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predict.py
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predict.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 24 19:29:57 2019
@author: a-kojima
"""
import numpy as np
import soundfile as sf
import matplotlib.pyplot as pl
from beamformer import complexGMM_mvdr as cgmm
from beamformer import complexGMM_mvdr_snr_selective as cgmm_snr
from maskestimator import model, shaper, feature
#==========================================
# ANALYSIS PARAMETERS
#==========================================
SAMPLING_FREQUENCY = 16000
FFTL = 1024
SHIFT = 256
#==========================================
# ESURAL MASL ESTIMATOR PARAMETERS
#==========================================
LEFT_CONTEXT = 0
RIGHT_CONTEXT = 0
NUMBER_OF_SKIP_FRAME = 0
#==========================================
# ESURAL MASL ESTIMATOR TRAINNING PARAMERTERS
#==========================================
TRUNCATE_GRAD = 7
IS_DEBUG_SHOW_PREDICT_MASK = True
NOISY_SPEECH_PATH = r'./dataset/data_for_beamforming/F02_011C021A_BUS.CH{}.wav'
CHANNEL_INDEX = [1, 2, 3, 4, 5, 6]
WEIGHT_PATH = r'./model/194sequence_false_e1.hdf5'
NUMBER_OF_STACK = LEFT_CONTEXT + RIGHT_CONTEXT + 1
OPERATION = 'median'
RECURRENT_CELL_INIT = 0.00001 #0.04
MAX_SEQUENCE = 5000
#==========================================
# get model
#==========================================
mask_estimator_generator = model.NeuralMaskEstimation(TRUNCATE_GRAD,
NUMBER_OF_STACK,
0.1,
FFTL // 2 + 1,
recurrent_init=RECURRENT_CELL_INIT)
mask_estimator = mask_estimator_generator.get_model(is_stateful=True, is_show_detail=True, is_adapt=False)
mask_estimator = mask_estimator_generator.load_weight_param(mask_estimator, WEIGHT_PATH)
#==========================================
# predicting data shaper
#==========================================
data_shaper = shaper.Shape_data(LEFT_CONTEXT,
RIGHT_CONTEXT,
TRUNCATE_GRAD,
NUMBER_OF_SKIP_FRAME )
#==========================================
# get features
#==========================================
feature_extractor = feature.Feature(SAMPLING_FREQUENCY, FFTL, SHIFT)
for ii in range(0, len(CHANNEL_INDEX)):
speech = sf.read(NOISY_SPEECH_PATH.replace('{}', str(CHANNEL_INDEX[ii])))[0]
noisy_spectrogram = feature_extractor.get_feature(speech)
noisy_spectrogram = (np.flipud(noisy_spectrogram))
noisy_spectrogram = feature_extractor.apply_cmvn(noisy_spectrogram)
features = data_shaper.convert_for_predict(noisy_spectrogram)
features = np.array(features)
mask_estimator.reset_states()
padding_feature, original_batch_size = data_shaper.get_padding_features(features)
sp_mask, n_mask = mask_estimator.predict(padding_feature, batch_size=MAX_SEQUENCE)
sp_mask = sp_mask[:original_batch_size, :]
n_mask = n_mask[:original_batch_size, :]
if IS_DEBUG_SHOW_PREDICT_MASK == True:
pl.subplot(len(CHANNEL_INDEX), 2, ((ii + 1) * 2) - 1)
pl.imshow(((n_mask).T), aspect='auto')
pl.subplot(len(CHANNEL_INDEX), 2, ((ii + 1) * 2))
pl.imshow(((sp_mask).T), aspect='auto')
if ii == 0:
aa,bb = np.shape(n_mask)
n_median = np.zeros((aa,bb,len(CHANNEL_INDEX)))
sp_median = np.zeros((aa,bb,len(CHANNEL_INDEX)))
n_median[:,:,ii] = n_mask
sp_median[:,:,ii] = sp_mask
dump_speech = np.zeros((len(speech), len(CHANNEL_INDEX)))
dump_speech[:, ii] = speech
else:
n_median[:,:,ii] = n_mask
sp_median[:,:,ii] = sp_mask
dump_speech[:, ii] = speech
if OPERATION == 'median':
n_median_s = np.median(n_median, axis=2)
sp_median_s = np.median(sp_median, axis=2)
else:
n_median_s = np.mean(n_median, axis=2)
sp_median_s = np.mean(sp_median, axis=2)
if IS_DEBUG_SHOW_PREDICT_MASK == True:
pl.figure()
pl.subplot(3,1,1)
pl.imshow((np.log10(noisy_spectrogram[:, TRUNCATE_GRAD // 2:- TRUNCATE_GRAD // 2] ** 2) * 10), aspect='auto')
pl.subplot(3,1,2)
pl.imshow(((n_median_s.T)), aspect = "auto")
pl.title('noise mask')
pl.subplot(3,1,3)
pl.imshow(((sp_median_s.T)), aspect = "auto")
pl.title('speech mask')
pl.show()
#==========================================
# beamforming
#==========================================
# sinple MVDR
cgmm_bf = cgmm.complexGMM_mvdr(SAMPLING_FREQUENCY, FFTL, SHIFT, 10, 10)
tmp_complex_spectrum, R_x, R_n, tt, nn = cgmm_bf.get_spatial_correlation_matrix_from_mask_for_LSTM(dump_speech,
speech_mask=sp_median_s.T,
noise_mask=n_median_s.T,
less_frame=3)
beamformer, steering_vector = cgmm_bf.get_mvdr_beamformer(R_x, R_n)
enhan_speech = cgmm_bf.apply_beamformer(beamformer, tmp_complex_spectrum)
# reference mic selection MVDR
cgmm_bf_snr = cgmm_snr.complexGMM_mvdr(SAMPLING_FREQUENCY, FFTL, SHIFT, 10, 10)
tmp_complex_spectrum, R_x, R_n, tt, nn = cgmm_bf_snr.get_spatial_correlation_matrix_from_mask_for_LSTM(dump_speech,
speech_mask=sp_median_s.T,
noise_mask=n_median_s.T,
less_frame=3)
selected_beamformer = cgmm_bf_snr.get_mvdr_beamformer_by_maxsnr(R_x, R_n)
enhan_speech2 = cgmm_bf_snr.apply_beamformer(selected_beamformer, tmp_complex_spectrum)
enhan_speech = enhan_speech / np.max(np.abs(enhan_speech)) * 0.75
enhan_speech2 = enhan_speech2 / np.max(np.abs(enhan_speech2)) * 0.75
sf.write('./result/enhacement_all_channels.wav', enhan_speech, 16000)
sf.write('./result/enhacement_snr_select.wav', enhan_speech2, 16000)