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# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
import numpy as np
from context import preprocess
test_wav = [
-3,0,1,-1,2,3,-2,2,
1,-2,0,3,-1,8,3,2,
-1,-1,2,7,3,5,6,6,
6,12,5,6,3,3,5,4,
4,6,7,7,7,3,7,2,
8,4,4,2,-4,-1,-1,-4,
2,1,-1,-4,0,-7,-6,-2,
-5,1,-5,-1,-7,-3,-3,-7,
0,-3,3,-5,0,1,-2,-2,
-3,-3,-7,-3,-2,-6,-5,-8,
-2,-8,4,-9,-4,-9,-5,-5,
-3,-9,-3,-9,-1,-7,-4,1,
-3,2,-8,-4,-4,-5,1,-3,
-1,0,-1,-2,-3,-2,-4,-1,
1,-1,3,0,3,2,0,0,
0,-3,1,1,0,8,3,4,
1,5,6,4,7,3,3,0,
3,6,7,6,4,5,9,9,
5,5,8,1,6,9,6,6,
7,1,8,1,5,0,5,5,
0,3,2,7,2,-3,3,0,
3,0,0,0,2,0,-1,-1,
-2,-3,-8,0,1,0,-3,-3,
-3,-2,-3,-3,-4,-6,-2,-8,
-9,-4,-1,-5,-3,-3,-4,-3,
-6,3,0,-1,-2,-9,-4,-2,
2,-1,3,-5,-5,-2,0,-2,
0,-1,-3,1,-2,9,4,5,
2,2,1,0,-6,-2,0,0,
0,-1,4,-4,3,-7,-1,5,
-6,-1,-5,4,3,9,-2,1,
3,0,0,-2,1,2,1,1,
0,3,2,-1,3,-3,7,0,
0,3,2,2,-2,3,-2,2,
-3,4,-1,-1,-5,-1,-3,-2,
1,-1,3,2,4,1,2,-2,
0,2,7,0,8,-3,6,-3,
6,1,2,-3,-1,-1,-1,1,
-2,2,1,2,0,-2,3,-2,
3,-2,1,0,-3,-1,-2,-4,
-6,-5,-8,-1,-4,0,-3,-1,
-1,-1,0,-2,-3,-7,-1,0,
1,5,0,5,1,1,-3,0,
-6,3,-8,4,-8,6,-6,1,
-6,-2,-5,-6,0,-5,4,-1,
4,-2,1,2,1,0,-2,0,
0,2,-2,2,-5,2,0,-2,
1,-2,0,5,1,0,1,5,
0,8,3,2,2,0,5,-2,
3,1,0,1,0,-2,-1,-3,
1,-1,3,0,3,0,-2,-1,
-4,-4,-4,-1,-4,-4,-3,-6,
-3,-7,-3,-1,-2,0,-5,-4,
-7,-3,-2,-2,1,2,2,8,
5,4,2,4,3,5,0,3,
3,6,4,2,2,-2,4,-2,
3,3,2,1,1,4,-5,2,
-3,0,-1,1,-2,2,5,1,
4,2,3,1,-1,1,0,6,
0,-2,-1,1,-1,2,-5,-1,
-5,-1,-6,-3,-3,2,4,0,
-1,-5,3,-4,-1,-3,-4,1,
-4,1,-1,-1,0,-5,-4,-2,
-1,-1,-3,-7,-3,-3,4,4,
]
def test_mel_scale_function_with_htk_true():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
mel = mfcc_inst.mel_scale(16, True)
assert np.isclose(mel, 25.470010570730597)
def test_mel_scale_function_with_htk_false():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
mel = mfcc_inst.mel_scale(16, False)
assert np.isclose(mel, 0.24)
def test_inverse_mel_scale_function_with_htk_true():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
mel = mfcc_inst.inv_mel_scale(16, True)
assert np.isclose(mel, 10.008767240008943)
def test_inverse_mel_scale_function_with_htk_false():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
mel = mfcc_inst.inv_mel_scale(16, False)
assert np.isclose(mel, 1071.170287494467)
def test_create_mel_filter_bank():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
mel_filter_bank = mfcc_inst.create_mel_filter_bank()
assert len(mel_filter_bank) == 128
assert str(mel_filter_bank[0]) == "[0.02837754]"
assert str(mel_filter_bank[1]) == "[0.01438901 0.01398853]"
assert str(mel_filter_bank[2]) == "[0.02877802]"
assert str(mel_filter_bank[3]) == "[0.04236608]"
assert str(mel_filter_bank[4]) == "[0.00040047 0.02797707]"
assert str(mel_filter_bank[5]) == "[0.01478948 0.01358806]"
assert str(mel_filter_bank[50]) == "[0.03298853]"
assert str(mel_filter_bank[100]) == "[0.00260166 0.00588759 0.00914814 0.00798015 0.00476919 0.00158245]"
def test_mfcc_compute():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
audio_data = np.array(test_wav) / (2 ** 15)
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
mfcc_feats = mfcc_inst.mfcc_compute(audio_data)
assert np.isclose((mfcc_feats[0]), -834.9656973095651)
assert np.isclose((mfcc_feats[1]), 21.026915475076322)
assert np.isclose((mfcc_feats[2]), 18.628541708201688)
assert np.isclose((mfcc_feats[3]), 7.341153529494758)
assert np.isclose((mfcc_feats[4]), 18.907974386153214)
assert np.isclose((mfcc_feats[5]), -5.360387487466194)
assert np.isclose((mfcc_feats[6]), 6.523572638527085)
assert np.isclose((mfcc_feats[7]), -11.270643644983316)
assert np.isclose((mfcc_feats[8]), 8.375177203773777)
assert np.isclose((mfcc_feats[9]), 12.06721844362991)
assert np.isclose((mfcc_feats[10]), 8.30815892468875)
assert np.isclose((mfcc_feats[11]), -13.499911910889917)
assert np.isclose((mfcc_feats[12]), -18.176121251436165)
def test_sliding_window_for_small_num_samples():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
mode_input_size = 9
stride = 160
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
audio_data = np.array(test_wav) / (2 ** 15)
full_audio_data = np.tile(audio_data, 9)
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
preprocessor = preprocess.Preprocessor(mfcc_inst, mode_input_size, stride)
input_tensor = preprocessor.extract_features(full_audio_data)
assert np.isclose(input_tensor[0][0], -3.4660944830426454)
assert np.isclose(input_tensor[0][1], 0.3587718932127629)
assert np.isclose(input_tensor[0][2], 0.3480551325669172)
assert np.isclose(input_tensor[0][3], 0.2976191917228921)
assert np.isclose(input_tensor[0][4], 0.3493037340849936)
assert np.isclose(input_tensor[0][5], 0.2408643285767937)
assert np.isclose(input_tensor[0][6], 0.2939659585037282)
assert np.isclose(input_tensor[0][7], 0.2144552669573928)
assert np.isclose(input_tensor[0][8], 0.302239565899944)
assert np.isclose(input_tensor[0][9], 0.3187368787077345)
assert np.isclose(input_tensor[0][10], 0.3019401051295793)
assert np.isclose(input_tensor[0][11], 0.20449412797602678)
assert np.isclose(input_tensor[0][38], -0.18751440767749533)
def test_sliding_window_for_wav_2_letter_sized_input():
samp_freq = 16000
frame_len_ms = 32
frame_len_samples = samp_freq * frame_len_ms * 0.001
num_mfcc_feats = 13
mode_input_size = 296
stride = 160
num_fbank_bins = 128
mel_lo_freq = 0
mil_hi_freq = 8000
use_htk = False
n_FFT = 512
audio_data = np.zeros(47712, dtype=int)
mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats,
frame_len_samples, use_htk, n_FFT)
mfcc_inst = preprocess.MFCC(mfcc_params)
preprocessor = preprocess.Preprocessor(mfcc_inst, mode_input_size, stride)
input_tensor = preprocessor.extract_features(audio_data)
assert len(input_tensor[0]) == 39
assert len(input_tensor) == 296
|
