshaped_inflection_blocks(
n, selected_inflect_block, consecutive_blocks)
differece_arrays = prep.difference_arrays(NUM_BLOCKS,
reshaped_inflect_blocks)
#----------------------synthesis-------------------------------------#
if typeOfEmotion == "Happy":
consecutive_blocks = 1 + int(0.5 / SAMPLE_PERIOD)
selected_inflect_block = prep.alteration_of_discrete_data(
inflection_sample_numbers, consecutive_blocks, inflect_blocks)
utterance_time_stamps = TIME_STAMPS[selected_inflect_block]
gain = 3.0
semitones = 0.5
synth.happy_patch(fs, semitones, QFACTOR, gain, utterance_time_stamps)
if typeOfEmotion == "HappyTensed":
consecutive_blocks = int(0.5 / SAMPLE_PERIOD)
inflection_sample_numbers = prep.matrix_of_sample_numbers(
rms, inflection_voice_samples)
inflect_blocks = prep.consecutive_blocks_for_inflection(
inflection_sample_numbers, consecutive_blocks)
selected_inflect_block = prep.alteration_of_discrete_data(
inflection_sample_numbers, consecutive_blocks, inflect_blocks)
utterance_time_stamps = TIME_STAMPS[selected_inflect_block]
gain = 3.0
semitones = 1.0
synth.happy_tensed_patch(fs, semitones, QFACTOR, gain,
utterance_time_stamps)
if typeOfEmotion == "Sad":
gain = 0.25
semitones = -0.5
synth.sad_patch(fs, semitones, QFACTOR, gain)
if typeOfEmotion == "Afraid":
speed = 8.5
depth = 50
utterance_time_stamps = TIME_STAMPS[selected_inflect_block]
synth.afraid_patch(fs, speed, depth, utterance_time_stamps)